An essay concerning a new healthcare

By Peter Van Osta, MD

Health and healthcare are of strategic importance to the socioeconomic framework of society, not only on a local (national) scale but also on an international (global) and geopolitical level. Our health and healthcare challenges are also changing on a global (population) scale, requiring a transformation of our approach to human health and healthcare. A resilient, robust and sustainable (RRS) socioeconomic and healthcare system architecture requires a paradigmatic revolution, from an institution-centered system to a more dynamic patient- and process-centered system, meaning a fundamental shift in the first principles of healthcare systems (S. Greene, 2012; N. Mead, 2000; M. Stewart, 2001; R.E. White, 2008; D.J. Hunter, 2008). The public and private health and healthcare system deals with the quality, efficiency, and effectiveness of its architecture, process, and outcome from a person's viewpoint (trimodal shift of reference frame). The network of institutions changes into a network of persons and their interactions. The nodes in the process change from institution to person, and the process itself changes from transactional to relational. The healthcare ecosystem changes from a transactional fee-for-service or diagnostic-related group (DRG) system to a relational outcome-based model (OBM). Our 'modus intelligendi', and 'modus operandi' change as we switch our strategic, tactical, and operational focus from institution (static) to person and process (dynamic). It is a change from the closed and localized institutional world of the past to the (global) open and connected universe of the future, bound together by interconnected analog and digital participants. The transformation is as profound and challenging as the change of reference frame from the geocentric to the heliocentric system. The paradigmatic changes in the way we have to deal with the complexity and dynamics of systems are disruptive to the overall scheme of thought as for the traditional healthcare architecture and framework (E. Morin, 1992, T.M. Brown, 2006).

Healthcare system redesign

Creating a trustworthy, robust, resilient, and sustainable healthcare system requires an integrated (political) approach, which involves analog participants (people, ...) and digital systems, working together towards an integrated hybrid analog-digital ecosystem and healthcare architecture, which is patient-centered (person-centered), process-oriented (integrated health services), ontology-driven and platform-independent, as no Point-Of-Care (PoC) should be an analog or digital island:

1. Patient-centered (person-centered, end) instead of institution-centered (means)
2. Process-oriented (relation-oriented, integrated health services) and dynamic (composable, transparent work-flow), instead of administration oriented (billing, reimbursement) and static (fragmented)
3. Ontology-driven (semantic integration of information, semantic referral, decidable, meaning, semantics, clinical archetypes), instead of paper-driven or low-level data-driven (passive, meaningless, semantic fragmentation)
4. Platform-independent and open standards (maximize safe & secure communication and interoperability), instead of proprietary standards and platform-, process- and data-silos (process fragmentation, vendor lock-in, information blocking)

The interconnected hybrid analog-digital system should allow for 24/7 operations (almost) in real-time, even during a health crisis, and be capable of safely and securely exchanging semantically structured patient and process data internationally. The focus should be on the primary, and active use of semantically structured patient and process data at the Point-Of-Care (PoC) (operational, workfloor).

Personal motivation

This essay is written out of love and admiration for our physicians, nurses, paramedics, other healthcare workers, and our patients. It deals with the challenges and various aspects of creating a transparent and integrated hybrid analog-digital healthcare process and a trustworthy, resilient, robust and sustainable (RRS) system-architecture. The center part is about various aspects of healthcare architecture, processes, and technology. The introduction and conclusion are about the environment in which a healthcare system is conceived, developed, and used (political, public, private, social, economic, ethical, ...). The main challenge is not the technology but the political, ethical, and socioeconomic environment. Therefore, I also try to explore some underlying dogmata and principles of the debate on health and healthcare (I am neither a politician nor a political scientist). I also deal with some philosophical principles concerning health and healthcare policy. Health and healthcare systems cannot be dealt with in isolation, as society's overall architecture and processes impact the production and destruction of health (C. Borrell, 2007; J.P. Mackenbach, 2014). The development of a hybrid analog-digital healthcare ecosystem cannot be dealt with in isolation, as it is part of modern society's (double) digitalization and energy transition. Modern health and healthcare management depend on skilled personnel, software, data, semiconductors, and electricity (e.g., medically vulnerable consumers (MVCs)) (R. Lawson, 2016). Modern health and healthcare planning, development, and maintenance require a comprehensive approach (e.g. πόλλ' οἶδ' ἀλώπηξ, ἀλλ' ἐχῖνος ἓν μέγα (Archilochus, fragment no. 201) and Isaiah Berlin, 1953). Modern health and healthcare strategy requires "the hedgehog's sense of direction and the fox's sensitivity to surroundings" (J. Gaddis, 2017; J. MacDougall, 2018).

This essay is not about writing the usual high-level vision statement, consultancy report, management summary, white-paper, wish-list, etc., but to make sure the new hybrid analog-digital system architecture and process really serves people and digital systems working together at the Point-Of-Care (PoC). Aspirations have to be aligned with capabilities. The proof of the pudding is in the eating, not in a presentation with the recipe. A healthcare system should first and foremost work for the benefit of healthcare workers and patients at the PoC. Healthcare system architecture and process renewal requires a long-term, integrated, multifaceted and step-wise (timeline, milestones, clustered) approach on many levels and aspects of society and healthcare. It requires a strategy, policy, and roadmap. As a result, it is a "tough read" for a "tough challenge", but as Hannah Arendt (1906-1975 CE) once said in an interview with Günter Gaus (1929-2004 CE) (Zur Person, 28 Oct. 1964): "Wissen Sie, wesentlich ist für mich: Ich muß verstehen. Zu diesem Verstehen gehört bei mir auch das Schreiben. Das Schreiben ist Teil in dem Verstehensprozeß." (R. Torkler, 2014, Zur Person (28 Oct. 1964)).

Avoiding normalcy bias or worst-case scenario bias is challenging. I am critical about health and healthcare problems in this essay, but I do not want to offend or hurt anyone. If this should happen, I do apologize. My apologies also for the occasional sadness. We will need all the help we can get to deal with our global health and healthcare challenges. Let this essay be a challenge and a source of inspiration: "L'avenir n'est pas ce qui va arriver mais ce que nous allons en faire." (Henri Bergson (1859-1941 CE)).

Our world is changing

Why do we need this paradigm change in our socioeconomic and healthcare system architecture? The health challenges of the twenty-first century are related to profound changes in supply and demand. A change in global human population trends and dynamics is one reason (population size, population structure, mobility). The other reason is a profound and global change in the interaction with our biotic and abiotic environment (B. Norton, 1992; D. Fidler, 2004; S.H. Ali, 2011). We are witnessing a complex combination of changes in natality, patient and professionals mobility and demographics, aging (cost of dying), growing demand for healthcare, an ever-increasing burden of chronic diseases, global health threats, environmental destruction, socioeconomic non-sustainability, and healthcare access inequalities. These environmental (biotic, abiotic), socioeconomic, and healthcare issues are severely challenging the sustainability of global socioeconomic and health systems. These threats and challenges constitute a polycrisis for which we need to be better equipped to handle them on an international and even global scale. An outdated health and healthcare policy will not help us improve the equilibrium between supply and demand or the resiliency, robustness, and stability of the overall system. We will have to reconceive, redesign and redevelop both public and private healthcare systems
(See also Health in All Policies: Framework for Country Action (WHO) and Health system governance (WHO) and Population dynamics and policies (UNPF) and Health inequities and their causes (WHO) and Health Inequalities (OECD) and CDC Health Disparities & Inequalities Report (CDHIR) and Fair Society, Healthy Lives (Marmot review)).

Health - input or burden of disease

Human potential that is lost due to poor health is immense (Global Burden of Disease (GBD)). We will have to reduce the burden of disease and the pressure on our healthcare system caused by non-communicable diseases and infectious diseases (C.J.L. Murray, 2017).

Non-communicable disease (NCD)

Non-communicable diseases (NCDs) have become the leading killer diseases in the Western world. They are a result of lifestyle, socioeconomic and environmental factors, and as such are socially transmitted conditions (L.N. Allen, 2017). According to a publication of the World Economic Forum (WEF) in 2011, non-communicable diseases are putting tremendous demands on social welfare and health systems and cause decreased workplace productivity, prolonged disability, and diminished resources within families. Non-communicable diseases, such as cardiovascular diseases, cancer, chronic respiratory diseases, diabetes, and mental health conditions, have a profound (macroeconomic) impact on our society and economy (S. Chen, 2018). Obesity represents an important public health issue (A. Berghöfer, 2008; F. Müller-Riemenschneider, 2008; D. Withrow, 2011). According to a report on the burden of obesity (OECD, 2019), obesity will curb GDP by an estimated 3.3% on average across the OECD. Obesity is even impacting national security (T.J. Smith, 2012; A. Hruby, 2015; unfit to serve (CDC)). About half of all global cancer deaths are due to risk factors such as smoking and alcohol use (W.S. Beckett, 1993; S.S. Hecht, 2002; K. B. Tran, 2022). Cancer due to lifestyle, and living conditions is an important public health and socioeconomic problem (D.C. Whiteman, 2016). Environmental and socioeconomic factors such as housing, working conditions, poverty, crime, and environmental racism impact well-being and mental health (G.W. Evans, 2002; G.W. Evans, 2010; J. Rocha, 2019). A wide range of personal, social, economic, and environmental factors have an impact on population health (DoH, Determinants of Health, EDoH, Environmental Determinants of Health, SDoH, Social Determinants of Health, etc. ...) (P. Braveman, 2014). Due to a sedentary lifestyle, consumption of industrial products (tobacco, alcohol, calorie rich and nutrient poor HFSS food, etc.), combined with population aging, there is an increase in chronic and lifestyle diseases (Physical Activity Transition, B. Majnoni D'Intignano, 1995; P.T. Katzmarzyk, 2009). A position paper argued that NCD death rates could be reduced by 2% per year by implementing five priority actions: tobacco control, salt reduction, improved diets and physical activity, reduction in hazardous alcohol intake, and essential drugs and technologies (R. Beaglehole, 2011; R. Bonita, 2013; R. Beaglehole, 2015)
(See also food desert and food swamp).

The global political challenges for coordinating population health sustainability, health promotion, disease prevention, care, and cure are increasing. Non-communicable diseases require a more complex intra-, extra- and trans-mural organization of population health and healthcare. However, society and healthcare are ill-equipped to cope with complex multidisciplinary processes, causing a lack of horizontal and vertical integration of process information, monitoring, control, and execution (operational, tactical, and strategic). Dealing with chronic and lifestyle diseases requires political, social, economic, and public health measures to prevent people from developing chronic and lifestyle diseases in the first place
(See also Global Diseases and Threats (CDC, USA) and Health and Economic Costs of Chronic Diseases (CDC, USA) and Institute for Health Metrics and Evaluation (IHME) and Global Burden of Disease (GBD, IHME) and The disease burden by cause and Causes of death globally: What do people die from?).

Infectious diseases

Infectious diseases are a global problem and a challenge to deal with (C.M. Michaud, 2009; Z.A. Bhutta, 2014; A. Cassini, 2018). Globalization (trade and travel), urbanization, and climate change are causing changes in infectious disease transmission patterns (epidemics, pandemics) (D. Fidler, 2004; S.H. Ali, 2011). International trade and travel is an important factor in spreading diseases, disease vectors, and vector-borne diseases to different parts of the world (A.J. Tatem, 2006; S. Knobler, 2006; A.J. Tatem, 2012; M. Harrison, 2013; A. Findlater, 2018; P. Antràs, 2020). In large parts of the world, infectious diseases remain an important challenge (T. Philipson, 1999; R. Beaglehole, 2011). Neglected tropical diseases (NTDs) cause devastating health, social and economic consequences to a large number of people (P.J. Hotez, 2007; N. Feasey, 2010). Emerging infectious diseases and high threat pathogens (HTPs) or high-consequence pathogens are a growing cause of concern (D.M. Morens, 2004; K.E. Jones, 2008; S.S. Morse, 2012; L. Garrett, 2019). We are confronted with emerging and re-emerging diseases on a global scale (B.J. Culliton, 1990; R.M. Krause, 1992; J. Lederberg, 1996; M.A. Winker, 1996; R.W. Pinner, 1996; G.L. Armstrong, 1999). Vaccine hesitancy results in disease outbreaks and deaths from vaccine-preventable diseases (E. Dubé, 2013; N.E. MacDonald, 2015). Outbreaks of infectious diseases can have an impact on national security (C. Beadle, 1993; T. Burki , 2010; M.R. Kasper, 2020). Dealing with infectious diseases requires political, social, economic, and public health measures to prevent people from being infected in the first place.

How we deal with antibiotics and bacteria operates like a revolving door. We push out the susceptible bacteria, and the (evolved) resistant ones return through the revolving door (J.A. Heinemann, 2000; I. Yelin, 2018). The same goes for viral, fungal, and protozoan infections (D.P. Kontoyiannis, 2002; D.E. Goldberg, 2012; D. Hughes, 2015; K.K. Irwin, 2016). Antimicrobial resistance (AMR), antibiotic resistance (ABR), and multidrug-resistant organisms (MDRO) are a growing problem for modern healthcare (A.P. Magiorakos, 2012). An estimated 4.95 million (3.62-6.57) deaths were associated with antimicrobial resistance (AMR) in 2019, including 1.27 million deaths (95% UI 0.911-1.71) attributable to bacterial AMR (C.J.L. Murray, 2022). Methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), macrolide-resistant Streptococcus species, and multidrug-resistant tuberculosis (MDR-TB) are some examples of drug resistance. According to the European Antimicrobial Resistance Surveillance Network (EARS-Net), from 2015 to 2019, the most commonly reported antimicrobial resistant bacterial species was E. coli (44.2%), followed by S. aureus (20.6%), K. pneumoniae (11.3%), E. faecalis (6.8%), P. aeruginosa (5.6%), S. pneumoniae (5.3%), E. faecium (4.5%) and Acinetobacter species (1.7%).

The 4.95 million deaths, due to antimicrobial resistance (AMR), mean 3028 Titanics with 1635 passengers each. This is the equivalent of 8 Titanics going down each day (365 days in a year). The 4.95 million deaths mean 8903 Airbus A380 airliners, with 525 passengers, four flight crew, and 27 cabin staff. This is the equivalent of 24 Airbus A380 airliners falling from the sky each day. According to the Centers for Disease Control and Prevention (USA), each year in the United States at least 2.8 million antibiotic-resistant infections occur, and more than 35,000 people die as a result (Antibiotic resistance threats in the united states (CDC, 2019, USA)). In the European Union (EU), 33,000 people die each year due to infections caused by resistant bacteria (Antimicrobial drug resistance (EU)). In general, we cannot deal mentally with the risk and threat of antimicrobial resistance. As comrade Joseph Stalin (1878-1953 CE) once said: "Смерть одного человека - трагедия, смерть миллионов - статистика" (L. Lyons, 1947)
(See also About Global Health Security (CDC, USA) and Infection prevention and control (WHO) and Model List of Essential Medicines (WHO) and Global Research on Antimicrobial Resistance (GRAM) and Antibiotic Resistance The Global Threat (CDC, USA)).

Non-communicable (chronic) diseases and infectious diseases

Economic and cultural globalization (life style), homogenization, and climate change are causing a synergistic neo-Colombian exchange (amalgamation) of non-communicable (chronic) diseases and infectious diseases. Non-communicable (chronic) diseases and infectious diseases interact with each other (P.W. Setel, 2004; T. Oni, 2015). The syndemic of non-communicable (chronic) diseases and infectious diseases during an epidemic put people at a higher risk for complications (Z. Zhongming, 2020). Infectious and noninfectious diseases combine to produce a pernicious cocktail. While non-communicable diseases have a linear growth pattern (creeping normality), infectious diseases can grow polynomial or exponential (low frequency, high impact). In both cases, in the end, society and healthcare systems cannot deal with the growing disease burden, regardless of a linear, polynomial or exponential pattern. The growing burden of disease poses an existential threat to human society. At a certain moment, the system reaches a tipping point (overshoot and collapse)
(See also Global Burden of Disease (IHME) and Burden of disease (OWD) and Report of the Commission on Macroeconomics and health. (WHO, 2002) and Chronic diseases and development and The global economic burden of non-communicable diseases (WEF, 2011) and Non-communicable Diseases (OECD) and The Heavy Burden of Obesity (OECD, 2019) and The obesity crisis (McKinsey, 2015) and Addressing obesity and high blood pressure could protect millions against future pandemics (McKinsey, 2020) and Division of High Consequence Pathogens and Pathology (DHCPP, CDC, USA) and Division of Preparedness and Emerging Infections (DPEI, CDC, USA) and Division of Vector-Borne Diseases (DVBD, CDC, USA) and Re-emerging diseases: gone today, here tomorrow?).

Healthcare - output or healthcare performance

Besides reducing the pressure on our healthcare system (input), we have to improve the efficiency and effectiveness (performance, output) of the entire healthcare ecosystem to deal with the disease pressure on our society and healthcare system. The way we deal with health and healthcare data at the Point-Of-Care (PoC) has to improve dramatically (e.g. Data at the Point of Care (DPC)). Due to analog and digital process and data deficiencies, the healthcare system capacity and capability (absorptive, adaptive, transformative) are limited. Structural deficiencies and process fragmentation put limitations on the healthcare system's capacity and capability (outcome). According to the OECD, improving the health care system's efficiency, public spending savings would be large, approaching 2% of GDP on average in the OECD (OECD, 2010). A significant share of health spending in OECD countries is, at best ineffective and at worst, wasteful (OECD, 2017). The OECD identified 3 major categories of wasteful spending in health systems: wasteful clinical care, operational waste, and governance-related waste (OECD, 2017). According to the OECD (2017), adverse events in 1/10 hospitalization add between 13 and 17% to hospital costs, and up to 70% could be avoided. The loss to fraud and error averages 6% of payments (OECD, 2017). In low- and middle-income countries, diagnostic accuracy can be as low as 34% (WHO, 2017 and WHO, 2018). In the USA, approximately 25% of health care spending may be considered waste, caused by the failure of care delivery, failure of care coordination, overtreatment or low-value care, pricing failure, fraud and abuse, and administrative complexity (W. H. Shrank, 2019). In 2013 an analysis of randomized controlled trials (RCT) was published focusing on the harms and benefits of 3,000 medical treatments (Q.W. Smith, 2013). The effectiveness of each treatment was rated based on six criteria: (a) beneficial, (b) likely to be beneficial, (c) trade-off between benefits and harms, (d) unlikely to be beneficial, (e) likely to be ineffective or harmful, and (f) unknown effectiveness. Only about a third of the treatments were shown to be beneficial (11%) or likely to be helpful (23%). Another 7% were rated as trade-offs between benefits and harms, with 6% rated unlikely to be beneficial and another 3% rated likely to be ineffective or harmful. The remaining 50% of medical treatments were regarded as being of unknown effectiveness. There is a need for clinical process and outcome monitoring and improvement (QA, QC, QI) using practical analytical tools for measuring, monitoring, and managing health care delivery processes to deal with the problem of poor quality and/or inefficiency waste in health care (W.I. Taylor, 1960; BC. James, 1989; J.M. Hughes, 1998). First, we should deal with the process and data problem at the PoC before we deal with secondary and tertiary use of fragmented, incomplete, deficient, and defective data as they are now (Garbage In, Garbage Out, GIGO)
(See also To Err Is Human: Building a Safer Health System (2000) and Health Care Systems: Getting More Value for Money (OECD, 2010) and Tackling Wasteful Spending on Health (OECD, 2017) and Healthcare systems: Tackling waste to boost resources (F. Colombo, 2017) and Half of medical treatments of unknown effectiveness (2013) and Hospital-Acquired Condition Reduction Program, (USA)).

We will have to increase the quality of healthcare systems by employing a risk-driven verifiable and validated quality assurance (QA) and quality control (QC) (Quality Management System (QMS)). About 27% of hospitalized Medicare patients experienced harm in October 2018 (Report OEI-06-18-00400, Office of Inspector General, USA, 2022). The WHO estimates show that in high-income countries, as many as 1 in 10 patients is harmed while receiving health care, causing over 46 million patient harms worldwide per year and over 1.4 million deaths (WHO, 2017). These 1.4 million deaths are the equivalent of 2518 Airbus A380 airliners, with 525 passengers, four flight crew, and 27 cabin staff. This is the equivalent of almost 7 Airbus A380 airliners falling from the sky each day. Major contributors to hospital-acquired conditions (HACs) are adverse drug events, catheter-associated urinary tract infections, patient falls, pressure ulcers, surgical site infection (SSI), central line-associated infections, venous thromboembolism, and ventilator-associated pneumonia (VAP). According to the ECDC and on any given day, about 80 000 patients have at least one healthcare-associated infection, i.e., one in 18 patients in a European hospital (Infections in acute care hospitals in Europe, ECDC). In 2017, according to the ECDC, 8.3% (11 787) of the patients who stayed in intensive-care units (ICUs) for more than two days presented with at least one ICU-acquired healthcare-associated infection (HAI) under surveillance (pneumonia, bloodstream infection, or urinary tract infection, ICU healthcare-associated infections, nosocomial infections) (S.J. Dancer, 1999; A. Rampling, 2001; G. Messina, 2013; Y. Longtin, 2014). Complications, surgical mortality, and failure-to-rescue (FTR) will have to be dealt with by implementing patient safety practices (PSP) (J.H. Silber, 1992; K.G. Shojania, 2001; A.A. Ghaferi, 2009; P.G. Shekelle, 2013). External assessment of health care providers, especially hospitals, using peer review, accreditation, statutory inspection, ISO certification, and evaluation (usually internal) against a verified and validated 'healthcare excellence' framework should protect patients and healthcare workers (C. Shaw, 2004). Education, training, and allowing public and private healthcare workers to work according to modern science based principles would improve healthcare. John Snow (1813-1858 CE), Ignaz Semmelweis (1818-1865 CE), Florence Nightingale (1820-1910 CE), and Joseph Lister (1827-1912 CE) should not have lived in vain
(See also Grossman model of health demand and Low quality healthcare is increasing the burden of illness and health costs globally (WHO) and Prevention of hospital-acquired infections: a practical guide (WHO) and Healthcare-associated infections (ECDC) and Crossing the Quality Chasm: A New Health System for the 21st Century and Better Ways to Pay for Health Care (OECD, 2016) and Caring for quality in health (OECD, 2017) and ISQua and Overview of the 100,000 Lives Campaign and Magnet4Europe).

Health and healthcare - working together

To deal with the global health and healthcare challenges we are facing, our public and private health and healthcare system needs a general overhaul and paradigmatic revolution, which alters the health and healthcare delivery system's very nature. Predistribution with regard to health is the idea that we should try to prevent health deterioration to develop in the first place, rather than restoring health with (expensive) healthcare as occurs under redistribution or restitution of health once it has deteriorated due to lifestyle, SDOH, ... . Health and healthcare policies should be combined and integrated as "the whole is greater than the sum of its parts." Our (global) health systems have to deal with and achieve tangible results at the "Point of Existence" (PoE, biotic, abiotic environment) and "Point of Life," (PoL, human lives), while our healthcare systems have to deal with the "Point of Care" (PoC). The production of social, mental, and physical health has to increase, while the anthropogenic, industriogenic, and iatrogenic production of morbidity and mortality has to be reduced (quality and dignity of life). We have to deal with determinants of health, population health improvement, disease prevention, and in-hospital complications, in order to reduce the burden of disease. How do we organize and deliver appropriate care in which the expected clinical benefits of care outweigh the adverse effects to such an extent that the treatment is justified (J. Robertson-Preidler, 2017)? We will have to deal with healthcare system performance, International Patient Safety Goals (IPSG), Potentially Preventable Complications (PPCs) and Never Events (NEs). Besides technical efficiency, we will have to deal with both production efficiency and allocative efficiency, in order to improve our healthcare systems and reduce the burden of morbidity and mortality on society.

The number of people affected and the degree to which their health deteriorates; as a result increases the pressure on healthcare systems and the demand for healthcare resources to keep society going. Resilient, robust, and sustainable (RRS) healthcare systems are systems that have the ability to deal with evolving challenges and to absorb, recover and prepare for future shocks (bimodal operations). The resilience of health and healthcare systems results from their absorptive, adaptive and transformative capacity (quantity) and capability (quality). Absorptive (coping) capacity (stability) is about persistence, and adaptive capacity is about incremental adjustment (flexibility), and transformative change (disruptive change) altogether is about the capacity and capability to address health and healthcare challenges. The resilience of health and healthcare systems defines the capacity and capability to deal with a particular frequency and amplitude of change. Health and healthcare systems cannot deal with high-frequency and high amplitude changes at the same time. If this happens, something has to give in, either the quantity and/or the quality of health and healthcare in a population. So let us take a look at some ideas and principles which may improve health and healthcare, improve performance, increase robustness and resilience, reduce waste, and avoid harm to people (citizens), patients, and healthcare workers in the 21^st century
(See also Putting People at the Centre: The Future of Health (OECD, 2017) and Science of Improvement and A Primer on Project Management for Health Care and Hospital-Acquired Condition Reduction Program (HARCP) and Potentially Preventable Complications (PPCs) and Never Events and WHO Health Policy and National quality policy and strategy: WHO initiative and The Tallinn Charter and Designing the road to better health and well-being in Europe and EU Health Policy and EU Enabling the digital transformation of health and care in the Digital Single Market; empowering citizens and building a healthier society and European Steering Group (ESG) on Sustainable Healthcare and US Health Policy and China Health Policy) and A world at risk (GPMB, WHO, 2019) and Pandemic preparedness and health systems strengthening (World Bank) and Resilient Health Care and How prioritizing health could help rebuild economies (McKinsey)).

• Introduction
• Challenges
• Discussion
• Go with the flow
• The ExR universe
• Unified Process Management
• Unified N-dimensional and Multi-scale Analysis
• Unified Information Flow Management
• Unified Resource Management
• Political implications
• Putting it together
• The way forward
• Bibliography
• Copyright notice and disclaimer

Modern society faces upward pressure in health care spending due to population changes (demographic and disease profiles), income growth, market mechanisms, and technological advances. However, public-sector deficits and public debt burdens will lead to governments to be less capable in the future to finance further increases in the supply of health care services (World Bank, 1993; F. Stein, 2018; B.M. Hunter, 2019). Health spending had reached 8.9% of GDP in 2016 on average in OECD countries, and real per capita health spending grew at an annual growth rate of about 2.5% (OECD average, 2017). In the absence of major policy change, health spending is expected to reach on average 11.8% of GDP in the OECD in 2040 (OECD, 2024). On average across the OECD, health spending is projected to represent 20.6% of government revenues in 2040, an increase of 4.7 percentage points from 2018 (Fiscal Sustainability of Health Systems, Fig. 3.11, OECD, 11 Jan. 2024).

"In per capita terms, health spending in 2017 is estimated to have reached US$ 4069 (adjusted for differences in price levels) on average across the OECD. This is roughly 70% more than OECD countries spend on education for each citizen" (OECD Health Statistics 2018). In the USA, by 2019, the amount spent on health had already increased to 17.7% of Gross Domestic Product (GDP), based on National Health Expenditure (NHE) data from the Centers for Medicare and Medicaid Services. National health spending in the USA is projected to grow at an average annual rate of 5.4% for 2019-28 and to reach US$ 6.2 trillion by 2028. Because national health expenditures in the USA are projected to grow 1.1 percentage points faster than gross domestic product per year on average over 2019–28, the health share of the economy is projected to rise from 17.7% in 2018 to 19.7% in 2028. The rise in health spending is partly due to population dynamics and the increasingly expensive healthcare industry. Market concentration and unusually weak competition in the healthcare system, indicated by the Herfindahl-Hirschman Index, allows providers to set higher prices without losing patients (L.C. Baker, 2001; L.C. Baker, 2014; H.T. Neprash, 2015; B.D. Fulton, 2017; C. Capps, 2017; M. Gaynor, 2018; C. Capps, 2018). Public investment in health and healthcare has steadily declined as part of Structural Adjustment Programs (SAPs) (M. Sparke, 2020). As a result, due to austerity and market rule, the role of the private sector in capital funding for healthcare projects is expected to increase (J. Barlow, 2010; B.M. Hunter, 2019)
(See also Value for money in health spending (OECD report 2010) and Fiscal Sustainability of Health Systems (OECD, 2015) and Health Expenditure (OECD Health Statistics 2018) and National Health Expenditure Data fact sheet (CDC, USA) and How has U.S. spending on healthcare changed over time? and EU Health systems coordination and Health, Health Production and "Flat of the Curve" Medicine).

The growing burden of (chronic) non-communicable diseases (NCDs) is driving a global epidemiological transition, which changes the healthcare industry business model towards a consumable business model and recurring revenue business model. Traditionally, healthcare, like other businesses, concentrated on selling a product or service for an acute illness that develops quickly, is intense or severe and lasts a relatively short period. Like many other industries, the healthcare industry, driven by the steady rise in chronic disease, has shifted from the one-time sales model to the recurring revenue model, which allows it to generate consistent revenue by providing ongoing access to healthcare products or services in exchange for regularly scheduled appointments and payments. Curing chronic diseases is not even considered a sustainable business model (S. Richter, 2018, p. 20; T. Kim, 2018). The recurring revenue model provides steady cash flow and high patient (customer) retention (PLV, patient lifetime value). This new business model resembles the model for personal computer printers that once carried a hefty price tag and are now being given away almost free. As it turned out, the catch was that the money wasn't in the printer but was in the ink cartridges (razor-and-blades business model). The cloud computing and sharing economy business model also aims at a steady profit (recurring revenue business model). The service and sharing economy turn everything into consumer goods used (consumed) by its customers without future productive use. The same goes for many non-communicable diseases (NCDs) because once you have an NCD, you are almost guaranteed to buy (consume) healthcare services and products for a long time. Rather than the traditional business hinging on dealing with acute illness, the healthcare industry increasingly thrives on repeat customers having non-communicable diseases (NCDs). This business model brings long-term repeat business with a consumable component and service which in the long run guarantees a steady and lucrative profit margin. However, the net effect on society is an increasingly strained healthcare budget and resources due to anthropogenic and industriogenic disease generation.

Only improving the productivity of healthcare ignores the anthropogenic or industriogenic root cause of the problem (I. Kickbusch, 2016; L.N. Allen, 2017; M. McKee, 2018). Corporation-induced diseases attributed to consumption of industrial products (tobacco, alcohol, food, cars, guns, etc.) are increasingly straining global population health and healthcare systems (B. Majnoni D'Intignano, 1995; R.I. Jahiel, 2008; A.B. Gilmore, 2011; J. Madureira Lima, 2018; N. Maani, 2020). Charming your population with a western lifestyle (energy-dense diets, reduced physical activity) while at the same time making a profit from their obesity with statins to lower their cholesterol and anti-diabetic medication for their diabetes mellitus type 2 makes a nice profit on the stock market but a loss on the "market of life". A western lifestyle is like walking down the primrose path. We get a positive monetary profit versus a life value loss (pecuniarisation of life, transforming life value into monetary value) (M. Muchie, 2006). New treatments dealing with the consequences of pollution, destructive living conditions, and environmental destruction makes a great profit for the healthcare industry, but the net effect on the viability of society is negative (allocative inefficiency, large-scale downstream negative impact). We trade healthy life years and quality of life for stock market value by simply commodifying the consequences of anthropogenic or industriogenic health destruction (managing for monetary value, pecuniarism). We create unmet needs through destructive activities and only commodify them but do not solve the root cause when the solution is not commodifiable.

When we encounter a commodification deficit, the problem cannot be dealt with appropriately (marketification deficit). In principle, we are more or less capable of dealing with anthropogenic or industriogenic problems once we have created them, but in practice, we cannot roll back destructive activities once they become part of our socioeconomic framework (production, profit, jobs, consumption, ...). Activity generates activity upon activity (do somethingism) but does not allow for restraint from destruction or a reflection upon the fundamental principles leading to ever new destructive endeavors (W.J. Baumol, 1996; S. Desai, 2010). In a monetized and marketized society, only (quantifiable) profitability and consumption is capable of generating political and socioeconomic activity (epistemectomy) (Z. Lan, 1992). The noncommodifiable or uncommodifiable is beyond (political) comprehension and out of reach for socioeconomic system design. Politically and personally, we prefer the commodification or consumerization of adaptation instead of mitigation. In a consumerized and commodified society, we can only prioritize private wealth, consumption, and profit over public health and wellbeing (A.M. Eikenberry, 2004). As a consumerized and commodified society, we are fundamentally incapable of self-reflection or restraint from (profitable) anthropogenic or industriogenic destruction. Our monetized (ontic) physical reality (environment) forces a monetized ontology (set of concept definitions) upon our conceptualization of reality. The biological debt we are building resembles off-balance-sheet (OBS) accounting, where the actual value of our (future) debt to be repaid is set aside as a mere footnote to the core balance sheet of society. Neither US GAAP nor IFRS deal with this kind of fiddling with society's balance sheet. This resembles the Enron scandal or credit default swaps (CDS), but now on a global scale as an off-the-books part of human society. We prefer to turn a blind eye and not fess up to the biological elephant in the room. We combine monetary wealth with biological bankruptcy, as we increasingly accumulate biological debt. The currency in which biological debt is settled is life itself.
(See also Commercial Determinants of Health (CDoH)).

Human activity, including destroying human lives, has been an inherent part of human existence throughout history, regardless of the political or economic model. Blaming political and economic systems closes our eyes to our responsibility as human beings (ostrich effect). Competition between destroying human lives due to human activity and repairing the damage creates a dynamic balance (transactions create value, non-activity does not). Human society is built upon a dynamic equilibrium of destruction and repair. Where there is the possibility of profit or income from repairing damage, there will be the attempt to make a living from destruction or vice versa. The cycle never ends and cannot be stopped; it pops up repeatedly. Destroying human lives with a wide range of human activities and making a living by repairing the damage is a never-ending cat-and-mouse game (hamster wheel economics). The (economic) activity (profit, income) is essential for both sides, however futile. When you look at it from a distance, you see the futility and stupidity of it all, a stark reminder of the absurdity of our actions. There is no glory in preventing or abstaining from anthropogenic or industriogenic destruction, nor glorious profit or consumptive satisfaction. Instead, there is only the stark reality of our actions and their consequences. "Sie können ein Problem niemals auf der Ebene lösen, auf der es erstellt wurde" (A. Einstein).

There are more important global health challenges to be dealt with than being forced to deal with anthropogenic or industriogenic health destruction. We should not continue producing and consuming obesogenic, diabetogenic, and carcinogenic junk food (HFSS food, highly processed food, convenience food) or tobacco products, and miserable living conditions while at the same time blaming the healthcare industry for making a profit out of dealing with the consequences (E.A. Finkelstein, 2012; X. Zhuo, 2014; L.N. Allen, 2017). Industrialized production of human morbidity and mortality diminishes the carrying capacity and resilience of human society on a global scale (cycle of self-destruction, negative PDCA cycle). It is like filling a bucket (that's) full of holes. You may spend a lot of work and earn a lot of money (wages, profit) filling the bucket, but this should be used on more productive and valuable aspects of human life and society. There is always a market for this kind of activity, and a nice profit to be made, but the overall value for society is negative. It all depends on what you include or exclude from the equation. You will make happy shareholders but destroy the lives of all other stakeholders, which are wisely excluded from the profit and loss (P&L) equation (externality). The amount of anthropogenic or industriogenic generated diseases will flow into healthcare as a communicating vessel system. The healthcare system is the mirror image of the disease generating capacity and capability of the system architecture of society. Using the public and private healthcare industry to adapt society to destructive anthropogenic or industriogenic living conditions and lifestyles wastes resources. Dealing only with healthcare productivity (efficiency, effectiveness) while not dealing with the anthropogenic or industriogenic causes (root cause) of non-communicable diseases (NCDs) and infectious diseases is like the tail wagging the dog or closing the stable door after the horse has bolted. The increasing anthropogenic or industriogenic destruction of human health and lives and the cost of dealing with the consequences, is an example of the parable of the broken window (La vitre cassée) or opportunity costs (F. Bastiat, 1850; E.A. Finkelstein, 2010). We keep trying to solve our health problems by coming up with a plethora of strategies that promise to "fix" healthcare, but we keep failing to "fix" human society.
(See also Customer lifetime value (CLV) and How to find the lifetime value of a patient (PLV) and Global Burden of Disease (IHME) and Burden of disease (OWD) and Report of the Commission on Macroeconomics and health. (WHO, 2002) and The global economic burden of non-communicable diseases (WEF, 2011) and Non-communicable Diseases (OECD) and The Heavy Burden of Obesity (OECD, 2019)).

Increasing demand and shrinking populations

What is health?

According to the 1948 WHO definition, health is: "A state of complete physical, mental and social well-being and not merely the absence of disease or infirmity". Health is a fundamental aspect of the quality and quantity of human life, both personal, public and on a population level. Population health deals with the conditions and factors that influence the health of populations over lifetimes (D. Kindig, 2003; D.A. Kindig, 2007). Public health promotes and protects the health of people and the communities where they live, learn, work and play (M.A. Rothstein, 2002). The WHO definition deals with personal health. The (hierarchical) relation between population, public, and personal health is related to a strategic, tactical, and operational view of health. Population health is a strategic asset to the socioeconomic viability and prosperity of a nation (J.I. Boufford, 2002; D. Kindig, 2003). Both the quantity and quality of population health determine the strategic capacity and capability of a population (resilience, robustness, sustainability). Population health can be evenly or uneven distributed, with a differential impact on the capacity (quantitative) and capability (qualitative) of a population to deal with health threats. Population health enables and limits the capacity, and capability of public health, and this, in turn, enables and limits the capacity and capability of personal health. What we destroy at a higher level cannot be compensated for efficiently and effectively at the lower level (quantitative impact relation). The same relation exists between, global, regional, national, and local health (geographic or spatial impact relation). The macro-level contains and puts limitations on which health level can be achieved at the meso- and micro-level
(See also Geopolitics of Public Health: The One and Only WHO).

Increasingly, communities, employers and industries are expecting and demanding strong coordinated government action to tackle the determinants of health (DOH) and well-being, and avoid duplication and fragmentation of actions (Adelaide Statement on Health in All Policies). Healthcare has to deal with people and patients of all ages and many aspects of their lives (social, economic, environment, ...). Health policy has to deal with physical, mental and social well-being, not only curing diseases (e.g. social economics, income, education, occupation, human-animal-plant-ecosystem). Similar to the one-nation policy of Benjamin Disraeli (1804-1881 CE) we could regard a One Health policy as a policy which emphasizes the (natural) bonds of responsibility not only between the social classes, but between mankind and all living beings
(See also Health promotion and disease prevention through population-based interventions (WHO) and Global Health (WHO) and One Health (WHO)).

Health depends on the environment, the social and economic architecture of society, sanitation, nutrition, life style and genetics. Healthcare contributes only about 11% to our health, while about 89% is determined by genetics, behavior, environment and social circumstances. Individual behavior contributes about 36%, environment about 7%, genetics and biology about 22%, and social circumstances about 24%. Together social circumstances and individual behavior determine about 60% of our health
(See also Determinants of Health and Socioeconomic status (SES) and The Relative Contribution of Multiple Determinants to Health, Health Affairs Health Policy Brief, August 21, 2014 and Ten leading causes of death in the United States, 1977, DHHS, Public Health Service (1980) and The world health report 2002 - Reducing Risks, Promoting Healthy Life (WHO)).

The architecture of society consists of interrelated and interdependent systems or spheres such as the ecosphere, the technosphere and the system of the sociosphere which, in turn, is made up of the system of economy, of the system of politics and of the system of culture (W Hofkirchner, 2003). The architecture of society both enables and limits the achievable health potential and health choices an individual can make and also depends on the position within the political, environmental, social and economic architecture of society. Autonomy and heteronomy with regard to health and healthcare depend on the natural and man made (sociopolitical, economic) challenges to human health and the political, economic and social organization of healthcare. Health related challenges and solutions are part of multiple levels and different domains of human life, both at the macro-, meso- and micro-level of the architecture of society and the human condition ("la condition humaine"). Dealing with health requires an integrated approach at the strategic, tactical and operational level of multiple domains of society (environmental, sociopolitical, economic) and human life. Healthcare as such is only part of the solution, dealing with "damage detection" (early detection) and "damage repair" (cure, care, caring) caused by natural and man-made "health damage". Society has to deal with health promotion and disease prevention through population-based interventions (political, environmental, economic, social). Healthcare has to deal with primary care, secondary care (general and regional hospitals) and tertiary care (university and supraregional hospitals). It has to deal with integrating intramural, transmural and extramural healthcare and inpatient and outpatient care
(See also Health in All Policies (WHO, HiAP) and Natural and man-made hazards (JRC, EU)).

Demographics

The size, structure, and distribution of a population, its spatial or temporal changes in response to birth, migration, aging, and death play an important role in population health management and healthcare policy and organization. Health workforce development (supply) as well as changes in patient population (demand) has a profound effect on public and personal healthcare. Public health and individual healthcare have to deal with a changing environment. Besides income growth and technological advances, population aging is one of the challenges to be dealt with. The population pyramids of many countries are changing due to a demographic transition towards "stage four", with low birth rates and low death rates, leading to an aging population. Lifestyle diseases due to low exercise levels (sedentary lifestyle, 'homo sedentarius') in an aging population contribute to the rise of noncommunicable diseases (NCDs) or 'diseases of civilization' in so-called 'developed' countries (W. Kuryłowicz, 1986). In those world-regions population growth is slowing down and increasingly populations are shrinking and aging (depopulation and sub-replacement fertility)
(See also World Population Prospects 2017 (UN) and Health: Key Tables from OECD (OECD, 2014)).

Physicians, nurses, and other healthcare workers do not grow on trees. Countries are facing severe challenges related to their health and care workforce (‎HCWF). Several countries are gradually being depleted of their active workforce, including physicians, nurses and other healthcare workers. Data from the US Department of Labor show that the healthcare sector employs 11% of American workers and still growing. The 'Global Strategy on Human Resources for Health: Workforce 2030' report predicts that shortages can mount up to 9.9 million physicians, nurses and midwives globally by 2030 (WHO Global Strategy on Human Resources for Health: Workforce 2030). The Association of American Medical Colleges (AAMC) predicts a shortage of up to 122,000 physicians by 2032 (AAMS, 2019), leading to an increasing mismatch in supply and demand in US healthcare. The nursing workforce is aging and the supply of nurses is tightening (Claire M. Fagin, 2001). Besides changing demographics as a cause of healthcare worker shortage, nurses are leaving the nursing profession because of burnout and a non-supportive work environment, mainly due to an ever increasing workload (R McAbee, 1991). Long working hours, inadequate professional support, serious staff shortages have a negative impact on the mental health of healthcare workers (U. Peterson, 2008; M.N. Ilhan, 2008; A.M. Mosadeghrad, 2011). Healthcare worker burnout is not a new problem, but the circumstances and causes change through the years (R.E. Marshall, 1980; W. Regelson, 1989; G. Deckard, 1994; R.M. Michaels, 1996). Socialization by means of a computer- and smartphone-screen also does not prepare for the complex hands-on practical and social skills required for healthcare professionals. Clinicians are cross-domain knowledge workers and nursing requires intuition, empathy, touching, and physical and mental agility (human touch workers) (C. Le Clair, 2019).

Medical deserts are growing, regions with inadequate access to one or more kinds of medical services (V. Lucas-Gabrielli, 2018). The imbalance between healthcare demand and the availability of healthcare resources (supply) is causing a snowball effect in healthcare process deterioration. When a process does not achieve the required capacity and capability level (variability, specifications), it fails to deliver the required outcome. Nursing staff shortages are a cause of care process deterioration, and put patients at risk (S. Hugonnet, 2004; S. Hugonnet, 2007). Not only the patient-to-nurse ratio is an essential factor, but also the quality of the working environment (A. Kutney-Lee, 2009). Due to the Baumol effect, people also quit the (public) healthcare sector for better paid jobs in more productive industries (high labor coefficient, stagnation of productivity) (Baumol, 1967; Baumol, 1993). The nature of healthcare does not allow for extensive automation or a significant increase in productivity. Failure to reduce the administrative burden due to inefficient and ineffective process design and implementation is also a cause of care process deterioration (J.H. Gorby, 1953; R. Agarwal, 2010). In an inefficient process, we should deal with the healthcare process itself and not only the quantity of human resources (demographics). The result of these problems is a global scramble for healthcare workers (scramble for talent and skills), which resembles the Scramble for Africa, as it drains poor and developing countries of their healthcare workforce (brain drain, skill drain) (M. Engel, 1987; Y. A. Misau, 2010; C Aluttis, 2014). Attempts to deal with healthcare labor shortages with migrant workers, and systems such as circular migration, do not solve the global need for skilled healthcare workers. It shows the desperation of healthcare organizations to provide healthcare for their patients, but it only shifts the healthcare worker shortage around the world on a global scale. A global problem cannot be solved with local solutions. Adding more workforce in one part of the world while at the same time weakening our defenses in another, already underserved, part of the world will not improve our defense lines against global health threats
(See also Health workforce (WHO) and WHO Global Health Workforce Statistics (WHO) and Core Health Indicators in the WHO European Region 2015. Special focus: Human resources for health (WHO) and Health Workforce (OECD) and physician supply and The Impact of the Aging Population on the Health Workforce in the United States: Summary of Key Findings (2006) and A Closer Look at the Public Health Workforce Shortage).

High resource patients

A small portion of the population is responsible for a very large percentage of total health spending: our elderly and those with serious or chronic illnesses. About 5% of the population accounts for half of all health spending (high resource patients (HRP)). According to Daryl Pritchard "High resource patients outspend the general population of health care users by more than tenfold." (D. Pritchard, 2014; D. Pritchard, 2016). These patients have health factors that include multiple chronic conditions (MCCs), such as respiratory diseases, heart disease, diabetes, cancer, behavioral health issues (e.g. smoking, drinking, physical inactivity), and psycho-social issues (family problems, depression, anxiety, substance abuse, sexual abuse, and violence) (R.M. Benjamin, 2010; M.P. Van Hook, 2003). They present with complex disease states (COPD, CHF, diabetes, behavioral health, etc.) (Walsh D.W., 2016). This leads to higher mortality rates and a poor functional status, sooner than people with fewer chronic conditions (Benjamin R.M., 2010). Complex Care Management (CCM) has to deal with complex medical and psychosocial issues such as environmental hazards, poverty, housing and other socioeconomic factors in order to reduce costly downstream inpatient expenditures for High Utilizer (HU) populations (M. Smeds, 2019). Both pre- and post-admission factors contribute to high-cost hopital admissions (B Rashidi, 2017). Coordination of political and economic measures, social and medical care, is critical for high resource patients (HRP) and high-cost hospital admissions
(See also Multiple Chronic Conditions (CDC) and How do health expenditures vary across the population? (Kaiser Family Foundation) and Medical Expenditure Panel Survey (MEPS, AHRQ, USA) and Responding to the Growing Cost and Prevalence of People With Multiple Chronic Conditions (OECD) and Patient-Centered Care For High-Need, High-Cost Patients (OECD)).

Aging and the end-of-life

Aging and the end-of-life contribute differently to healthcare expenditures (HCE). Aging people aren't all sick, dependent and on support. The "value" of a human being does not equal its economic exploitability, neither its ideological or political "value" (T.W. Schultz, 1961; G.S. Becker, 2012). Ageism or stereotyping and/or discrimination against individuals or groups on the basis of their age is not acceptable as it is immoral (R.N. Butler, 1969; R.T. Higashi, 2012; K.M. Ouchida, 2015). The goal should be "aging with dignity" and to increase life expectancy in good health, both social and personal. Education, earning a decent living, a healthy environment, and a healthy lifestyle allow for active aging and "aging with dignity" on a population level. Increasing longevity may even delay important medical costs, which are associated with the end-of-life (Jacobzone, 2002). With regard to the impact of increased and increasing life expectancy, there are three possible scenarios: compression of morbidity, expansion of morbidity and a dynamic equilibrium (J. F. Fries, 1980; J. Figueras, 2001, p. 40-41). Cross-national evidence for the validity of the compression of morbidity hypothesis, originally proposed by J.F. Fries in 1980, is now generally accepted (V. Mor, 2005). An increased and increasing life expectancy should lead to the compression of morbidity at very old age or more years in good health, and even progressively postponing the age-related increase in expenditures (J. Figueras, 2001, p. 43). However, the rise in noncommunicable diseases due to physical, social and environmental factors are increasingly causing a rise in morbidity rates (measure of sickness) and slowing down mortality rate decline (E.M. Crimmins, 2010; J.F. Fries, 2011).

Besides a decent life and existence, people also deserve a decent and dignified end-of-life, which is not the same as ending your life in an overmedicalized environment (R. Martensen, 2008; W. Glauser, 2011; D. Cook, 2014). Kindness, humaneness, and respect should guide end-of-life care in order to respect human dignity (H.M. Chochinov, 2007; G. Kennedy, 2016). End-of-life care should avoid futile medical care, after careful consideration (L.J. Schneiderman, 1990; R.J. Jox, 2012; A.A. Kon, 2016). The way we deal with the end-of-life also has an impact on healthcare expenditures. End-of-life care can be either comfort-oriented care or life-extending care. Proximity to death, or the cost of dying, has a more important influence on health-care costs than age itself (E. Ginzberg, 1980; M. Seshamani, 2004; A.A. Scitovsky, 2005; F. Breyer, 2010). Healthcare spending skyrockets at the end-of-life and end-of-life care accounts for approximately 30% of national Medicare spending in the USA (G. Riley, 1987; M.A. Davis, 2016). Behavioral economics provides one possible explanation why a decision about end-of-life care remains such a minefield, even when it does not improve the length or quality of people's lives (A. Tversky, 1981; S.D. Halpern, 2013; P. Ring, 2018). Compared to the general population this group is relatively small and the window of time for a significant impact on costs is limited by these patients' life expectancy (M. D. Aldridge, 2015) (root cause analysis and root cause management)
(See also Health spending (OECD) and National Health Expenditure Data (USA) and Healthy life years statistics (EU) and Healthy Life Years (HLY) and Disability-Adjusted Life Year (DALY) and Years of Life Lost (YLL) and Years Lost due to Disability (YLD) and Quality-Adjusted Life Year (QALY) and The True Cost Of End-Of-Life Medical Care and WONCA Special Interest Group: Quaternary Prevention & Overmedicalization and Protection of the human rights and dignity of the terminally ill and the dying).

Price growth and technology versus living conditions

Price growth

Price growth and technological advancements, independent of population aging, contributes to rising healthcare expenditures (HCE) (S. Jayawardana, 2019). The spectrum of health systems ranges from universal systems in countries in which health is a social right and the state is its guarantor versus market systems in which health is a commodity and each person purchases it individually (O. Feo, 2008). The use of the budget as a system of control by means of direct or indirect provision systems (both public trust and surrogate), differ in the impact on the efficiency and quality of health care (B. Abel-Smith, 1992; William C. Hsiao, 2007). Comparing prices between healthcare systems is not easy as they differ in public or private versus out-of-pocket expenses. Health and hospital spending has both a price and volume component. Health expenditure depends on both the prices of goods and services and the volume of care (L. Lorenzoni, 2017). Depending on the preference for private (volume) or public healthcare (cost), the analysis is highly ideological. Some emphasize the volume of care (quantity) and the moral hazard of getting away with overconsumption of healthcare (A. Gawande, 2009; A. Gawande, 2011), while others emphasize the price problem (G.F. Anderson, 2003; U. Reinhardt, 2019). A country's high health care spending may be due to a relatively high volume of healthcare consumption or to the relatively high price a country pays for its healthcare (L. Lorenzoni, 2017). An analysis of the costs and prices of goods and services for all participants of the healthcare system has to provide the information to decide where and how to take action to improve the system. The solution will depend on the context and situation, but in most cases it will be a combination of measures, both individual (patient, provider) and population-based (environment, social, economic, health improvement, ...). Privatization of public healthcare is also not the (easy) solution (M. Segall, 2000; S.E. Gollust, 2006). The solution is not a mere technocratic problem, but a matter of political choice. Public healthcare is funded by taxpayers (patients), private healthcare is funded by shareholders who do it for profit (return on investment). To whom are healthcare providers accountable to and for what? Healthcare policy is also a matter of ethics. It is a matter of political authority and motivation to manage the growth of healthcare costs, both its volume and price (R.G. Evans, 1991)
(See also W. O. Cleverley, 2017 and Health Expenditure (OECD) and What the Health Care Debate Still Gets Wrong (USA) and Price setting and price regulation in health care (OECD) and Updated Study on Corruption in the Healthcare Sector (EU)).

What are some of the elements in the debate of volume and cost of healthcare? The US healthcare system seems to be one of the most expensive (L. Lorenzoni, 2017). In the United States of America (USA), by 2016 the amount spent on health had already increased to 17.2% of GDP, based on National Health Expenditure (NHE) data from the Centers for Medicare and Medicaid Services. In the USA Medicare spending accounts for about a fifth of total spending, private insurers account for about a third. Although the US Affordable Care Act tried to curb the US insurers' profits, these companies only spend 80 to 85 percent of every premium dollar on patient care (U. Reinhardt, 2017). Health spending per privately insured beneficiary also differs by a factor of three across geographic areas in the USA and hospital industry consolidation seems to be another important driver of higher prices (monopoly hospitals) (Z. Cooper, 2018). U.S. hospital pricing is not very transparent, which doesn't allow for a transparent healthcare system (U. Reinhardt, 2006). In the USA, hospital price setting for uninsured and out-of-network patients, etc., is based on the hospital charge description master (CDM) or chargemaster, which is a comprehensive listing of items billable to a hospital patient or a patient's health insurance provider (A. Dobson, 2005; S. Brill, 2013). There is an increasing gap between billed charges and underlying costs in this for-profit system (C. P. Tompkins, 2006; J. Lagasse, 2016). As market values increasingly crowd out non-market norms, healthcare is increasingly being regarded as a for-profit commodity instead of a human right (B. H. Gray, 1986; M. J. Sandel, 2012). Profit and return-on-investment for providers and private shareholders in a marketized, consumerized and desocialized healthcare system, is more important than the return to patients and society (J.A. Murnane, 2008; R. Simmons, 2009; V.M. Valdez, 2009; M. Fotaki, 2013; P. De Vos, 2015). Healthcare increasingly becomes financially transparent for private shareholders, but financially and morally opaque (or morally non-transparent) for its customers (patients) and employees (healthcare workers)
(See also B.L. Cole, 2007; S.H. Woolf, 2013 and U.S.Health Care Coverage and Spending (2017) and For-Profit Enterprise in Health Care and Conflict of interest in the healthcare industry and How an industry shifted from protecting patients to seeking profit and Study: Hospitals charge more than 20 times cost on some procedures to maximize revenue (USA) and Health Care Costs and Medical Technology).

Healthcare and medical technology

The healthcare industry consumes over 10% of gross domestic product (GDP) of the Western world (Global Health Expenditure Database (WHO)).

Medical technologies can save lives and improve health but must be placed within a broader approach to public and private health care. Medical technology is only part of the solution to our global health challenges. Medical technology mainly thrives in the Western world, but is deficient in large parts of the world, with severe consequences on global health challenges. Improvements in medical technology are often thought to be the gatekeeper to healthier, longer life. However, a healthy lifestyle, which does not require expensive high-tech medicine, is enough to enable individuals to enjoy a very long and healthy life (A.J. Vita, 1998; Y. Ben-Shlomo, 2002; D. Kuh, 2003; M. Marmot, 2013; N. Mehta, 2017). Early life events play a powerful role in influencing later susceptibility to certain chronic diseases (C. Power, 1997; P.D. Gluckman, 2008). Social prescribing links patients in primary care with sources of support within the community to help improve their health and well-being (L. Bickerdike, 2017). Green prescriptions enhance well-being and alleviate stress, and may even mitigate income-related health inequalities regarding chronic diseases and life expectancy (A.E. Van den Berg, 2017). There is also a tendency to focus on high tech expensive medical care, instead of small incremental investments in low technology. Productive efficiency in health care declines with more investment as the marginal returns diminish (K. Baicker, 2011). With regard to healthcare policy and technology, we should distinguish productive efficiency and allocative efficiency. Productive efficiency means that health care resources are put to the best use possible and produce as much health as they can, while allocative efficiency means that the right share of resources is being devoted to health care versus other goods in the economy. Productive efficiency would mean that all healthcare providers operate using best-practice technological and managerial processes. Expensive medical equipment deployment does not equal providing high value healthcare, although it may be technically efficient (S. Palmer, 1999). The WHO estimates show that in high-income countries as many as 1 in 10 patients is harmed while receiving health care, causing over 46 million patient harms worldwide per year, and over 1.4 million deaths (WHO, 2017). We won't achieve allocative efficiency without productive efficiency (K. Baicker, 2011). Healthcare is also becoming more capital intensive, primarily due to (high tech) technological developments (U.S. Bureau of Health Planning and Resources Development, 1975, p. 170). The cost of medical technology is increasingly contributing to the spiraling healthcare costs (R.K. Kumar, 2011). As healthcare is becoming more capital intensive, it is increasingly leading to physician proletarianization and (physician) labor disenfranchisement (J.B. McKinlay, 1985; L.K. Altman, 1990; J.L. Scarpaci, 1990). Medical technology can be part of healthcare improvement, but requires careful Health Technology Assessment (HTA) and cost-effectiveness analysis (M.C. Weinstein, 1977)
(See also Fair Society, Healthy Lives (Marmot review) and Health Care Systems: Getting More Value for Money (OECD, 2010) and Tackling Wasteful Spending on Health (OECD, 2017) and Health Care Costs and Medical Technology).

When technology is being developed and deployed in an un-integrated and non-meaningful way, it has a net destructive effect on overall healthcare process performance as random technology adoption contradicts implementation science (evidence-based practice) (R.R. Schoville, 2015). The development and deployment of healthcare technology has to be balanced with political, environmental, economic and social measures to improve living conditions and population health. High-cost and high tech care, in combination with inefficient use of healthcare resources does not equal high-value care (K. Baicker, 2012). Although a modern racecar will win a race against a horse and carriage, it will not achieve its full potential on a dirt road and certainly not without (the right) fuel. A Formula One race car is useless if you need a bus to bring children to school. In addition we have to be aware of the conflicts of interest arising from the medical-industrial complex (A.S. Relman, 1980; K.D. Strang, 2019). Healthcare technology deployment is part of the objectification, standardization, and commodification of healthcare, which has both intended and unintended consequences (E.D. Pellegrino, 1999; S. Timmermans, 2009). We need applied Responsible Research and Innovation (RRI) in order to deal with the challenges we are facing as a global society (we also need basic or blue skies research). Effective and efficient (productive) implementation of technology bridges the gap between science (industry) and practice by helping to ensure that evidence-based programs validated in the "laboratory" produce similar outcomes in the "real world" (D.L. Fixsen, 2015)
(See also Allocative versus productive efficiency and Health Technology Assessments (WHO) and Health Technology Assessments (HTA, EU) and parable of the broken window and Health in All Policies (WHO, HiAP)).

Living conditions and lifestyle

We are in a new era of human-induced or anthropogenic diseases caused by human activities and demographic growth (A.L. Chaber, 2018). Living conditions and lifestyles are increasingly straining public health infrastructure and health services. Modern living conditions and lifestyle (macro cause) are increasingly problematic and are causing ever more trouble for individual people (micro effect) (R.M Van Dam, 2008; E. Kvaavik, 2010). They contribute to the global disease burden and have an impact on morbid death and morbid living or quality-adjusted life years (QALY) and disability-adjusted life years (DALY) (A. Wahlberg, 2015). A combination of population density-independent factors and population density-dependent factors are increasingly outpacing the carrying capacity of our environment. Globalization, climate change, pollution, unhealthy living conditions and lifestyles, both physical and psychological, increasingly strain modern society, social welfare and healthcare systems. Together they are acting as growing patches of quicksand or sinkholes in which population health value (DALY, QALY) and increasing fractions of our Gross Domestic Product (GDP) disappear. We accept this creeping normality, and only tend to commodify the way we deal with the consequences (double revenue but negative overall value model). We are actively changing the odds against us, in a Red Queen's race (increasing biotic threats) and Court jester phenomenon (abiotic threats) by increasing the destructive biotic and abiotic forces which we unleash upon ourselves.

Every attempt to improve the efficiency and effectiveness of healthcare systems and the productivity of healthcare workers, in order to deal with these problems, remains a band-aid solution or "Kurieren am Symptom" and a (frustrating) Sisyphean endeavor for our healthcare workers and healthcare organizations. We should reduce behavioral, metabolic, and environmental risk exposure (modifiable risk factors), in order to reduce their health burden, and healthcare spending. Increasing healthcare expenditure, expensive high tech, and increasing the number of healthcare workers to fill a bottomless (population health) pit is not the way to go (allocative inefficiency). Healthcare is not a panacea to solve man-made health problems, which should be solved by political, environmental, social, and economic population measures (K. Villadsen, 2015). Medicalization of (anthropogenic) health problems instead of dealing with the root cause is not sustainable (S. Whitmee, 2015; K. Queenan, 2017). Healthcare systems are not meant to be the "wastebasket" of a self-destructive socioeconomic system and society. It won't get any better unless we are capable of dealing with the underlying causes, such as man-made physical, economic, social, and environmental factors (root cause analysis and solutions). With the environmental and population health problems we face, improving healthcare is only a small part of the solution. The situation resembles a ship heading full steam towards a group of icebergs, and instead of changing course, we start building better pumps to get rid of the water coming in after we hit the first iceberg. Of course, these high-tech pumps are now powered by solar energy and operated using artificial intelligence. In the end, the icebergs win and the "Titanic" goes down. The rise in obesity rates and lifestyle diseases and the cost of dealing with the consequences, is an example of the parable of the broken window (La vitre cassée) or opportunity costs (F. Bastiat, 1850; E.A. Finkelstein, 2010). The 'broken window' of noncommunicable diseases due to physical, social and environmental factors, results in a reallocation of resources but not an increase in aggregate wealth (D.E. Bloom, 2012; H.A. Whiteford, 2013; S. Chen, 2018). Concerning anthropogenic health destruction, the discussion between public and private healthcare is irrelevant, as the socio-economic root cause is not being dealt with in both cases. A destructive consumption pattern and lifestyle leading to health deterioration, may provide a high customer lifetime value (CLV). A destructive consumption pattern and lifestyle may even increase patient lifetime value (PLV), due to repeat visits for a chronic disease. Patient lifetime value (PLV) as such is part of a value chain or market approach to health care (D.A. Pitta, 2004). CLV and PLV do not reveal the net transfer in life quantity and quality between customer/patient and commodity and healthcare provider. We also have to deal with an increase and social segregation of diseases of affluence and diseases of poverty (D.S. Massey, 1996; E. Mendenhall, 2017; M. Singer, 2017). Health equity and social justice have never been achieved for low-income and racial minorities, and it remains beyond reach as we do not deal with the root cause. We only deal with the symptoms of health disparities, not the root cause of the social determinants of health (SDOH) (I. Kawachi, 1999; D. Coburn, 2000; D. Coburn, 2004; V. Navarro, 2007; WHO, 2008; P. Braveman, 2011; P. Braveman, 2014; M. Marmot, 2014; E.A. Benfer, 2015). Converging political and socio-economic forces are a socio-economic train wreck waiting to happen (M.L. Diede, 2002).

The responsibility for developing a disease or maintaining one's health is a shared responsibility between society and the individual (R.C.H. Brown, 2019). Ideological bias and political myopia do not solve the problem of public and private health and healthcare. Concerning health and healthcare policy, we merely tend to commodify and monetize the consequences of our lifestyle(s) and mainly adhere to "Every man for himself and the Devil take the hindmost." Our healthcare policy may provide us with a clear conscience but does not deal with the root cause of the problem and only confirms and sustains the socioeconomic mechanisms of inequity and unequal socioeconomic status (SES) (M. Marmot, 2013, p. 1-74). You cannot have a healthy population in a sick society, merely a healthy healthcare industry which, of course, we blame for making a profit of the situation (M. Rossdale, 1965; J. Dixon, 2000; J. Banks, 2006)
(See also Fair Society, Healthy Lives (Marmot review) and Health Equity in England: The Marmot Review 10 Years On).

Living conditions and lifestyle - homo economicus and homo politicus

How do we deal economically and politically with global health and healthcare challenges? For our liberalized global economy it is deemed more important to have an enforcable General Agreement on Tariffs and Trade (GATT), than enforcable healthcare measures in case of a global emergency. We have international courts for a wide range of issues, but not a World Health Court (WHC) for health and healthcare crimes against humanity. Don't worry, Nature itself will take care of it, lobbying or begging for mercy won't do and it operates without court of appeals or prisoners of war (POW). As the ultimate hegemon, Nature does not care about jus bellum justum, Hague Conventions of 1899 and 1907 or Geneva Conventions. Neither a Westphalian Doctrine nor Monroe Doctrine will protect us against the globalized forces and unlimited biotic and abiotic resources of Nature. Not our human laws and mores (nomos, νόμος) will decide the outcome of our conflict with Nature, but the immutable Laws of Nature itself (physis, φύσις), which are not open to negotiation (A.T. Price-Smith, 2008, pp. 12-13).

We are increasingly exchanging commercial profit and consumptive benefits for human health and lives (externalization) in a globalized and intergenerational zero-sum game (A. Hornborg, 2001; A. Hornborg, 2012; J.B. Foster, 2014). The negative impact on human health with its associated healthcare consumption, in relation to the pleasure and satisfaction of both consumption and monetary benefits, is being perceived and regarded as an acceptable cost and number of casualties, as it involves a transfer of life value, cost, income, profit and benefits between socioeconomic strata (age, generations, social and geographic distance). Short-term consumptive satisfaction and monetary benefits (cost, income, profit) are offset against long-term and long-distance population and environmental casualties and destruction (F. Reusswig, 2003). The (promised) light at the end of the tunnel, is just the light of an oncoming train and the road to hell is paved with good intentions. The global community continues taking initiatives, but in the end we fail to reach the goal when global, sustained and multi-domain coordination (governance) is required (E. McIntyre, 2007; T.S. Anish, 2013). Good intentions, by nature, are noncommittal and as long as the fulfillment is far away into the future, with no hard intermediary deliverables, they remain promiseware to be delivered "ad kalendas Graecas" (parturiunt montes, nascetur ridiculus mus, Horatius, Ars poetica 137). On a global scale, the world community did not achieve the United Nations' Health For All (HFA) initiative by 2000 or the Millennium Development Goal (MDG) sanitation target (i.e., to halve the proportion of people without sustainable access to basic sanitation by 2015). Now, the United Nations' Sustainable Development Goal (SDG) is for everyone to have "adequate and equitable" sanitation by 2030.

There is no such thing as a (shared) global health strategy or global health policy, only national politics enacted on a global scale (C. McInnes, 2006; S.E. Davies, 2008; E.M. Speakman, 2017). When facing complex political, socieconomic and global health challenges, most of the time the political process succumbs to the politician's syllogism and mere "do-somethingism." The challenges we are facing, require tangible solutions, not only 'acclamatio' ("Qui non est hodie, cras minus aptus erit"). An "argumentum ad captandum" won't do either. We have to solve macro-problems with tangible macro-solutions, not by means of declaratory or mere symbolic politics (initiativitis), but by changing the biophysical and socioeconomic boundary conditions (patterns for living) of modern society surrounding our healthcare systems, in order to reduce the pressure on our healthcare systems and the environment which has to sustain human life and health (S. Baker, 2007; S. Happaerts, 2012). Dealing with population growth, socioeconomic engineering and technological innovation, in order to deal with (self-)destructive consumption patterns will be required (C.J.A. Bradshaw, 2014; C.J.A. Bradshaw, 2015). The individual is the mental and physical "prisoner" of the socio-economic environment in which it has to live, let it be the savanna or a modern megalopolis. Practical global population and fertility control via humane population planning and family planning, in order to deal with destructive living conditions, overconsumption, environmental destruction, and socioeconomic stability. Initiativitis, a tendency to announce swathes of new initiatives with little inherent substance, or "do somethingitis" is not the solution to our problems. The political situation resembles a quadrilemma or a problem requiring a choice among four alternatives, each of which is (or appears) more or less unacceptable or unfavorable relative to the ideological position(s) from which the situation is being analyzed (population health, environmental health, consumerism and profit externalizing spatiotemporal destruction).

How do we manage global health threats? Are we capable of conceptualizing health systems in an efficient and effective global conceptual framework? Do we have a robust and resilient international political, legal, and operational framework capable of dealing with global health threats? What about the principles, structures, and processes of power that shape what is possible in case of a crisis? How do we deal with actions, practices, and policies in the sphere of global health threats? What about strategic, tactical, and operational decision-making capabilities and capacity? What about the requirement for efficient and effective collective action to address global health problems and threats (bandwidth, synchronization, frequency, amplitude)? Having International Health Regulations (IHR, WHO, 2005), while not being capable of operational, efficient and effective implementation does not contribute to global health and survival in case of an emergency in a socioeconomic globalized and increasingly urbanized world (World Health Organization, 2011; W. Kondro, 2011; H.V. Fineberg, 2014). The International Health Regulations originated with the International Sanitary Regulations adapted at the International Sanitary Conference in Paris in 1851. They were a response to the cholera epidemics that hit Europe in 1830 and 1847, and were meant to standardize international quarantine regulations against the spread of cholera, plague, and yellow fever. Their origin in safeguarding international maritime trade relations during a pandemic is still visible in their approach to pandemics. A glaring gap in the IHR, which has not been remedied, is its lack of enforceable sanctions for not implementing the IHR (H.V. Fineberg, 2014). International relations (Western) are still governed by the Peace of Westphalia (1648 CE), which no virus or bacteria every agreed to (S. Patton, 2019). The 'Peace of Westphalia' and the so-called rules-based international order are not even a footnote in the Law Book of Nature.

We are not capable to deal with natural phenomena on a global scale (threats, crises). We think, regulate and act at a 17^th century nation state context and pace in a globalized and socioeconomic integrated 21^st century (trade and travel volume and speed, air travel, internet) (L.O. Gostin, 2005; E. Mack, 2006). We seem to lack the political, cultural, cognitive, and emotional capacity to deal with global natural phenomena. A never-ending cycle of international pacts and treaties obscures the implementation deficit and operational failure of creating an effective and efficient global system capable of dealing with global health threats or, as Cicero (106 BCE-43 BCE) already knew: "epistula enim non erubescit" (Cicero, Epistulae ad Familiares V, 12, 1). We cover up our implementation deficit by simply creating new treaties in a never ending cycle. Mitigation tackles the causes of health threats, whereas adaptation tackles the effects. Reducing global health threats and stabilizing global healthcare systems ("mitigation") fails, which leaves us to adapt to the emerging biotic and abiotic health threats ("adaptation"). In a certain way our approach to global health crises resembles the Charge of the Light Brigade: "C'est magnifique, mais ce n'est pas la guerre. C'est de la folie" (French Marshal Pierre Bosquet)

Living conditions and lifestyle - making sense of the world around us

Why, as human beings, are we not capable of doing whatever we have to deal with our global health and healthcare challenges? Is it something in our nature or nurture? The way we are capable to look at ourselves and the world around us, enables and restricts our ability to deal with the health and healthcare challenges we are facing. The human mind struggles to grasp reality and has a limited conceptual capacity, limiting its capability to see things as they are and to get somewhat reliable access to reality without illusions clouding our view (B.G. Yacobi, 2013). Our ideologies and beliefs are meant to reduce the complexity of reality down to a level at which we can handle reality, not to elevate our understanding of reality to what is required to deal with reality as such. We should not forget that "all models are wrong, but some are useful". Not even our science provides us with a rock-solid everlasting foundation, as Karl Popper (1902-1994 CE) once stated: "The empirical basis of objective science has thus nothing 'absolute' about it. Science does not rest upon rock. The bold structure of its theories rises, as it were, above a swamp. It is like a building erected on piles. The piles are driven down from above into the swamp, but not down to any natural or 'given' base; and when we cease our attempts to drive our piles into a deeper layer, it is not because we have reached firm ground. We simply stop when we are satisfied that they are firm enough to carry the structure, at least for the time being." (K. Popper, 1959, p. 111). While human "laws" are contingent constructions and limited in time and space, Nature's Laws are independent of what we make of them. The word law is even insufficient to describe what natural phenomena are. What we now call gravity operates just the same, no matter what we call it or whether we think we understand. Our understanding is mathematically descriptive, but not reaching the essence of what it is.

At least science, as opposed to ideologies, has developed a method for renewal of its foundational principles (e.g. geocentrism, phlogiston theory, miasma theory, etc.). We deal with the world we live in as behaving according to the limited model or illusion we have created within our conceptual view (fallacy of misplaced concreteness, a map is not the territory). Due to the diminishing validity of the limited and biased model of our interaction with reality, the boundary conditions are changing and the unknown and unknowable beyond the limits of our paradigms and ideologies is encroaching on us. We do not see and cannot act upon what we cannot think, because of a conceptualization deficit caused by paradigmatic and ideologically constrained conceptualization. Or as Wolfgang Stegmüller (1923-1991 CE) once wrote: "Man muss nicht das Wissen beseitigen, um dem Glauben Platz zu machen. Vielmehr muss man bereits etwas glauben, um von Wissen und Wissenschaft reden zu können." ("Evidenzvoraussetzungen") (W. Stegmüller, 1969, p.33). What remains in a confrontation with extra-paradigmatic reality is ad-hoc "case-based reasoning", or a casuistic and factualist approach, in order to adapt to an ever-changing world which is beyond our conceptual reach.

Paradigmatic limitations do not allow for the advancement of our understanding or development of new models that are capable to resolve fundamental health and healthcare problems. Sustainability, in a sense, is an attempt to keep production and consumption at the same level and avoid unpopular and painful self-restraint, which is impossible to achieve within our set of fundamental principles and ideologies. The ideological (semantic) acceptability of "free software" versus "open-source software" is an example. We are prisoners of our humanity, paradigms, preconceptions and (political) illusions, and skillfully ignore disconfirmating and dissonant data and opinions (Z. Kunda, 1990; K. Edwards, 1996; P.E. Tetlock, 1999; C.S. Taber, 2006). Emotional and cognitive limitations are a problem not only of the general public, as Walter Lippmann (1889-1974 CE) once proclaimed in Public Opinion (1922), but for homo sapiens as a species. "For the most part we do not first see, and then define, we define first and then see. In the great blooming, buzzing confusion of the outer world we pick up what our culture has already defined for us, and we tend to perceive that which we have picked out in the form stereotyped for us by our culture" (W. Lippman, 1922, p. 81). We cannot outsmart human cognitive and emotional limitations (M. Allais, 1953; A. Tversky, 1992). The collective-risk social dilemma and the tragedy of the commons are an example of our human (moral) inability to deal with (perceptually) spatio-temporal far away collective risks (G. Hardin, 1968; G. Hardin, 1985; M. Milinski, 2008).

In our attempt to make sense of the world around us, we always start from a set of arbitrary assumptions or first principles. Once we have established these arbitrary foundational principles, the reality around us is perceived through a conceptual and ideological filter, ignoring what is outside the conceptual framework. Our beliefs and first principles have no truth value in themselves; they only decide upon the truth value from what we deduce from our premises and first principles. Quite often "...the theory supersedes the fact. It is the fact that must conform; and it is the theory that we must strive to nurture, develop, and abstract..." (Maier's Law, N. R. F. Maier, 1960). As Albert Einstein once said: "Erst die Theorie entscheidet darüber, was man beobachten kann" (W. Heisenberg, 2022, p.31). A theoretical model determines which parameters can be determined experimentally and described by theory and which cannot. In international politics, we have realism, idealism, and civilizational principles. In national politics, we have left-wing and right-wing politics. For establishing socio-economic systems, we have Marxism and capitalism. Our ontological and epistemological framework acts as a mental straitjacket or keyhole through which we can perceive and understand our world. We do not need conspiracy theories or other nonsense to make a mess of our understanding of the problems of our world. For those who imagine Marxism or any other -ism might do better, forget it. -Isms are the backbone of our ideologies and arguments determining the foundations of our access to reality and the goals we are capable of achieving (worldview), but fail to inform us about the unideological nature of reality which we are unable to grasp or understand (P. Blau, 2017). Ideologies act as a lens through which we view reality, but cloud our view of reality as in the end "il n'y a rien hors de l'idéologie". Ideology involves a mental model specifying and limiting desirable goals and the causes shaping goal attainment (J.M. Strange, 2005). Ideologies influence how we focus on, process, remember, interpret, understand, synthesize, decide about, and act on reality. These mental models impose an interpretive structure for understanding and reacting to events and act as a vehicle for understanding and acting in uncertain situations (M.D. Mumford, 2008). We resemble a sorcerer's apprentice, unable to control or contain the forces we have unleashed. In a society driven by a market economy, consumerism and the profit principle, monetizing (commodifying) and internalizing the cost of destructive aspects of production and consumption patterns into the market mechanism of socio-economic systems (SES) by means of tangible socioeconomic engineering or a Pigovian tax, seems to be the only feasible option (D. Fullerton, 1997; ENV/DEV/509 (UN, 1999); T.L.T. Nguyen, 2016; differential rent, scarcity rent; marginal extraction cost; double-divident hypothesis, relative inelasticity of natural resources) (biophysical environment)
(See also Health in All Policies (WHO, HiAP) and Global Strategy for Health for All by the Year 2000 and EU burden from non-communicable diseases and key risk factors (EU) and Health at a Glance:Europe 2018 (OECD) and Sustainable Development Goals tracker and Lancet countdown 2018 and World Scientists' Warning to Humanity: A Second Notice and The 1.5 Health Report (WHO) Medical Society Consortium on Climate Change and Health and Docs for Climate letter (Belgium) and Obesity Prevention Source and Tackling obesity would boost economic and social well-being (OECD) and Towards Health-Equitable Globalisation:Rights, Regulation and Redistribution (WHO) and parable of the broken window and Brundtland Commission and Report of the World Commission on Environment and Development: Our Common Future (1987) and Initiativitis: a disease of organisations and Behavioural Insights Team and Changing consumption and production patterns in developed and developing countries discussed in commission on sustainable development (UN, 1999).

The way we define our political and socioeconomic reality enables or restricts the discovery of the truth about the world we live in. In the same way, how we know (epistemology) influences what can be known, thus shaping our ontological assumptions about the world we live in. Relying on market theory and mechanisms that reduce every problem to a profitable transaction with a consumer, externalizing the noncommodifiable aspects of our problems, only consolidates and sustains health destroying activities. Nowadays we rely solely on market theory for addressing public issues as there is no longer an ontology (concepts) to deal with the noncommodifiable domain of society as we have perfomed a mental conceptectomy. We live in an epistemological vacuum when dealing with problems which market theory can't deal with. We live in a society within a market, instead of a market within society and are not capable to take social, environmental, and other factors into account (E. Sternberg, 1993; E. Sternberg, 1996). It does not matter if it is a free or highly regulated market, the most fundamental principles of the commodification of all aspects of society and human existence remain the same. Having to spend economic and public health resources on anthropogenic and industriogenic healthcare problems takes away resources from other domains, not the least from natural health threats. Anthropogenic population health destruction has a destructive domino effect on society. We are witnessing a destructive anthropogenic disease process of the global metabolism of our biosphere. We will also have to deal with environmental, social, and economic disparity and as a result the increasing Matthew effect in modern society (Matthew 25:29) (M. Singer, 2009, pp. xiii-xvii; M. Singer, 2017)
(See also Global resource consumption and global energy consumption and operations research and anthropogenic metabolism and social metabolism and industrial metabolism and urban metabolism).

Deficit thinking blames the victim for his or her health problems, instead of examining how educational and socioeconomic systems should be designed and structured to prevent the poor, precariat and lower middle class, with a low socioeconomic status (SES), from succumbing to destructive socioeconomic policies and exploitation (B.G. Link, 1995; N.E. Adler, 2002; V.L. Shavers, 2007). Silencing the enchanting music and singing voices (siren song) of the Sirens of destruction is a political and moral duty. It is not a matter of capitalism against socialism or Marxism, but a matter of basic human decency and common sense. Marxism and capitalism are both limited to narrow materialistic (economic) and internationalist principles. For capitalism it is the market which is the alpha and omega of its dogmata, and for Marxism it is class. Both share a common belief in economic laws that are independent of the human will (e.g. invisible hand of Adam Smith). They also share a belief in (linear) 'progress' and 'economic laws' leading towards 'paradise on earth' (teleology). Both private and public ownership of the means of production lead to environmental problems and destruction of human health. As Hannah Arendt once wrote in Macht und Gewalt: "Die Alternative Kapitalismus-Sozialismus ist keine wirkliche Alternative. Dies sind gleiche Brüder mit ungleichen Kappen." (H. Arendt, 1970, p. 119).

We should not overestimate the wisdom of the poor or the precariat of modern society. As soon as people get out of (monetary) poverty they join the merry-go-round of destructive consumerism of the growing middle class, without looking back to the less fortunate producing cheap food, clothes and gadgets in miserable working conditions (e.g. sweatshops, migrant workers, working poor, wage slavery, gig workers, child labor, blackbirding). The vast majority of consumers have no scruples against the exploitation of vulnerable populations, either at home or at the other end of the world (D. Arnold, 2006; C Zimmerman, 2017). We now have on a global scale what Benjamin Disraeli (1804-1881) once wrote about Great Britain: "Two nations; between whom there is no intercourse and no sympathy; who are as ignorant of each other's habits, thoughts, and feelings, as if they were dwellers in different zones, or inhabitants of different planets; who are formed by a different breeding, are fed by a different food, are ordered by different manners, and are not governed by the same laws ... the rich and the poor." (B. Disraeli, Sybil, or the Two Nations, 1845) (Condition of England question). Lifting people out of monetary poverty without increasing wisdom makes them join into (self-)destructive production and consumption. If the emptiness of our lives can only be filled through consumption, nothing can save us from destruction. Destructive overconsumption is related to social status and human development, where the richest 10 percent account for 60 percent of all private consumption (global carbon inequality, K. Hubacek, 2017). Overconsumption destroys resources (material, energy), ecosystems and human lives (R. Savage, 1993; M. Sargent, 2008). Reduction of overconsumption can be achieved by reducing pro capita overconsumption and/or reducing the number of people capable of overconsumption by means of socieconomic engineering (e.g. reducing materials-intensity, impoverising extreme overconsumers, etc.) (I. Røpke, 1999; P.M. Brown, 2000)
(See also Too Many People, Too Much Consumption and resource depletion and Pigovian tax).

Wealth and wisdom do not coincide and as a species we have our cognitive and emotional limitations (suboptimal decision-making, bounded rationality, anthropocene) (A. Tversky, 1974; A. Tversky, 1981; J.H. Barnes Jr, 1984; K. Arceneaux, 2012; G. Saposnik, 2016). The main problem is our cognitive and emotional myopia, not the paradigms, principles or -isms which we use as an excuse or scapegoat, and which are mere codified beliefs (δόξα), or 'flatus vocis': "Nihil enim aliud est prolatio (vocis) quam aeris plectro linguae percussio" (Roscellinus Compendiensis, 'Sententia Vocum'). When we do not like or understand reality, we do not hesitate to resort to a plethora of logical fallacies (non sequitur) to make our point. As a destructive consumer or producer, the desired personal outcome justifies our argumentation and blame game (The pot calling the kettle black). When our personal pleasures are at stake, we perform a mental principlectomy and, without hesitation, indulge in mere self-gratification. As Alexis de Tocqueville (1805-1859 CE) put it in De la Démocratie en Amérique, "Je veux imaginer sous quels traits nouveaux le despotisme pourrait se produire dans le monde: je vois une foule innombrable d'hommes semblables et égaux qui tournent sans repos sur eux-mêmes pour se procurer de petits et vulgaires plaisirs, dont ils emplissent leur âme." (A. de Tocqueville, 1840, p. 149).

Disrupting profit barriers with new (digital) technologies and business models, while at the same time destroying the social fabric of society and the income, environment and living conditions of people around the world, is not a sustainable model. GDP only measures the commodified aspect of the balance sheet of society, not welfare or the well being of citizens. "It [GDP] measures neither our wit nor our courage, neither our wisdom nor our learning, neither our compassion nor our devotion to our country, it measures everything in short, except that which makes life worthwhile" (Remarks at the University of Kansas, Robert F. Kennedy, 18 March 1968). Non-consumptive activities provide higher overall satisfaction (J.J. Vaske, 2013). The usual sentimentalism, and endless discussions will not solve our health and healthcare problems. Endless ideological discussions between environmentalists and anti-environmentalists or other -ists and -isms, such as Capitalism versus Marxism or Keynesianism versus Friedmanism, won't do either. “For in spite of itself any movement that thinks and acts in terms of an ‘ism becomes so involved in reaction against other ‘isms that it is unwittingly controlled by them. For it then forms its principles by reaction against them instead of by a comprehensive, constructive survey of actual needs, problems, and possibilities.” (John Dewey (1859-1952 CE), Experience & Education). As Albert Einstein (1879-1955 CE) once said: "Zwei Dinge sind unendlich, das Universum und die menschliche Dummheit, aber bei dem Universum bin ich mir noch nicht ganz sicher."
(See also Anthropocene).

Living conditions and lifestyle - noncommunicable diseases

How do we deal with noncommunicable diseases (NCD) and avoidabe deaths due to risk factors causing them? Noncommunicable diseases usually require long-term management by health systems once they become clinically manifest. They are deemed noncommunicable because they do not fulfill Koch's postulates. Most noncommunicable diseases propagate mainly by means of psycho-social, cultural, ethnic, and socioeconomic attributes of populations, society, and local communities (A.H. Mokdad, 2001; M. Ackland, 2003). They have to be dealt with by managing the 'vectors' or what can be regarded the 'Sirens of destruction' (R. Beaglehole, 2011). We will have to deal with the commercial determinants of health (CDOH) or profit-driven destruction of living conditions and health (I. Kickbusch, 2016; M. McKee, 2018; B. Freeman, 2019)
(See also Bradford Hill criteria) and F. Wang, 2018 and Poverty and health (WHO) and Behavioral Risk Factor Surveillance System (BRFSS, CDC) and Dahlgren-Whitehead rainbow and A healthy lifestyle increases life expectancy by up to seven years (MPG, 2017) and Health, Income, & Poverty: Where We Are & What Could Help and social determinants of health (SDOH) and Gini coefficient and Poverty (Council of Europe) and Income poverty statistics (EU) and Tackling obesity would boost economic and social well-being (OECD) and Sanitation & Hygiene (CDC) and The cost of air pollution (OECD) and Pollution (World Bank Group) and How air pollution is destroying our health (WHO) and air pollution and Eating a balanced diet (NHS, UK) and Health Analysis, The Quality Unit, Scottish Government 22 July 2013 and Former Facebook exec says social media is ripping apart society and Anekdote zur Senkung der Arbeitsmoral (Heinrich Böll, 1 May 1963 CE)).

A healthy lifestyle, which costs nothing, is enough to enable individuals to enjoy a very long and healthy life (N. Mehta, 2017). The solution for our healthcare problems is not only spending more on (curative) healthcare and technology, but also to allow people to live a decent and healthy life to prevent chronic illness from developing (avoidable diseases and deaths) (D.J. Hunter, 2008). Education, earning a decent living, a healthy environment, and a healthy lifestyle is at least as important as healthcare and technology (G. Dahlgren, 1991, Dahlgren-Whitehead rainbow). Encouraging and enabling a healthy lifestyle remains a significant challenge and requires a tailored approach (S.J. Hardcastle, 2015). Environmental hygiene, clean water, sanitation and waste management are also critical to population health in order to prevent water, sanitation, and hygiene (WASH)-related diseases. An example is the work on the London sewer system by Joseph Bazalgette (1819-1891 CE) in response to the Great Stink (1858 CE). Increasing urbanization and a Western lifestyle go hand in hand with a rise in urban pathologies (H.M. Choldin, 1978; S.L. Kirmeyer, 1978; B.M. Popkin, 1999; M.J. Pongsiri, 2009; R. Labonté, 2011). Noncommunicable diseases due to physical inactivity are also a growing economic and public health problem (J. Kruk, 2014; L. Jaspers, 2015; R. Arena, 2016). The toxic environment of a Western lifestyle (energy-dense diets, reduced physical activity), substantially increases mean body weight and the prevalence of obesity (J.O. Hill, 1998; WSC Poston II, 1999; J.C.K. Wells, 2006). Creating obesogenic environments and satiation manipulation through palatable, energy-dense foods (cheap vegetable oils and fats, carbohydrates) destroys population health (B.M. Popkin, 1994; A. Drewnowski, 1997; B.M. Popkin, 1998; B.M. Popkin, 1998; A.A. Martin, 2014; T.L. Davidson, 2019). According to a report on the burden of obesity, obesity will curb GDP by an estimated 3.3% on average across the OECD (OECD, 2019). Noncommunicable diseases, such as cardiovascular diseases, cancer, chronic respiratory diseases, diabetes, and mental health conditions, have a profound (macroeconomic) impact on our society and economy (L. Chaker, 2015; T. Muka, 2015; S. Chen, 2018). Smoking-related illness in the USA costs more than US$ 300 billion each year, but the USA is also the fourth largest tobacco-producing country in the world, following China, India, and Brazil. In the USA in 2016, healthcare spending attributable to modifiable risk factors had reached US$ 730.4 billion, corresponding to 27% of total healthcare spending (H.J. Bolnick, 2020). The USA's attributable expenditure was mainly due to five risk factors: high body-mass index (BMI), high systolic blood pressure, high fasting plasma glucose, dietary risks, and tobacco smoke. The US healthcare market was US$ 2.2 trillion in the year 2016, which would mean that healthcare spending attributable to modifiable risk factors was 33.2%. In 2016 the US Gross Domestic Product (GDP) stood at US$ 17.95 trillion, so the US$ 730.4 billion were 4.1% of the GDP. The US budget deficit stood at US$ 426 billion, which equaled about 2.4% of GDP
(See also Economic trends in tobacco (CDC, USA) and Overview of the US Economy 2016 and Healthcare industry overview and trends in 2016 and US healthcare market 2016).

Pollution is causing a silent genocide on a global scale (R. Walters, 2010; R. Fuller, 2022). Major forms of anthropogenic pollution include air pollution, light pollution, litter, noise pollution, plastic pollution, soil contamination, radioactive contamination, thermal pollution, visual pollution, and water pollution. Large parts of the population in urban areas breathe air that does not meet the health-based World Health Organization (WHO) Air Quality Guidelines ( F.J. Kelly, 2015). The Air Quality Index (AQI) indicates how polluted the air currently is or how polluted it is forecast to become. The AQI is based on measurement of particulate matter (PM_2.5 and PM₁₀), Ozone (O₃), Nitrogen Dioxide (NO₂), Sulfur Dioxide (SO₂) and Carbon Monoxide (CO) emissions. Public health risks increase as the AQI rises. The global atmosphere is being used as an open sewer, with negative effects on human health, both morbidity, and mortality (L.B. Lave, 1970; D. Desaigues, 2007). Air pollution is a cause of cardio-pulmonary deaths and lung cancers (C.A Pope III, 2002). In 2014, some 7 million premature deaths annually were attributed to air pollution (WHO, 2014). These 7 million deaths are the equivalent of 12,590 Airbus A380 airliners, with 525 passengers, four flight crew and 27 cabin staff. According to the 2015 Global Burden of Disease Study, exposure to outdoor fine particulate matter (PM_2.5) is the fifth leading risk factor for death worldwide, accounting for 4.2 million deaths and 103.1 million disability-adjusted life-years (DALY) in 2015 (A.J. Cohen, 2017, black carbon). Annual health care expenditures associated with pollution are estimated to range from US$630 billion (upper bound) to US$240 billion (lower bound) or approximately three to nine percent of global spending on health care in 2013 (A.S. Preker, 2016). According a study backed by the United Nations the removal of tetraethyllead (TEL) from automotive fuel, had resulted in US$2.4 trillion in annual benefits, 1.2 million fewer premature deaths, higher overall intelligence and 58 million fewer crimes (A. Steiner, 2011). However, tetraethyllead (TEL) is still being widely used in aviation gasoline in piston-engine aircraft. Anthropogenic nitrogen and phosphorus overload of the environment, related to intensive farming accompanying the human population explosion and socio-economic development is increasingly poisoning and destroying the biosphere (P.M. Vitousek, 1997; S.R. Carpenter, 1998; V. Smil, 1999). The resulting ecocide goes together with a silent genocide (M.A. Gray, 1995). Pollution poses one of the greatest public health challenges and it disproportionately affects the poor and the vulnerable population. The poverty rate in OECD countries ranges from about 6% to 27% (OECD, 2020). In Europe, relative poverty increased from 20% in 1980 to 22% in 2017 (T. Blanchet, 2019). Air pollution respects no political or social borders and allows for environmental free-riding behavior (A.A. Fraenkel, 1989; D.M. Konisky, 2010). Commodification and exploitation of the consequences of pollution without dealing with the anthropogenic and industriogenic root cause is not a sustainable solution (allocative inefficiency). As comrade Joseph Stalin (1878-1953 CE) once said: "Смерть одного человека - трагедия, смерть миллионов - статистика" (L. Lyons, 1947)
(See also Air pollution, the 'silent killer' that claims seven million lives a year (UN News) and Air Pollution: Africa's Invisible, Silent Killer and Air Pollution in World: Real-time Air Quality Index Visual Map).

Our energy and resource consumption, accompanying the technological revolution, is reaching stratospheric dimensions. Either we burn fossil fuels and produce carbon dioxide (CO₂) and other greenhouse gasses, or we consume "green energy", and we destroy our global environment by mining lithium, cobalt, copper, and rare-earth elements (e.g. neodymium for neodymium magnets in wind turbine electric generators). The destruction of human health due to mining facilitates noncommunicable diseases (NCD) and infections, promoting epidemics and pandemics of NCDs and infectious diseases. We lose what we gain in the stock market in the "market of life." There is no such thing as a free (energy) lunch. A mobile app and wearable will not compensate for bad living conditions or environmental pollution and the ongoing ecocide and silent genocide. Monitoring and monetizing health data of affluent consumers using social media, mobile apps and wearables, does not solve the problem of poverty, socioeconomic inequality, and social health inequality (SHI) (D. Sakellariou, 2017; K. Latulippe, 2017). 'Empowerment' of middle and upper class consumers by means of fitness apps, wearable devices and other digital gadgets for the self-optimization of 'me, myself and I', will not solve the problem of environmental pollution and destruction or social inequality (wellness syndrome, C. Cederström, 2015; C. Cederström, 2017; C. Cederström, 2018). A smart fitness program for the hyperconsuming 'worried well' of the (upper) middle class won't have an impact on the Social Determinants of Health (SDOH) or Environmental Determinants of Health (EDOH). Creating "responsible consumers" within a framework of an irresponsible and undemocratic socioeconomic architecture, only strengthens the norms that reinforce the global socioeconomic structures upon which a destructive society exploiting human capital and natural resources is built (M. Bagnoli, 2003; M. Jacques, 2004, A. Malpass, 2007; A. Wiese, 2015). This kind of consumerism also does not lead to anything resembling "εὐδαιμονία" (R.A. Easterlin, 1974; A.J. Oswald, 1997; R.E. Lane, 2000). We may continue to increase our healthcare industry production and consumption, but it is like pouring money in a bottomless pit, unless we deal with the root cause of an ever increasing man-made burden of disease on modern society (socioeconomic destruction of human lives and living conditions)
(See also ecological footprint and Netherlands fallacy).

Geospatial determined living conditions determine health and wellbeing (N. Krieger, 2002; N. Krieger, 2003; N. Krieger, 2020). Life expectancy and health correlate with income and local area characteristics (R Chetty, 2016; S. Lago, 2017; G. Erreygers, 2011; Preston curve). In 2018, 55% of the world's population lived in urban areas, a proportion that is expected to increase to 68% by 2050 ( UN 2018 Revision of World Urbanization Prospects). People living in the banlieues, inner-city slums, ghettos, and favelas of modern cities can only dream of healthy living conditions (W.J. Wilson, 2008; Y.F. Thomas, 2016; A. Prasad, 2018). Cramped living quarters in modern day "insulae", do not allow for a safe and healthy life (e.g. Grenfell Tower, Habitation à Loyer Modéré). Our inner cities are urban heat islands (UHI), and become death traps during extreme heat events (EHEs) (L. Filleul, 2006; B. Stone, 2010; K. Laaidi, 2012; K.V. Wong, 2013). Urbanization and social stratification creates a subtle but 'de facto' social segregation or apartheid. Poor planned or unplanned urban housing, transport, and food systems, along with social and lifestyle factors, are drivers in the epidemic of noncommunicable diseases, which are linked to risks and hazards such as air pollution, poor diet, physical inactivity, traffic injury and domestic injury (WHO health risks in cities). Population aging in modern society coincides with a global increase in urban population density (confounding factor). Global geospatial, environmental, educational, social and economic stratification by design and development, does not allow for public and personal health optimization. The poor and the precariat in an industrialized society and market economy cannot afford a healthy life style and diet. Having to live on a cheap diet of processed food, high in added fat, sugar (non-milk extrinsic sugars, NMES) and salt (HFSS), and miserable living and working conditions does not contribute to human health. High-calorie foods and beverages with low nutritional value (HFSS) in combination with low physical activity are a major cause of type 2 diabetes, some cancers and chronic kidney disease (A. Tannenbaum, 1942; A. Tannenbaum, 1942; A. Tannenbaum, 1953; T. Byers, 2002; L.H. Kushi, 2006; L.H. Kushi, 2012; World Cancer Research Fund, 2007; WHO Guideline, 2012). By feeding the poor and the precariat with cheap obesogenic, diabetogenic, and carcinogenic junk food, we transfer their life value and quality of life to the stock market and create shareholder value out of their loss of life value (E. Roos, 1998; M. Johnson, 2019). By denying the poor and the precariat access to healthcare insurance and healthcare, countries keep the profitability of the (private) healthcare industry safe, or when providing universal healthcare, they subsidize unhealthy living conditions and low-quality food
(See also environmental justice and ghetto tax and food desert and food swamp and Water, Sanitation and Hygiene (WASH, UNICEF) and air pollution (WHO)).

Compared to the living and working conditions of the modern precariat and wage slaves, life of a native Indian in the Amazon rainforest or a Masai on the Serengeti, still has its advantages, as long as they can stay away and out of reach of modern society, the money economy and avoid destruction of their existence and culture (e.g. cocacolonization, cultural genocide). The moral hazard of getting away with making the poor and vulnerable bear the consequences of pollution, environmental destruction and damaging living conditions is a global problem. Continuing privatization of the pleasure and benefits of consumption and profits, and socialization (negative externalization) of social welfare and healthcare costs is not sustainable on a global scale
(See also Fair Society, Healthy Lives (Marmot review) and Doughnut economics and ecological footprint and global environmental inequality and Netherlands fallacy and environmental racism and environmental dumping and global waste trade and toxic waste colonialism and NIMBY).

Destructive production and consumption is a global problem to be dealt with, such as the Seveso disaster (1976), Bhopal disaster (1984), San Juanico disaster (1984), Chernobyl disaster (1986), Exxon Valdez oil spill (1989), Deepwater Horizon oil spill (2010), , etc. (A.E. Agwu, 2018; G. Song, 2018; D. Chernov, 2020). Structural problems to be dealt with are the tobacco industry, alcohol consumption, processed food (high in fat, salt and sugar), fossil fuels, black carbon, rare-earth element (REE) mining, microplastics, industrial monocropping, and climate change. It is not a matter of Neo-Luddism, postmodernism, extreme environmentalism, Pleistocene nostalgia or back to nature fantasies. Those who criticize all aspects of modernity should contemplate pre-modern maternal mortality, neonatal mortality, child mortality, water, sanitation, hygiene, life expectancy, and life span. Conspiracy theories and other nonsense aren't helpful either. It is not a matter of ideology, such as liberalism, socialism or Marxism, as this is irrelevant for the problem we are dealing with. Not even civilizations, such as Western, Eastern, Christian, Islamic, or Sinic, etc., matter for the challenges we are dealing with. It is a matter of biological and socioeconomic survival of human societies on a global scale, given the increasing biotic and abiotic stress on human populations. Our conceptual, cognitive, social, and emotional limitations do not allow for developing and implementing a solution to problems on a global scale. Political, environmental, and industrial risk management and prevention on a global scale and scope seem beyond our human capacity and capability. Being unable to proactive mitigation of our health and healthcare risks, we try to adapt our living environment at an ever-increasing cost of human and non-human resources. The maintenance cost of the socioeconomic and healthcare system will become a problem with the rising cost of adaptation of humans and human society. "It's no measure of health to be well adjusted to a profoundly sick society" (M. Vonnegut, 1975, p. 208)
(See also Doughnut economics and ecological footprint).

Living conditions and lifestyle - infectious diseases

A globalized world becomes increasingly vulnerable and provides excellent supplies and logistics for creating global pandemics, while at the same time our society, ecosystems, economy and healthcare systems remain ill-equipped to deal with such a global challenge (J. Pike, 2014). Growing urbanization, increased mobility (mass tourism), global value and food chains, biotic homogenization, and global transport networks provide excellent conditions and logistics for the spread of pathogens (M.J. Pongsiri, 2009; R. Labonté, 2011). Globalization is the compression of time and space, universalization and reduced variability of biological, social and economic relations (M.J. Pongsiri, 2009; A. Linklater, 1999). It does not change the essence of economic and social relations, but amalgamates them on a global scale and allows for a Darwinian emergence of a dominant model of biological, social and economic relations in a Neo-Columbian exchange (loss of socioeconomic diversity). International trade and travel is an important factor in spreading diseases to different parts of the world (M. Harrison, 2013).

The reduction of global socio-economic diversity resembles biodiversity loss. Reduction of socio-economic diversity also resembles reduction of genetic diversity, it makes global society more vulnerable to adverse events due to "increased homogeneity" or "reduced diversity" (e.g. paradigmatic "island" system). At the same time we lack operational integration, robustness and resilience as the integration is shallow and one-sided due to fundamental system fragmentation. Scientific and technological change operates at a certain pace, which cannot (always) keep up with the speed and amplitude of global phenomena. Socio-cultural and economic mechanisms also limit the speed, amplitude and duration of a response in case of a need for social, cultural and behavioral adaptation (inertia, resistance to change, socio-cultural "hysteresis").

A 2011 OECD report made clear that there is not sufficient interoperable, globally shared information available in real-time about pandemic risk inventories, hazards or threatened segments of the built or natural infrastructure. There is a dramatic lack of forward thinking and planning for the creation and distribution of socioeconomic and medical countermeasures-including screening and diagnostic tests, medication, vaccines and surge capacity, which, in part, arises because of the lack of real-time information (H. Rubin, 2011). While the cholera pandemics in the nineteenth century were linked to trade routes and facilitated by merchant shipping, we now provide even "better pandemic logistics" by airplane (AON, 2005; A. Mangili, 2005; A.J. Tatem, 2006; Institute of Medicine (US) Forum on Microbial Threats, 2010). Infectious diseases of poverty (IDoP) disproportionately affect the poorest population in the world (Z.A. Bhutta, 2014). A combination of infectious diseases and preexisting conditions, such as noncommunicable diseases (NCDs), social, political, and structural determinants have a synergistic effect and are a cause of syndemics (M. Singer, 2017; E. Mendenhall, 2017; A.C. Tsai, 2017; S.S. Willen, 2017). Tropical diseases and neglected tropical diseases (NTDs) continue to ravage the poor regions of the world. Emerging infectious diseases (EIDs) are also on the rise and are poorly monitored (K.E. Jones, 2008; E.H. Loh, 2015)
(See also International Health Regulations (WHO, 2005) and Public Health Emergency of International Concern (PHEIC) and Pandemic preparedness (WHO) and National Pandemic Strategy (CDC, USA) and Influenza pandemic preparedness (ECDC) and Pandemics: an insurance point of view (OECD) and Coalition for Epidemic Preparedness Innovation (CEPI)).

How do we deal globally with infectious diseases? How do our ideologies and ideas influence our behavior towards infectious disease? Our globalized lifestyle (travel and trade), accompanying economic liberalization, has an impact on the rising infectious disease burden on society due to the global change in disease distribution, transmission rate and, in some cases, management of diseases due to socioeconomic globalization (J. Sommerfeld, WHO, 2004). Mass production and mass consumption creates a level playing field for any event which rides the homogenized environment. We also deal with the environment we live in as behaving according to the limited model or illusion we have created within our conceptual view (fallacy of misplaced concreteness, a map is not the territory). We are in an era of a neo-Columbian exchange of invasive species and pathogens on a global scale. It resembles a zoonotic "population transfer" (species transfer) or "resettlement" of animals, plants, bacteria and viruses by means of "forced migration". While we make a lot of fuss about a so-called human "population transfer", as consumers and travelers we actively and willingly participate in a zoonotic "species transfer". As a result, there is a profound change going on in the global microbiome of the global biosphere, due to socioeconomic activity and habitat destruction (P. Daszak, 2000; S.S. Morse, 2012; N. Dubilier, 2015). Growing urbanization also affects the epidemiology of emerging infectious diseases, as higher socioeconomic connectivity of cities facilitates spreading of infections (D.R. Phillips, 1993; E. Alirol, 2011; C.J. Neiderud, 2015). Our poorly ventilated buildings and (public) transport systems are full of "respiratory waste", droplets and (bio)aerosols facilitating airborne transmission of (airborne) infectious diseases (H. Burger, 1990; M. Nicas, 2005; L. Morawska, 2006; R.M. Jones, 2015; R. Tellier, 2019; M. Pan, 2019). In addition, our cities, buildings and workplaces are a biological wasteland with plenty of fomites facilitating spreading of infectious diseases (S. Bures, 2000; N. Lee, 2003; S.A. Boone, 2005; A.N.M. Kraay, 2018). As a result, we provide excellent breeding grounds, storage, transportation and logistics for a global distribution of invasive alien species and pathogens (zoonosis, phytosis). Behavioral naivité when we make the first biological contact with a new infectious disease, slows down our capacity and capability to respond. There is no place for irrational millenarianism, declinism or alarmism or any other -ism, if we want to solve our health and healthcare problems with infectious diseases. Conspiracy theories or political radicalization won't do either. Redesign and redevelopment of our socioeconomic system, (urban) environments and lifestyle, inspired by new principles (fundamental assumptions) and concepts (understanding retained in the mind), could help. Continuous verification and validation will be necessary
(See also Landscape epidemiology and urban ecology and industrial ecology and cross-species transmission (CST) and object-oriented ontology).

How do we deal with pandemic risk and pandemics? Pandemics occur at regular intervals about every 30-50 years and since 1700 we had 10-13 pandemics in the world. With an odds ratio (relative probability) of at least 1:49, the probability (risk) for a pandemic in a given year is 2% (1/(1+49)). With an odds ratio of 1:29 it is 3.3% (1/(1+29)). The overall probability of a pandemic is increasing as our global population size has dramatically increased, also due to our growing urbanization (concentration) and because we're traveling farther, faster, and more often. The question is not if it will occur, but when and how a pandemic will ravage the world (Anne-Lise Bagur, 2013). An epidemic refers to the rapid spread of disease to a large number of people in a given population within a short period of time. A pandemic refers to an epidemic of disease that has spread across a large region, for instance multiple continents, or worldwide. They both refer to to an infectious wave, spreading though a population as a high speed temporal phenomenon, but they differ in their spatial component (spatio-temporal propagation, dxy/dt) (W.O. Kermack, 1927; J. Baetens, 2013). The impact of an epidemic and pandemic is related to its infection fatality rate (IFR), case fatality rate (CFR). and infectivity or average number of people infected by each sick person (R₀, basic reproduction number, G.N. Milligan, 2015, p. 310, pandemic severity index). In general, the larger the value of R₀, the harder it is to control an epidemic. The effective reproduction number R_t or R_e takes into account that varying proportions of a population are immune to any given disease at any given time (susceptible population S). The reproduction number (R) depends on how long a person is infectious, the number of interactions, transmission probability, and susceptibility of the other person: R = Duration x Opportunities x Transmission probability x Susceptibility (DOTS) (R.M. May, 1987). Endemicity decides on the continued presence of an infection in a population (W.O. Kermack, 1932; W.O. Kermack, 1933). Compartmental models, inspired by the pathophysiology of infectious diseases, are being used to predict how a disease spreads through the population (e.g. SIR model, etc.). With a high R₀, infectivity, clinical severity (virulence), ICU demand (organ dysfunction) and high IFR and CFR, societies, economies and healthcare systems collapse under the pressure. The most notable example is the collapse of pre-Columbian civilization due to the European invasion of the Americas (Columbian exchange). Nowadays, due to globalization and human-assisted spreading of species and pathogens, invasive species and emerging infectious diseases (EID) are given a free ride on a global scale, which dwarfs the Columbian exchange (B.A. Wilcox, 2005; K.A. Murray, 2013). Antigenic evolution of pathogens by means of antigenic drift and antigenic shift now operates on a global scale, as increasingly a globalized bio- and zoosphere interacts with the speed of flight and the force of greed (A. Lange, 2009; B.D. Greenbaum, 2015; J. D'Souza, 2015). More pandemics will follow (G. Evans, 2013). The basic laws for spreading infectious diseases are also valid for any other "contagion" or communicable/contagious phenomenon, ideology, culture, emotion, imitation, socio-economic, invasive species, etc...

The potential impact of a pandemic influenza outbreak has been calculated and it can be used as a model for the impact of a pandemic on modern society. Infectivity and transmission of disease-causing pathogens can lead to (potentially) exponential phenomena, while mortality is a linear phenomenon. Infectivity is the ability of a pathogen to establish an infection, while transmissibility refers to a pathogen's capacity to pass from one organism to another. Three variables related to a pandemic are crucial for the estimation of its economic effects: the morbidity rate (the percentage of the population infected), the number of work weeks lost, and the mortality rate (the percentage of those infected that die, infection fatality rate (IFR)). The attack rate can be used to estimate the required resources for delivery of medical care as well as production of vaccines and/or anti-viral and anti-bacterial medicines. The foreseeable effects of a pandemic influenza outbreak on the global economy, based on SARS experience, range from 1.42 million deaths (0.022% mortality) and economic losses of USD 330 billion (0.8% of GDP) to 142.2 million deaths (2.21% mortality) and economic losses of USD 4.4 trillion (12.6% of GDP) (W.J. McKibbin, 2006). In 2005 the US Congressional Budget Office (CBO) examined two scenarios of pandemic influenza for the United States. A mild scenario with an attack rate of 20% and a case fatality rate (CFR, the number who die relative to the number infected) of 0.1% and a more severe scenario with an attack rate of 30% and a case fatality rate of 2.5%. The CBO study found a GDP contraction for the United States of 1.5% for the mild scenario and 5% of GDP for the severe scenario (Congressional Budget Office, 2005). The effect on GDP of the severe scenario is comparable to the effect of a typical business-cycle recession in the United States during the period since World War II. The macroeconomic effects of a pandemic in Europe, assumed it to have a morbidity rate of 30% and a mortality rate of 2.5% (L. Jonung, 2006). The number of weeks off work due to the pandemic were taken to be on average 3 weeks. Applying these figures on sickness and mortality rates to the EU-25 (2006), suggested that about 150 million Europeans would become sick for three weeks and 2.5% of those, in other words 0.75% of the total population, would die. The EU-25 (2006) would end up with an estimated GDP loss ranging between 2 and 4%
(See also Coronavirus isn't an outlier, it's part of our interconnected viral age (WEF) and Outbreak Readiness and Business Impact - Protecting Lives and Livelihoods across the Global Economy (WEF) and The macroeconomic effects of a pandemic in Europe. A model-based assessment (EU, 2006) and The Global Risks Report 2020 (WEF)).

Pandemic simulations, such as the Clade X pandemic tabletop exercise of 2018 by the 'Johns Hopkins Center for Health Security' (N. Myers, 2018), Crimson Contagion of 2019 by the US 'Department of Health and Human Services' (HHS), and Event 201 of 2019 by the 'Johns Hopkins Center for Health Security' in partnership with the 'World Economic Forum' (WEF) and the 'Bill and Melinda Gates Foundation', revealed major flaws in preparedness for pandemics, due to a lack of funds, coordination, and resources
(See also Event 201 scenario and Crimson Contagion).

What can we do already in order to prepare for and deal with epidemics caused by infectious or etiological agents? A low frequency, non-linear, and high amplitude event such as an epidemic or pandemic overwhelms human society. It is a matter of coordinating (spatio-temporal) a combination of socioeconomic and healthcare measures depending on the situation (bacterial, viral, prion, basic reproduction number (R₀), herd immunity, severity, mortality). Reducing the frequency and amplitude of epidemics is an important aspect of our preparation (root cause analysis). Efficient and effective reduction of pathogen creating and spreading activities is part of the procedure (risk management and mitigation). Preventing a pandemic can be done in a linear mode, while dealing with an epidemic requires exponential capabilities and capacity (principiis obsta (et respice finem), Ovidius, Remedia Amoris, 91). Linear preparations have to continue at a steady pace in order to have sufficient capacity once an non-linear (exponential, geometric, initial logistic) process challenges our society. Improving resilience and robustness of our society, economy and healthcare systems by means of environmental, socioeconomic and value chain engineering is another aspect of our preparation (environment, living conditions, raw materials, intermediate goods, logistics, industry, services) (avoid both scope myopia and scope creep). Socioeconomic value web or grid engineering, in order to deal with disruptions of the socioeconomic web (upstream and downstream). Find out what does not scale exponentially under epidemic pressure, and either replace it or create a buffer or workaround (risk management and mitigation). Diagnostic and therapeutic capacity planning, ranging from laboratory emergency preparedness planning to healthcare facility planning and preparation. Resilient and robust analog and digital networks, dealing with production resources, production capacity and advanced logistics are needed in order to deal with non-linear, low frequency and high amplitude threats. A connected and integrated socioeconomic system should avoid the creation of a single point of failure (SPF) (right-shoring of production, logistics and consumption). Epidemic prevention and containment strategies can be considered under the broad categories of socioeconomic engineering, (antiviral) medication, vaccines and nonpharmaceutical interventions (case isolation, household quarantine, school or workplace closure, restrictions on travel, track and trace, personal protective equipment) measures (N. M. Ferguson, 2006; T.C. Germann, 2006; M.E. Halloran, 2008). Careful evaluation, planning and execution of non-pharmaceutical public health interventions is required, depending on the pathogen and the socioeconomic and environmental conditions (J.E. Aledort, 2007; H. Markel, 2007; ECDC, 2018). In case of an outbreak, an efficient and effective 'Find, Test, Trace, Isolate and Support' (FTTIS) program is also an important public health tool for dealing with any outbreak of an infectious disease (WHO). A fragmented and operational unconnected public healthcare system is no match for a globalized economy in case of an epidemic (I. Kickbusch, 1999, I. Kickbusch, 2000). The lack of a operational unified healthcare system makes it difficult to respond in case of a surge in medical demand due to a natural or man-made disaster. We have a global pyramid of analysis (surveillance), but a (politically, operational) fragmented chain of command (response) (P. Das, 2001; L. Garrett, 2005; W.E. Parmet, 2006; M.T. Osterholm, 2007; R. Katz, 2010; E. Baekkeskov, 2014; open loop system, open-loop controller). We cannot continue to ignore this operational fragmentation, inefficiency and ineffectiveness
(See also Non-pharmaceutical public health measures for mitigating the risk and impact of epidemic and pandemic influenza (WHO, 2019) and Pandemic preparedness & planning (CDC, USA) and Pandemic preparedness (ECDC, EU)).

Pandemic countermeasures have to act as (dynamic) social, health and economic breakwaters adapted to the severity (dz, morbidity, mortality) and propagation (dxy/dt) of the pandemic (see below). They not only have to include measures with regard to the healthcare system itself, but also require socioeconomic engineering in order to improve the robustness and resilience of society itself. An integrated web of socioeconomic measures should be capable to act as exponential "shock absorbers" (snowball capacity). Socioeconomic "just in case" engineering should avoid a single point of failure and "deresilientiation". Otherwise, 'when the pandemic shit hits the fan', the messy and hectic consequences of our unpreparedness become publicly and painfully visible (e.g. survivalism, socioeconomic collapse). Which leaves us with the words of Count Axel Oxenstierna to his son "An nescis, mi fili, quantilla prudentia mundus regatur?", (1648) and Cicero "Cuiusvis est errare; nullius nisi insipientis, in errore perseverare" (Cicero, Philippicae Orationes 12,5).

Multilevel and modular challenges and solutions

Besides reducing the pressure of non-communicable and communicable diseases on our healthcare systems, improving the performance of our healthcare systems requires a paradigm shift. We should reduce the contribution of healthcare systems to morbidity and mortality. We have to break the performance barrier by means of allometric engineering of the healthcare ecosystem components (system re-design and re-engineering). The traditional layers of business architecture, information architecture, and technology architecture are not sufficient to create a meaningful hybrid analog-digital ecosystem. The creation of a hybrid integrated analog-digital healthcare ecosystem requires a long-term vision, an integrated, multifaceted and step-wise (timeline, milestones, clustered) approach on many levels and aspects of society and healthcare. A modern healthcare system should not be designed for an 'average patient', which does not exist, but should be adaptable (modular) to the varying needs of the individual patients as they evolve through life and within a population. A modern healthcare system has to improve the care of individual patients, multilevel population health management, process and decision support at all levels. It should achieve foundational, structural, semantic, semiotic, operational, and organizational interoperability for meaningful and decidable information processing (A. Aguilar, 2005; S. Garde, 2007; Y. Hongqiao, 2009; H. Yang, 2010; K.H. Hwang, 2010; C. González, 2011; T.N. Arvanitis, 2014). Healthcare systems and their data should be designed in a layered and modular way, reflecting the aggregation level from operational (PoC) cross-functional teams to strategic level. In addition, information systems have to enable consistent multi-scale administrative reporting and operational and financial transactions. Politics, processes and operational point of care activities all have an impact on the way we deliver care to our patients and multiple issues need to be dealt with in a spatiotemporal coordinated way, while keeping the system going during redesign and development (Neurath's boat). Changeability and adaptivity should be built into a modular healthcare process, in order to avoid process disruption during transitions towards an improved process, which involves an increase in efficiency and effectivity, while at the same time reducing costs (capability oriented approach). To achieve the interconnection of healthcare systems we have to implement different aspects (layers) of interaction (data and processes). The ecosytem integration levels are character stream, unstructured data, structured data, meaningless or meaningful (decidable) data for both man and machine (ontology). Due to the complexity of modern society we encounter wicked problems, when trying to deal with the challenges of modern healthcare, which require advanced problem structuring methods (PSMs) and solutions
(See also Clinger-Cohen Act of 1996 (40 U.S.C. 1401(3)) (USA) and Digital Strategy at HHS (USA) and eHealth (EU)).

In healthcare systems, information architecture, flow and processing is not as efficient and effective as it could be (A.X. Garg, 2005; R. Hillestad, 2005; B. Chaudhry, 2006; M.B. Buntin, 2011; A.L. Kellermann, 2013). We are not capable to integrate our analog and digital workforce and resources into one integrated hybrid system, which would allow for extending and expanding our cognitive and operational process capabilites (allometric engineering, Baumol effect). The healthcare sector has a limited understanding of how their data can be put to work in order to improve quality, efficiency and effectiveness of care. Healthcare systems are syntactically and semantically incomplete, a patchwork of proprietary and incompatible standards, are full of vendor locked-in data, and are cumbersome to deal with. Information blocking interferes with access, exchange, or use of electronic health information (EHI) by patients and their healthcare providers. The healthcare industry fails to adopt standardized application programming interfaces (APIs). It is like trying to run the internet without agreeing upon TCP/IP, the World Wide Web without HTML, mobile phones without GSM standard or global positioning without Global Positioning System. We may envy the global industries which were built upon these global standards, but don't seem to understand the comparison with the global mess of our digital healthcare system architecture (lexeme, syntax, semantics, ...). The problem with healthcare data, information and medical record-keeping is nothing new (W.G. Patterson, 1954; C.E. Forkner, 1960; H. Fallon, 1970; B.L. Craig, 1989; B.L. Craig, 1990). Already in 1863 CE, Florence Nightingale in her Notes on Hospitals, wrote: "In attempting to arrive at the truth, I have applied everywhere for information, but in scarcely an instance have I been able to obtain hospital records fit for any purposes of comparison. If they could be obtained, they would enable us to decide many other questions besides the one alluded to. They would show subscribers how their money was being spent, what amount of good was really being done with it, or whether the money was not doing mischief rather than good: they would tell us the exact sanitary state of every hospital and of every ward in it, where to seek for causes of insalubrity and their nature; and, if wisely used, these improved statistics would tell us more of the relative value of particular operations and modes of treatment than we have any means of ascertaining at present" (F. Nightingale, 1863, p. 176; G.O. Barnett, 1989, p. 85). Also in 1993, Howard Leslie Bleich wrote: "The paper medical record is an abomination .. it is a disgrace to the profession that created it. More often than not the chart is thick, tattered, disorganised and illegible. Progress notes, consultant's radiology reports, nurses notes are co-mingled in accession sequence. The charts confuse rather than enlighten. They provide a forbidding challenge to anyone who tries to understand what is happening to the patient." (H.L. Bleich, 1993). The ever increasing use of unexplained abbreviations and acronyms is not helpful either (B. Grange, 2000; P. Das-Purkayastha, 2004; K.E. Walsh, 2008; L. Berlin, 2013). Although physicians and nurses have a legal and ethical obligation to manage medical records appropriately, their quality, completeness and correctness is not guaranteed (H.L. Bleich, 1993; J.T. Nagurney, 2005).

However already in 1952 people complained about the administrative burden of keeping hospital records: "The practice of extensive charting probably did no great harm a decade or so ago. There were plenty of nurses. The physician was not crowded with an office full of patients demanding of his time. Today, the nursing shortage being what it is, every effort should be made to utilize our nurses to the utmost and not take their time with clerical work of no immediate value" (J.H. Gorby, 1953). Part of the problem is caused by the fact that the goal of medical records is mainly for billing purposes rather than for clinical decision-making or communication. As part of the revenue cycle, medical billing and coding translate a patient encounter into the languages healthcare facilities use for claims submission and reimbursement. A patient encounter will not be coded or billed if not documented in the medical record. The pressure to document all kinds of data for medical billing sometimes leads to more than 50% of medical notes being copied and pasted in Electronic Medical Records (J. Steinkamp, 2022). The conflict between the clinical usefulness of high-quality (processable, computable) medical records and the burden of creating them remains unsolved, as the discussion is between clinical quality and billing quantity ((partially) conflicting preferences, Contra principia negantem non est disputandum). Relevant clinical information is hidden in a swamp of information used for billing. Paradigmatic myopia and inertia with regard to medical record-keeping and safe, secure, meaningful and computable health data, limit the possibility to transcend dogmatic and narrow focused non-solutions. We keep looking for Vulcan, within the constraints of an outdated healthcare paradigm.

As the WHO estimates show that even in high-income countries as many as 1 in 10 patients is harmed while receiving health care, causing over 46 million patient harms worldwide per year, and over 1.4 million deaths, we cannot regard current healthcare as a high quality process (WHO, 2017). Although technology has evolved since the days of Florence Nightingale, the fundamental problem of intelligent (balanced) processable clinical documentation has not been solved, let it be implemented on a large and integrated scale. Creating a clinical (data) documentation system, which serves both man and intelligent machine in a unified and hybrid process is still beyond our (mental) reach. As human beings we are limited in our capacity to deal with the level of complexity required by the modern health and healthcare process. We do what is possible, but not what is required. Even today we are not capable to create a hybrid ontology-enabled ecosystem which connects both man and machine, to exchange meaningful information for diagnostics, therapies, medication, patient education and coaching, clinical management support systems (CMSS), clinical decision support systems (CDSS), clinical execution support systems (CESS) and clinical quality support systems (CQSS), etc. (strategic, tactical and operational support systems). Both horizontal and vertical (omnidirectional, omnispatial, omnitemporal) operational, tactical and strategic data and process integration is to be achieved, based upon safe, secure, open, transparent and international data and process standards. The cognitive capacity of the healthcare process has to be both extended and expanded in combination with improving process enactability and monitorability. The overall strategy (governance) should provide coherence and direction to the actions and decisions in health and healthcare strategy, tactics and operations
(See also Health: Systems-Lifestyle-Policies and What is Interoperability? and What is Semantic Interoperability? and Levels of conceptual interoperability (LCIM) and A Paradigm for National Electronic Health Records Implementation (USA, 2009) and Promoting Interoperability (USA) and Electronic Medical Record Adoption Model (EMRAM) and Cognitive limitations).

The socioeconomic and healthcare climate is changing, and evolutionary pressure on public health and healthcare systems is increasing. Demographics, life style changes, urbanization, globalization, climate change, pollution, scientific advances, public demand and new (digital) technologies act as (disruptive) meteorites on a socioeconomic and healthcare ecosystem built for dinosaurs. The growing public health and healthcare challenges require a paradigmatic revolution in the way we deal with health and healthcare and/or will lead to growing financialization of healthcare (Y. Zhang, 2014; J. Seddon, 2017; F. Stein, 2018; B.M. Hunter, 2019). Data and the (unleashed) financial economy fill the (value) gaps left by our political and socioeconomic failure in the real economy. The answer to the challenges of public health and individual healthcare are as much ethical, political, economical, environmental, social, organizational, professional as technological. Spending more money on an outdated system or narrow-mined economic reasoning will not solve society and healthcare problems. Technology alone will not solve our problems. We tend to adapt our healthcare problems to tools, rather than adapting tools to our healthcare problems. Complex Care Management (CCM) is not dealt with merely utilizing a mobile application on a smartphone and narrow-minded instrumentalism (Law of the instrument). More money (year-on-year financial increases) and more technology is not the sufficient (simple) answer to deal with the challenges facing society and healthcare. Health and healthcare is both a public and private problem and a qualitative (effectiveness) and quantitative (efficiency) problem. Resistance to change is built into the foundations (first principles) of the system itself. A redesign (first principles) and development of the foundations of (modern) society and healthcare is required in order to make the system safe, effective, patient-centered, timely, efficient (affordable) and equitable. We will have to work on all levels to create a new internally consistent public health and personal healthcare ecosystem. Healthcare process design and development has to move from a (static) 'well oiled machine' model to a (dynamic) 'complex adaptive system' (CAS) to facilitate the transition (P.E. Plsek, 2001). We will have to move from walls without an integrated system, towards an integrated system without walls. It is not about thinking outside the box, as this confirms the box to be the reference frame, but throwing away the box altogether. The traditional healthcare system has its focus on healthcare institutions and providers, while modern healthcare is moving towards a patient-centered approach and even person-centered care (PCC) (Engel, 1977; Ruiz-Moral, 2016).

The trend toward patient-centeredness, facilitated by technological development, is the healthcare equivalent of consumerization, meaning the reorientation of healthcare to focus on the patient (end user) as an individual consumer of care, in contrast with traditional organization-oriented healthcare. Patient Centered Design (PCD) of a healthcare system is also the healthcare equivalent of User Centered Design (UCD). Consumerization of healthcare fundamentally changes the relationship between organization and patient and the entire healthcare process and architecture. Citizen-centered care deals with the prevention, proactive and personalized services, and healthy lifestyles of our citizens (P Nykänen, 2012). The public and personal healthcare system tends to forget the person (homo sapiens) and socioeconomic and environmental context behind the patient as a healthcare-consumer (homo consumerensis). Person-centered care (PCC) also, deals with reversing the dehumanization of medicine and disempowerment of patients as it takes into account the "life dimension" of the person who becomes a patient (De Maeseneer, 2012, Engel, 1977; Ruiz-Moral, 2016; Smith RC, 2002). Health policies also have to deal with macro-level social, political, and ecological factors (M. Singer, 2017). A patient-centered (consumerized) approach can be dealt with within a (mere) market- and process-driven (utilitarian) context, while a person-centered care (PCC) has to be dealt with within a biosocial or biopsychosocial model (M. Singer, 2017; Borrell-Carrió, 2004).

Patient-centered care or user-centered care (individualized) deals with healthcare improvement utilizing technology and process efficiency and effectiveness, while person-centered care also deals with the non-quantifiable ethical dimensions of caring for the individual patient as a human being (personalized) (D.S. Paulson, 2004). Care refers to meeting the patients's physical and basic needs, whereas caring refers to more of the humanistic, emotional needs. Care (productivity) and caring (humaneness) are both to be taken serious and accounted for. The changes going on in healthcare are part of a profound change in the architecture of society and have to be dealt with within the context of the overall transformation of modern society. The healthcare system that will emerge from the Copernican paradigm shift will be the one that is capable to adapt and adjust to the changing environment of an individualized and personalized healthcare process (R. Snyderman, 2012; K.B. Angstman, 2014). The transition from a transactional fee-for-service episodic care delivery system to a relational and value-based population and personal health care system requires a redesigned and re-aligned healthcare system (K.B. Angstman, 2014). Merely doing more of the same or doing things differently, that's the political and organizational question. Do we have to work harder, or do we have to work smarter? Do we keep running in a hamster wheel or running in circles but making no progress? Do we go on doing things the same way, repeating the same mistakes, guided by a sense that motion but not direction, is the crucial question?

A political laissez-faire attitude, will harm both our citizens, patients and healthcare workers. Without a "grand bargain", and paradigm change, the transition to a sustainable public, and at the same time individualized and personalized healthcare system won't work (e.g., Individualized Health Care Plan (IHP)). The disequilibrium caused by an outdated healthcare policy and ongoing public health and healthcare development will increasingly destabilize society and cause havoc for our citizens, patients, and healthcare workers. Without incorporating the ongoing public health and healthcare paradigm change (public and individualized and personalized healthcare) into the healthcare system itself, we are "squaring the circle", or trying to do the impossible. Significant quality and productivity gains are required to keep the modern public and private healthcare systems capable of keeping up with evolving threats, demand, mismatched budgets, and decreasing human resources. Improving process outcomes and process quality, combined with cost savings and value in efficiency- and effectiveness-based reimbursement models instead of volume-based models will be required (transparency and accountability). Effectivity and efficiency will need to improve in order to keep the public and private healthcare system up and running at a decent level in order to satisfy the need and demands of increasingly strained healthcare workers. Humaneness and human dignity of both our patients and healthcare workers have to be an integral part of the healthcare process. Legal frameworks will need to be rebuilt starting from new first principles instead of patching up the old and outdated legal frameworks.

Continuing with outdated first principles and political phantasies will not solve the problems of health care, as: "A well-known criticism on the Aristotelian Logic is the complaint, that it provides for the consistency of thought with thought, but not for the consistency of thought with things; that it secures right processes upon given or assumed materials, but does not guarantee the materials upon which the processes are conducted." (from the Preface of The First Principles of Knowledge by John Rickaby, S.J., 1901). As Francis Bacon told us in his Novum Organon: "The syllogism consists of propositions, propositions consist of words, words are symbols of notions. Therefore if the notions themselves (which is the root of the matter) are confused and over-hastily abstracted from the facts, there can be no firmness in the superstructure. Our only hope therefore lies in a true induction." (F. Bacon, 1863, Aphorisms concerning the Interpretation of Nature and the Kingdom of Man). Isaac Newton in the General Scholium from the third (1726) edition of his Philosophiæ Naturalis Principia Mathematica argued that: "Rationem vero harum gravitatis proprietatumex phænomenis nondum potui deducere, & hypotheses non fingo. Quicquid enim ex phænomenisnon deducitur, hypothesis vocanda est; & hypotheses seu metaphysicæ, seu physicæ, seu qualitatumoccultarum, seu mechanicæ, in philosophia experimentali locum non habent" (T.V. Carey, 2012). Mathematician Roger Cotes echoed Isaac Newton’s view on hypotheses in his preview of the second edition of the Principia (1713): "Those who assume hypotheses as first principles of their speculations, although they afterwards proceed with the greatest accuracy from those principles, may indeed form an ingenious romance, but a romance it will still be" (D.J. Glass, 2014).

Process redesign and improving operational, tactical, and strategic processes, based upon a new set of first principles following (global) the natural reality, and not merely an "argumentum ab auctoritate" will be required. The problem however, is that the "house of cards" of our health system and healthcare will collapse when the transformation of the foundations of healthcare happens without careful planning, design, and execution in order to keep the system going during the transition. We will have to keep the system going during the transition from a transactional to a relational model. A dynamic and balanced approach to the performance risks versus the insurance risk during the transition will be required. From fee-for-service, over shared savings/losses, bundled payment, and global payment model, the financial responsibility for the healthcare provider increases. The transition from a pay-for-performance model to an alternative payment model (APM), such as shared savings/losses, bundled payment, or global payment model, requires careful planning and execution. An alternative payment model (APM) is a payment approach that gives added incentive payments to provide high-quality and cost-efficient care. APMs can apply to a specific clinical condition, a care episode, or a population. Fee-for-service payment models and bundled payment models (BP, episode-based payment) emphasize different aspects of the overall healthcare process, with regard to the financial risk for the provider. Both BP and FFS degrade when the provider becomes more risk averse. When dealing with a traditional Fee-for-Service (FFS) payment model, a balanced shift from fee-for-quantity to a fee-for-quality will be necessary (e.g. accountable care organizations). A Fee-for-Quantity system has a tendency to externalize the cost of poor quality (negative externality) to society, while a Fee-for-Quality system attempts to internalize the cost of poor quality for the healthcare provider (pay for performance). Healthcare will have to move from one-sided risk models to two-sided financial risk models as part of value-based care taking into account how healthcare services impact patient outcomes and healthcare costs (upside risk versus downside risk arrangements). Patient education and (chronic) disease prevention will require more attention overall to balance prevention with treatment. A shift from intramural to extramural and from curative to preventive care will also be required. Healthcare process optimization requires a new health care paradigm, multi-factorial process redesign, integrated multilevel management, outcome monitoring and quality assurance (QA) and control (QC) in order to improve the overall performance of the entire process (T.M. Davidson, 2001; Kelley & Gravina, 2017; Andel, Davidow, Hollander, & Moreno, 2012)
(See also Health Care Has a Lot to Learn from Consumer-Friendly Industries and Bundled Payment vs. Fee-for-Service: Impact of Payment Scheme on Performance and OM Forum-The Vital Role of Operations Analysis in Improving Healthcare Delivery and How are income and wealth linked to health and longevity? and Physician Payment Models: Review and Update and Low quality healthcare is increasing the burden of illness and health costs globally and The real question is not who's going to pay for tomorrow's care model. It is: who is going to pay for today's care model tomorrow?).

Dealing with process performance and productivity in healthcare

How do we deal with the growing need for reliable performance evaluation of our healthcare industry (S. Swaminathan, 2008; A. Traberg, 2011)? How do we improve the experience of care, the health of populations, and reduce per capita costs (A.J. Barnes, 2014; F.P. Vlaanderen, 2019)? How do we deal with the transition from a pay-for-performance model to a mixture of shared savings/losses, bundled payment, and global payment models (E.F. De Vries, 2021; D. Cattel, 2022)? What about the problem of inappropriate care in the form of under-use, over-use, and misuse of health care services (M.R. Chassin, 1998)? What is appropriate care (J.E. Wennberg, 1987; N.B. Pitts, 1997; C.D. Naylor, 1998; J. Robertson-Preidler, 2017)? Do we mean by appropriate care, care characterized by evidence-based care, clinical expertise, patient-centeredness, resource use, and equity (J. Robertson-Preidler, 2017)? What does it mean to be productive in healthcare? Productivity is the relation between output as compared to input: productivity = output / input or also productivity = effectiveness + efficiency. Productivity is both the relation between output versus input and the combination of effectiveness and efficiency. Effectiveness is doing the right things, efficiency is doing things in a right way, and productivity is doing the right things in a right way. So far for the definitions. The problem is about what we consider to be output versus input and what are "the right things" and what is "the right way"? How to define the value of healthcare? How does value-based healthcare (VBHC) relate to value-based integrated care (VBIC) (P. Valentijn, 2016; P. Valentijn, 2017; R. Nuño-Solinís, 2019)? The EU Expert Panel on Effective ways of Investing in Health (EXPH) proposed to define value-based healthcare (VBHC) as "a comprehensive concept built on four value-pillars: appropriate care to achieve patients' personal goals (personal value), achievement of best possible outcomes with available resources (technical value), equitable resource distribution across all patient groups (allocative value) and contribution of healthcare to social participation and connectedness (societal value)." In a certain way, value-based healthcare (VBHC) can be seen as the application of the principle "Μέτρο για όλα τα πράγματα είναι ο άνθρωπος" to healthcare activity evaluation (perspectivism)
(See also Defining value in "value-based healthcare" (EU)).

Defining productivity and value in healthcare is a highly ideological and political activity, involving healthcare decision makers, providers and patients (M.C. Weinstein, 1977). The value of the output can be narrowly defined in monetary or equivalent measures, QALY and DALY or emotionally defined by means of an "argumentum ad passiones". Output of healthcare related activities can be the income of healthcare organizations and providers or the beneficial result to patients or society. The World Health Organization defines an outcome measure as a "change in the health of an individual, group of people, or population that is attributable to an intervention or series of interventions". Outcome (health system performance) can be defined by means of the "Triple Aim" or the "Quadruple Aim" (T. Bodenheimer, 2014). Input can be the infrastructure and the process with their direct and indirect resources and costs. Input can also be refraining from destructive socioeconomic and personal activities which lead to health problems, both in spatial and temporal relations. The healthcare process itself deals with both care and caring, meaning both quantifiable performance measures (QA, QC, budget, time, scope) and humaneness and kindness. Care and caring are often considered to be mutually exclusive and the capability of dealing with them in an appropriate way, depends on the ideological framework from which societies build and perform their healthcare activity (conceptual containment). Adhering to performance measures, IPSG's, PPC's, and NE's while at the same time neglecting humaneness and kindness, versus being humane and kind while at the same time neglecting performance measures, IPSG's, PPC's, NE's is not the way to go.
(See also Healthcare Outcome Measures).

We are witnessing the deployment of value-based healthcare (VBHC), activity-based costing (ABC) and time-driven activity-based costing (TDABC) in healthcare (M.E. Porter, 2006; G. Keel, 2017). Performance Management (PM) in healthcare is seen as an opportunity to improve not only effectiveness, efficiency, and quality of health services (value-based health care) but also the transparency of the economic activities and the availability of information in real time (T. Mettler, 2009). Management by objectives, which Peter Drucker (1909-2005 CE) put forward in The Practice of Management (1954), has also reached the healthcare industry (A. Traberg, 2011). Patient Reported Experience Measures (PREMs), Patient Reported Outcome Measures (PROMs), Key Performance Indicators (KPIs), and clinical metrics are part of the consumerization of healthcare (CoH). Measuring and managing productivity from a consumer and market perspective opens up the potential for marketization and privatization of healthcare. In a certain way, this is part of a process of increasing commodification of healthcare as a service or product, which originated with the creation of Diagnosis-Related Groups (DRG) (S. Samuel, 2005). Healthcare is increasingly seen as a commodity to be marketized and managed according to economic principles, and individuals are defined as 'health care consumers' (S. Henderson, 2002). A patient is narrowly defined as a consumer of healthcare services and products, not as a person in his own right and part of a broader context of society beyond the healthcare market. It is unwise only optimizing diabetes care employing KPIs, PREMS, and PROMS while ignoring the obesogenic and diabetogenic environment in which people have to live their daily lives. KPIs, PREMS, and PROMS allow for optimizing service and product lines, but missing the point if the essence of the activiy itself does not make any sense when seen as part of a broader picture. It is part of public choice theory as a normative theory, leading to the individual (consumer) as the common decision unit (private healthcare) instead of the aggregate whole (public health) and taking (only) healthcare market principles as the foundation of healthcare policy decisions. This is part of the broader political tendency to dismantle the welfare state and public care services (Paul Pierson, 2010). The market pendulum swings back and forth between nationalization and privatization, but we fail to see the limitations and constraints of the overall framework and consensus underlying both systems. Because of the increasing commodification of healthcare, both self-regulation and clinical autonomy of the medical profession are under significant challenge (S. Harrison, 2009)
(See also A Study in Hospital Efficiency: As Demonstrated by the Case Report of the First Five Years of a Private Hospital (E.A. Codman, 1914) and value-based health care (M.E. Porter, 2006) and Overall hospital quality star rating (USA)).

Dealing with process quality and outcome in healthcare

A healthcare provider is involved, but a patient is committed to a disease (e.g., the role of the pig and the chicken in the egg and bacon breakfast). Involvement is nice, but it doesn't have the same accountability for success as commitment does. To improve healthcare provider commitment, healthcare providers need to have some skin in the game; the goal has to have some fundamental importance. The role of feedback to healthcare providers is to give a direction that benefits the patient. Feedback is useless unless it points out a possible valuable direction for the patient.

The WHO definition of quality of care is "the extent to which health care services provided to individuals and patient populations improve desired health outcomes. In order to achieve this, health care must be safe, effective, timely, efficient, equitable and people-centred." To Err Is Human (2000), Crossing the Quality Chasm (2001), Beyond the Checklist: What Else Health Care Can Learn from Aviation Teamwork and Safety (2012) and Improving Diagnosis in Health Care (2015) have dealt with quality of care issues. Although it is an environment where human error can have devastating effects, healthcare lacks a safety and quality culture or "airline cockpit culture" (Crew Resource Management (CRM); TeamSTEPPS). When volume and income (fee-for-service) have priority over patient safety, it externalizes the cost of low quality to the patient and society. The 'checklist excuse' ("it is only about checking items") is only one of the excuses for not implementing safety procedures (e.g. WHO Surgical Safety Checklist; OR Black Box). Healthcare culture is highly resistant to change, due to the structure and organization of healthcare and how it operates (M.D. Fottler, 1987; S.W. Glickman, 2007). In order to improve the safety and quality of healthcare, it will require complex, cultural and organisational changes (R. Clay-Williams, 2015; N. Kapur, 2015). Introducing technology as a 'deus ex machina' will not do the trick, it is the culture and 'modus operandi' of healthcare organizations and healthcare workers, which will have to change. A culture of transparency and accountability is an important element of quality and safety management. Accreditation is also a way of improving the quality culture in healthcare organizations, but it is important to implement the guidelines, rules and regulations in an efficient and effective way and to avoid window dressing and bureaucratic implementations. Failure mode and effects analysis (FMEA) would help to find weak spots in the healthcare process and to avoid 'penny wise and pound foolish' quality measures. Implementing a swiss cheese model of accident prevention, would also help to improve safety. Healthcare policies, rules and regulations, efficient and effective legislation and active law enforcement should be implemented in order to protect both patients and healthcare workers
(See also To Err Is Human, 2000 and Crossing the Quality Chasm, 2001 and Beyond the Checklist: What Else Health Care Can Learn from Aviation Teamwork and Safety, 2012 and Improving Diagnosis in Health Care, 2015 and Shining a Light: Safer Health Care Through Transparency (NPSF, 2015) and Low quality healthcare is increasing the burden of illness and health costs globally (WHO) and Quality of care (WHO) and WHO Service delivery and safety and Reviews of National Health Care Quality (OECD) and Healthcare quality (EU) and Quality Of Healthcare: Health Economics Versus Health Politics).

Accountability in healthcare entails the procedures and processes by which healthcare providers justify and take responsibility for their activities (E.J. Emanuel, 1996). One aspect of accountability includes formal and informal procedures for evaluating compliance with all domains of accountability, and for disseminating the evaluation and responses by the accountable parties (E.J. Emanuel, 1996). It deals with reducing abuse, assuring compliance with procedures and standards, and improving performance/learning (D.W. Brinkerhoff, 2004). It should be relevant, evidence- and data-based. Also, medical professionalism is more than merely an activity that straddles market competition and government regulation (S. Board, 1999)
(See also International Society for Quality in Health Care).

History of dealing with process quality and outcome in healthcare

The concept of evidence-based quality improvement goes back to the 1850s with Florence Nightingale and her collaboration with the medical statistician William Farr (E.C. Kudzma, 2006). In medicine, a result- or outcome driven approach goes back to the "End Result System" of Ernest A. Codman and his article on A Study in Hospital Efficiency: As Demonstrated by the Case Report of the First Five Years of a Private Hospital (1914), in which he stated: "Every hospital should follow every patient it treated to determine whether the treatment has been succesfull for this patient, and should inquire - if not - why not - with the view to prevent similar failure in the future" (E.A. Codman, 2013). Codman put forward three core principles of quality assurance (QA):

1. Examining quality measures to determine if problems are patient-, system-, or clinician-related
2. Assessing the frequency and prevalence of quality deficiencies
3. Evaluating and correcting deficiencies so that they do not reoccur

1. Structure, or the characteristics of health care delivery systems
2. Process, or what and how care is provided
3. Outcomes, or the consequences of care

Structural indicators measure the quality characteristics of an organization. They provide information about organizational conditions within which an organization can provide responsible care and concern the organization of a hospital and the departments, the infrastructure, the instruments, the policy, the staff framework, and the financial resources. These characteristics are usually easy to measure and often form the first starting point for quality improvement.

Process indicators indicate the course of (care) processes within an organization. Usually, they concern care and clinical work, which is why we speak of clinical indicators. On the other hand, process indicators can also deal with communication with stakeholders, waiting times, length of stay, etc.

Outcome indicators (also often called result indicators) measure aspects that relate to the result, the outcome of care (care process). Examples include mortality ratios, health status, satisfaction of patient/staff, treatment of employees, cost-related aspects or financial ratios, complications after specific interventions, etc.

Michael Grossman would develop the Grossman model of health demand (1972). Health economics would be applied to a cost-benefit model or value-based health care (VBHC) (M.E. Porter, 2006). The value based framework builds upon the tradition of outcome assessment in clinical work, which started with Florence Nightingale, Ernest A. Codman and Avedis Donabedian, in combination with health economic evaluation. In VBHC, outcomes of a medical process can be represented in a three-tiered and hierarchical system (M.E. Porter, 2009; M.E. Porter, 2010):

1. Health status achieved or, for patients with some degenerative conditions, health status retained (short-term scope);
2. Outcomes related to the recovery process (time, budget, QA);
3. Sustainability of health, such as sustained elimination of disease, emergence of negative consequences (QC, long-term scope).

1. Organize into Integrated Practice Units (IPUs) (project team integration around a "DRG" or scope)
2. Measure Outcomes and Costs for Every Patient (efficiency, effectiveness, relate scope to time and budget and QC with QA, PDCA cycles)
3. Move to Bundled Payments for Care Cycles (defragmentation of scope, scope driven payment, "earned value management")
4. Integrate Care Delivery Systems (geographic capacity and capability differentiation)
5. Expand Geographic Reach (economy of scale, skills and quality)
6. Build an Enabling Information Technology Platform, which supports the entire process (system architecture):
1. Center the IT system on patients (Customer Centered Design)
2. Use common data definitions ((international) standards)
3. It encompasses all types of patient data (integrated view on patient)
4. The medical record is accessible to all parties involved in care (physician, nurse, safe and secure, ...)
5. The system includes templates and expert systems for each medical condition (reduce overhead, increase efficiency)
6. The system architecture makes it easy to extract information (configuration management)

History of industrial quality management

Quality (QA, QC) of the healthcare process is an important aspect of health service delivery. Modern industrial quality management goes back to the work of Walter A. Shewhart, W. Edwards Deming and Joseph M. Juran. Walter A. Shewhart is the 'father' of statistical process control (SPC), which employs statistical methods to monitor and control a process. Shewhart and Deming translated the (empirical) scientific process of hypothesis testing into the iterative PDCA (plan-do-check-act) approach for the control and continuous improvement of processes and products (Shewhart cycle/Deming circle). Juran put forward the "Juran trilogy", which is composed of three managerial processes: quality planning, quality control, and quality improvement (J. Juran, 1999). These three principles have to be translated into the conceptual framework of healthcare (policy). Accreditation, according to international best practices in quality and patient safety, equals quality planning. Quality assurance (QA) or "pay for quality", would take care of healthcare process quality. Quality control (QC) or "pay for performance", would take care of healthcare outcome value (value based healthcare). Quality improvement (QI) is the (iterative) approach to healthcare organization's operational process performance improvement. The combination of QA, QC and QI, which integrates healthcare organization-wide efforts, would lead to Total quality management (TQM). TQM also requires a data-driven healthcare policy and system, not only "quality by acclamatio" or as W. Edwards Deming once said "In God we trust, all others must bring data". TQM would mean a shift from fee-for-service (volume) to fee-for-quality (process and outcome) or from a provider-oriented system to a patient-oriented healthcare system
(See also What Is Value-Based Healthcare? and Value-based health care and Joint Commission and operations research and Here's how to make 'value-based healthcare' a reality and DMAIC).

Management of quality of care has to deal with the requirements of treating noncommunicable diseases (NCDs), infectious diseases, and the changing global and regional patterns of health problems. Reducing unwarranted variation and improving efficiency and patient outcomes will be required to meet the challenges of modern healthcare. Avoiding Potentially Preventable Complications (PPCs) and Never Events is an important goal of medical quality management (Primum non nocere and "talk the talk and walk the walk"). Providing high quality health care requires an optimized multifactorial and multidisciplinary process, and according to the Anna Karenina principle, it is possible to fail in many ways, while to succeed is possible only in one way. The Anna Karenina principle can be observed in complex processes which have to perform at a high quality level: "By studying the dynamics of correlation and variance in many systems facing external, or environmental, factors, we can typically, even before obvious symptoms of crisis appear, predict when one might occur, as correlation between individuals increases, and, at the same time, variance (and volatility) goes up.... All well-adapted systems are alike, all non-adapted systems experience maladaptation in their own way,... But in the chaos of maladaptation, there is an order. It seems, paradoxically, that as systems become more different they actually become more correlated within limits." (A. Gorban, 2010). High quality (healthcare) systems must meet simultaneously a number of requirements; therefore, they are more fragile (Arnold V.I., 1992, pp. 31-32). Health care quality improvement efforts have to deal with a combination of measures such as health care structures, processes, and/or outcomes. High quality healthcare requires standardized safety procedures, publicly available data on quality performance, strong lay representation, a focus on patient safety and raising medical quality standards. Industrial quality management methods, which were developed by William Edwards Deming and Joseph M. Juran, would be beneficial for healthcare process management also (avoid reinventing the wheel). The healthcare data revolution is mainly driven by pioneers in the USA and the UK such as Tim Kelsey (co-founded Dr Foster in 2000), Bruce Keogh (measurement, analysis and public disclosure of clinical outcomes), and Brian Jarman (Hospital Standardized Mortality Ratio (HSMR) methodology).
(See also ICHOM and Redefining Health Care: Creating Value-Based Competition on Results and The Getting It Right First Time (GIRFT) Programme and Data for Measuring Health Care Quality and Outcomes, OECD).

Measuring healthcare quality

Health spending is too often low-value and its growth threatens health systems sustainability. Pay-for-Performance (P4P) and Pay-for-Quality (P4Q) are incentives for quality and performance of healthcare providers. The goal is to provide higher-quality care at a lower cost. Both public healthcare, such as Medicare and Medicaid in the USA, as well as private insurance companies are increasingly using Pay-for-Performance programs. The P4P programs range from Shared Savings Models by means of Accountable Care Organizations (ACOs) to Bundled Payment Models. These programs are mainly based on process and outcome indicators, which are being used to support healthcare delivery reform (P. Van Herck, 2010; S.R. De Bruin, 2011; J.R Britton, 2014). Pay for Quality (P4Q) is about the reporting of quality measures and payment or non-payment for actual performance on those quality measures. Pay-for-Performance (P4P) or value-based payment (VBP), comprises payment models that attach payment or non-payment to healthcare provider performance. Incentives for improving the quality of care, such as Pay-for-Performance (P4P) and Pay-for-Quality (P4Q) are aimed at improving the quality, efficiency, and overall value of health care, but studies to date show mixed results (J. James, 2012; F. Eijkenaar, 2013). There is no one-size-fits-all magic formula, but a balanced, validated and verified P4Q and P4P program is one of the instruments for healthcare improvement. A careful validation of both short term and long term effects is also required when introducing a P4P or P4Q program (A.M. Ryan, 2016). Commodification of quality also seems to undermine intrinsic motivation (E.L. Deci, 1999). Besides analytical and data-driven value, healthcare also deals with meanings, values and relationships which are also important to deal with. High quality and high value healthcare should deal with both aspects of healthcare activities (balance hermeneutics with an analytical approach). We should not use "context" as an excuse for not measuring and analyzing process and outcome data, but integrate socioeconomic context into the analysis (part and the whole) (J.A. Smith, 2007)
(See also Value-based health care in Europe (OECD) and What Is Pay for Performance in Healthcare? and The Problem With 'Pay for Performance' in Medicine and Will Pay For Performance Backfire? Insights From Behavioral Economics and Bundled payment and CMS value-based programs (USA) and Hospital Readmissions Reduction Program (HRRP) and Hospital-Acquired Condition Reduction Program (HACRP) and Accountable Care Organizations (ACO) and Primary Care Medical Home (PCMH)).

Measuring healthcare quality is becoming an industry and quality measures are increasingly being used as a competitive instrument. Indicators are being used by policymakers, third-party payers, patients and healthcare professionals. Healthcare quality has to be measured, monitored and acted upon by providers and patients. When dealing with the result of an indicator it is important to understand who wants to know what and why (D.M.J. Delnoij, 2010)? Be aware of red herrings and oversimplifications in healthcare metrics (P. Zweifel, 1999; K.A. Corso, 2018). When dealing with data and their interpretation we should be aware of Anscombe's quartet and the Rashomon effect (F.J. Anscombe, 1973; R. Anderson, 2016). Both providers and patients have to have access to quality indicators, which have to be validated, verified and crosschecked (multi-dimensional) for reliability and performance (causality, correlation or dependence). Clinical indicators should be designed, defined, and implemented with scientific rigour (J. Mainz, 2003). It is also essential, as with all clinical research, that the conclusions are not only statistically significant (whether or not the value of a statistical test exceeds some prespecified level) but also clinically significant (medical importance) (S.M. LeFort, 1993; L.M. Friedman, 2014; P. Ranganathan, 2015). Statistical significance only measures how much the effect is statistically detectable or discernible. However, it is unrelated to practical significance, like the effect size or any other context-less numerical measure. The practical domain significance, the smallest meaningful magnitude of an effect, brings the context to statistical significance and determines the scientific meaningfulness of the outcome in the real world (e.g., MCID - minimal clinically important difference) (R. Jaeschke, 1989; A.G. Copay, 2007; N.M. Gibbs, 2016). Structure, process and outcome indicators are being developed and used, each with their own contribution to indicating healthcare quality (J. Mainz, 2003; J. Rademakers, 2011). Clinical quality indicators can be rate- or mean-based, providing a quantitative basis for quality improvement, or sentinel based, identifying incidents (J. Mainz, 2003). A quality indicator should act as a "canary in a coal mine", sensing danger before (irreversible) harm occurs to a patient or healthcare worker. Back in 1941, W.H. Heinrich pointed out that the greater volume of minor incidents present an advantage to do something before a serious similar accident occurs: "present day accident prevention is misdirected when it is based largely upon the analysis of major accidents" (W.H. Heinrich, 1941, p. 30).

Quality indicators should be spread as sensors over the healthcare process on a risk-based location and time (e.g. Early Warning Scoring (EWS), Failure mode and effects analysis, Swiss cheese model). Quality indicators should not be passive post-factum indicators of healthcare quality, but in-line and even predictive or ante-factum (before the event) indicators of risk exposure before an issue occurs (risk management) (M. Charlson, 1994). Process (re-)design should lower the risk of injury and harm to both patients and healthcare workers. Quality indicators have to deal with International Patient Safety Goals (IPSG), Potentially Preventable Complications (PPCs) and never events (T.A. Brennan, 1991; R.L. Fuller, 2009; Z. Shaikh, 2016)
(See also International Society for Quality in Health Care (ISQuA) and Agency for Healthcare Research and Quality (AHRQ) and AHRQ Quality Indicators and Assuring the quality of health care in the European Union: a case for action (WHO, 2008) and Comorbidity Calculations and Tools for ICD-9 and ICD-10 Codes).

Reported outcome measures can be clinician-rated outcome measures (CROMs) or patient-reported outcome measures (PROMs) (L.A. Michener, 2011). CROMs are clinician completed questionnaires relevant to assessing treatment outcomes, while PROMs are questionnaire completed by those using healthcare services. Patient-reported outcomes (PROMs) are part of the evaluation of the healthcare experience (T. Weldring, 2013). PROMs (Patient Reported Outcome Measures) are a way to capture a person's perception of their health. PREMs (Patient Reported Experience Measures) capture a person's perception of their experience with health care or service. Patient-rated outcome measures can range from a Global Rating of Change (GROC) to condition-specific measures (R. Jaeschke, 1989; L.A. Michener, 2011). Individual practitioners initially used PROMs to enhance individual patients' clinical management as part of feedback systems (N. Black, 2013). Nowadays, PROMs are also being used to inform audit and research, which involves data being collected, aggregated, and analyzed at a system level (N.J. Devlin, 2010). Comparing provider-assessed adverse events (AE) and patient-reported quality-of-life (QOL) outcomes would give us a better idea of the harm versus benefit relation (T.M. Atkinson, 2016). Validation and verification is an important aspect of establishing quality measurements of patient-reported outcomes (S.P. McKenna, 2011; B.C. Johnston, 2013). It is also important to understand the meaning of the responsiveness and minimal clinically important difference (MCID) of Patient-Reported Outcome Measures (A.R. Sedaghat, 2019; L.P. Hoehle, 2019). Responsiveness is the ability to detect change accurately, while Minimal Clinically Important Difference (MCID) indicates the clinical meaningfulness of the score or the smallest change meaningful to patients. When assessing quality of hospital care it is also important to correct for differences in hospital case mix that might account for differences in outcomes (S.D. Horn, 1984; R.H. Brook, 1985; S.F. Jencks, 1987). A 'praxis aureum' (wealthy patients) versus a 'praxis pauperum' (poor patients) will also have an impact on healthcare outcomes (social determinants of health). Patient dumping (homeless dumping) or skillfully avoiding patients from skid row by means of a pricing strategy, thereby manipulating the patient mix, also creates a bias when comparing healthcare providers (K. Blalock, 2001; J. Kahntroff, 2009). "Cherry picking" of low risk patients and "lemon dropping" of high risk patients, should be accounted for, when comparing healthcare outcomes (A.A. Desai, 2009; C.J. Humbyrd, 2018). It is also important to keep the context in mind, such as geographic and cultural factors, when dealing with patient reported quality indicators (N.W. Scott, 2008; D.M.J. Delnoij, 2012). A proxy indicator or surrogate marker, which is an indirect measure or sign that approximates or represents a phenomenon in the absence of a direct measure or sign, has to be carefully validated or otherwise taken 'cum grano salis'. We should also be aware of the so-called raven paradox, with regard to armchair "evidence". Research on the best approach of patient-reported outcome measures and dealing with the potential iatrogenic impact, should be taken care of (M. Wolpert, 2012; M. Wolpert, 2013; M. Wolpert, 2014). Framing of statements in patient surveys should be avoided as it induces bias in patient satisfaction measures (F. Dunsch, 2018)
(See also AHRQ Programs and ICHOM and MedWatch (FDA) and Quality of Life (EORTC) and Quality of Life by Proxy: A Risky Business).

Dealing with ethics in healthcare

What is the meaning of ethics in a medical context? Which kind of ethics is applied to medicine in healthcare policy and practice (R. Gillon, 1994; R.E. Eckles, 2005)? We have medical deontology and the Hippocratic oath or WMA Declaration of Geneva. But there is also consequentialism or utilitarianism. As a result, there is 'value pluralism', which states that a multitude of sometimes incommensurable human goals exist: "human goals are many, not all of them commensurable, and in perpetual rivalry with one another" (I. Berlin, 1969, p. 171). History has shown amoralism can exist in healthcare, a principle which Niccolò Machiavelli put forward in Il Principe (1532). Amoralism is the doctrine or attitude that ignores or rejects moral values or deems them irrelevant. With the application of artificial intelligence (AI) in healthcare there is also a growing concern about the ethics of artificial intelligence
(See also WMA Declaration of Geneva and Medical ethics and Nuremberg Code and Tuskegee Syphilis Study (USA) and Belmont Report (USA)).

There is a relation between quality of care and the ethical principles of healthcare policy and practice. The quality of healthcare is closely related to the four ethical principles underlying healthcare:

1. Autonomy, refers to the right of the patient to retain control over his or her body.
2. Beneficence states that healthcare providers must do all they can to benefit the patient in each situation.
3. Non-Maleficence means, "to do no harm" or "primum non nocere".
4. Justice states that there should be an element of fairness in all medical decisions. Procedures should uphold the spirit of existing laws and are fair to all players involved

With regard to healthcare quality and ethics, it is also important to distinguish between taking care of patients and caring for patients. Taking care of patients deals with objective, professional care and process optimization, while caring for patients is a humanistic way of interacting with patients that demonstrates sincere care and concern for patients simply because they are human beings (Paulson, 2004). While care can be regarded as "eradication of a problem", both efficient and effective, the individual patient as a person is not to be reduced to the problem requiring eradication. When care predominates over caring for the patient, the individual patient is reduced to an object passing through an healthcare factory (dehumanization). When caring for the patient predominates over quality of care, Potentially Preventable Complications (PPCs) and Never Events may happen
(See also Paulson, 2004; Finfgeld-Connett, 2008; Letiche, 2008 and Potentially Preventable Complications (PPCs) and Never Events and WHO global strategy on people-centred and integrated health services).

Due to the vulnerability of our patients it is important to implement effective rules and regulations with regard to safe, secure and ethical use of artificial intelligence (AI) in healthcare. An important question is upon which ethical basis does society found the ethics of an integrated hybrid analog-digital healthcare system? How to deal with both confidentiality (medical ethics) and privacy (information ethics)? What about responsibility (medical), liability and professionalism (information)? What about enforced treatment (public health), surveillance, censorship, etc. (I. Brown, 2007)? While a deontology deals with the morality of the patient-physician relation, consequentialism (utilitarianism) deals with the value (outcome) of medical care. Consequentialist and deontological ethical theories emphasize generalizable standards and impartiality, but ethics of care (EoC) emphasizes the importance of response to the individual. We can program or train a system to adhere to consequentialist and/or deontological ethics, but what to do with an ethics of care (EoC)? Thought experiments such as the trolley problem point to a utilitarian way of dealing with machine ethics. How to integrate machine ethics with (human) deontological or virtue ethics? Will machines in the end calculate the monetary cost versus benefit of a decision, just like a commercial profit and loss calculation? Do we create a modern healthcare from a motive- or consequence-oriented point of view, or in other words, from a deontological or consequentialist point of view? Does "the end justifies the means" as in consequentialist ethics or does "the means never justifies the end" as in deontological ethics? The fundamentals of healthcare policy increasingly shift towards an utilitarian approach, with the increasing technological ability to monitor and control the healthcare process and resources at the individual level. Healthcare policy merely based on benefit-cost analysis (BCA) and cost-effectiveness analysis (CEA), reduces healthcare to an utilitarian ethical framework, which may become blind for the person within the patient (human dignity, human rights, civil rights). Commodification of healthcare data, derived from a digital system opens up (analog) healthcare for data monetization, data colonialism, and surveillance capitalism (N. Couldry, 2019; N. Couldry & UA Mejias, 2019; S. Zuboff, 2019). For most (tech) companies, profit and market share are more important than ethics, privacy, confidentiality, human and civil rights, as can be witnessed on several occasions (e.g. Cambridge Analytica, AggregateIQ, NHS data-sharing controversy, Project Nightingale, etc.). Narrow minded economic reasoning (machina economicus) along neoclassical (neoliberal) economic principles and a lack of common sense (savoir faire) and medical ethics, puts limitations to trustworthy digitization, algorithmization and artificial intelligence (AI) in healthcare (David C. Parkes, 2015; Jérôme Béranger, 2016) (See also Principles of European Medical Ethics and medical privacy and The ethics of digital health technology and Beyond Ratios: Ethical and Nonquantifiable Aspects of Regulatory Decisions).

What about ethics and the way we define value? Value as a concept and what to do with added value in the health care system, is an important (ethical) aspect of modern healthcare policy and management. Financial value (profit) is only one aspect of dealing with value in healthcare, it should not be the only one. Value-based healthcare (VBHC), aiming only at increasing cost-effectiveness (monetary and shareholder value), is too limited as a guiding principle for modern society and healthcare policy development (M.E. Porter, 2010). Privatizing profit (internalizing) and socializing (externalizing) losses is not the way to go with healthcare. Medical ethics, morality and patient rights should take priority over economic benefit and commercial exploitation. Each patient and healthcare worker is a person with inalienable rights, not just "good business". The choice is not between non-profit or for-profit healthcare as such, but between who gets most of the added value: patients, healthcare workers and organizations (profit reciprocity) or public/private shareholders (profit asymmetry). Added value (profit) as such, is only a means to an end, the allocation and destiny of added value in the health care system is an important aspect of healthcare policy. The ethical values (humaneness, philanthropic attitude), the quality of the legal standards involved, and the quality and effectiveness of regulatory enforcement are also important aspects of a multi-value-driven healthcare policy. The EU Expert Panel on Effective ways of Investing in Health (EXPH) proposed to define "value-based healthcare (VBHC)" as "a comprehensive concept built on four value-pillars: appropriate care to achieve patients' personal goals (personal value), achievement of best possible outcomes with available resources (technical value), equitable resource distribution across all patient groups (allocative value) and contribution of healthcare to social participation and connectedness (societal value)."
(See also C. Marzorati, 2017 and B.H. Gray, 1983 and J.A. Muir, 1983 and Ethical Dilemmas of For-Profit Enterprise in Health Care and A Healthy Bottom Line:Profits or People? and OECD Regulatory Enforcement and Inspections Toolkit and What does the EU think is value-based healthcare? and Expert Panel on Effective ways of Investing in Health (EXPH, EU)).

Dealing with ethics and tech companies

Tech companies "have a horse in the race" when discussing access to health and healthcare data. Tech companies (social quantification industry) act as if they resemble a modern day version of The White Man's Burden (Digital Man's Burden), bringing digital civilization to the 'savages' of the analog world (digital colonization). Their growing dominance of the public sphere and invasion of the private sphere can also be regarded as a coup d'état of the "merchant caste". Their customers are not our patients, who are the commodities and raw material or data points for healthcare data monetization (D. Lupton, 2012; D. Lupton, 2014; M.M. Goldstein, 2015). Their business model depends on their capability to extract value from defenseless populations, such as patients. The like button, tracking cookies and 'patient empowerment' are the modern day version of "trade beads" being traded for "gold" (monetizable personal data, website monetization). Soft value (appeal to emotion) is traded for hard value (money, shareholder value) and the inequality of the bargain is obscured (argumentum ad passiones and other logical fallacies). The service provided to the individual users is the carrot and stick model to induce the handover of precious personal data for their monetization. On-line identity and access management (IAM) is an important part of the business model (user activity tracking). Concerning their business strategy, we could rightfully say with Vergilius: "Equo ne credite, Teucri! Quidquid id est, timeo Danaos et dona ferentes" (Aeneis II, 49). The activities of the social quantification industry also resemble 'digital Darwinism', which is a modern day version of Social Darwinism. Their Darwinist mantra is digital selection and survival of the digital as the fittest, while at the same time destroying the analog political, social and economic fabric of society. These high-tech multinational corporations (MNCs) behave towards governments, the local communities in which they operate, and their customers, as the Athenians did against the Melians in the Peloponnesian War ("ο ισχυρός επιβάλλει ό,τι του επιτρέπει η δύναμή του και ο αδύνατος υποχωρεί όσο του το επιβάλλει η αδυναμία του" (History of the Peloponnesian War, Thuc. 5.89.1: The Melian Dialogue)) (U. Etuk, 1987). In a way, tech companies operate as the civil service of a colonial administration, with regard to their employees, local governments, and customers (e.g. T.J. Omenma, 2007). It is also a kind of indirect rule, where local governments maintain law and order, but data and profits are being siphoned away by the "colonizer". Their attitude also resembles enclosure of the commons for objectification, commodification and monetization of patients as privately owned data. These companies overrate their contribution to research as an excuse to make a profit (J.P.A. Ioannidis, 2013). Commercial stockpiling of personal, social, medical and genetic data for (commercial) exploitation (monetizing) is as if putting on a pileus cornutus, without the option of removing the 'hat' as your data are now being owned by a company (e.g. 23andMe, Google, Facebook, Neustar, Acxiom, Experian, Merkle, etc.), which will exploit and sell data for commercial profit (identity, targeting, attribution, patenting) (E.C. Hayden, 2012; E. Vayena, 2013; George J. Annas, 2014; Asunción Esteve, 2017). End users are putting on the digital 'pileus cornutus' by accepting clickwrap agreements (wrap contracts), without even the necessity of a (discriminatory) law to force them to identify as a (potential) target for commercial exploitation, appropriation of user-created content, or risk of economic or social stigmatization (Nancy S. Kim, 2013; Jacobsson L., 2002; Lane J., 2010; Koenig, T.H., 2014; Rustad M.L., 2014; Barnhizer D.D., 2014; Goel V., 2014). Users of the Internet of Things (IoT), with its automated mass surveillance, are being boobytrapped into contracts which destroy their privacy (S.R. Peppet, 2014; J. McGuirk, 2015; R.P. Minch, 2015; R.H. Weber, 2015; G. Noto La Diega, 2016). Various identity attributes (devices used, biologic, biographic, biometric, behavioral, and reputational information) are being combined and commercialized. (digital phenotyping, quantified self, digital footprints, digital exhaust). Even "the home is becoming a data factory", and privacy is being commodified (J. McGuirk, 2015). People and their privacy are being commodified and being dealt with as raw material for mining personal data or as marketable gadgets (J.E. Campbell, 2002; You Are Now Remotely Controlled). Voice-activated personal assistants (VAPA) are listening, recording and processing acoustic happenings (eavesmining, eavesdropping with datamining) (E. West, 2019; S.J. Neville, 2020).
(See also privacy policy).

Massive collection of surplus and personal data and processing them in computational factories running "prediction engines", allows for the creation of an economy of action (e.g. Pokémon Go footfall, self-driving cars, web usage mining, IoT usage mining, ...). Emotional manipulation of users of social media, even in the complete absence of nonverbal cues, is an established practice (J.H. Fowler, 2008; J. Guillory, 2011; A.D.I. Kramer, 2014). The creation of the digital 'human futures market' created a 'surveillance dividend', compared to which the classical (surveillance) industry is being dwarfed (M. Symonds, 2017, p. 70; C. Tsalikis, 2019). The digital economy allows for mass surveillance (dataveillance), social engineering (computational politics) and consent engineering on a unprecedented scale (E.L Bernays, 1947; J.T. Klapper, 1948; R. Clarke, 1988; Z. Tufekci, 2014; S. Schuster, 2017). The use of on-line neuromarketing techniques drives unethical (self-)destructive consumption (E.R. Murphy, 2008; Y.I. Ulman, 2015; S.J. Stanton, 2017). A/B testing (bucket testing, split-run testing) is being used on a massive scale to "optimize" the user experience (R. Kohavi, 2017). The digital economy and social media allow for the creation of a digital panopticon (C. Fuchs, 2011; L. Mitrou, 2014). While in a liberal democracy, there exist constitutional restraints on government, these tech companies are capable of breaking all the constitutional boundaries, both implicit and explicit. They are capable of recreating the feudal and absolute state of the ancién regime through the digital backdoor, which once again no longer allows for the protection of the privacy and freedom of its citizens. In the end, they achieve the dream of totalitarianism by creating a digital panopticon where they are capable of controlling every aspect of life and civil society. "La souveraineté n’existe que d’une manière limitée et relative. Au point où commencent l’indépendance et l’existence individuelle, s’arrête la juridiction de cette souveraineté. Si la société franchit cette ligne, elle se rend aussi coupable que le despote qui n’a pour titre que le glaive exterminateur ; la société ne peut excéder sa compétence sans être usurpatrice, la majorité, sans être factieuse." (B. Constant, 2012, p. 97).

Trying to find a solution for economic and ethical boundary-crossing problems and other wicked problems goes beyond neoclassical economic principles (Charles E. Harris, Jr., 2008, p. 214-215). Healthcare workers and patients do not need a Cambridge Analytica, Pokémon Go footfall, AggregateIQ, or the social quantification industry (data brokers, ad-tech industries, ...) in healthcare. Governments, civil society, patients, healthcare professionals, academia, ethicists and responsible industries should work together in order to develop trustworthy ethical rules of conduct for artificial intelligence. Society has to deal with ethics shopping, ethics bluewashing, ethics lobbying, ethics dumping, and ethics shirking, a.k.a the five "ethics gerunds" (Ben Wagner, 2018; Luciano Floridi, 2019). Trustworthy (enforceable) ethical rules, privacy and confidentiality rules and regulations, and law enforcement are required as the tech industry is not capable of self-regulation and ethical behavior. Maintaining the delicate balance between public and private involvement in healthcare is an important aspect of healthcare policy (H. G. Frederickson, 1999)
(See also Ethics of artificial intelligence and Ethics guidelines for trustworthy AI (EU) and High-Level Expert Group on Artificial Intelligence (EU) and General Data Protection Regulation (GDPR, EU) and ePrivacy Regulation (EU) and Algorithmic Accountability Act of 2019 (USA) and California Consumer Privacy Act (CCPA, California, USA) and Deceptive Experiences To Online Users Reduction Act (DETOUR, USA) and Code of conduct for data-driven health and care technology (UK) and Data Exploitation and Identity, Attribution, and the Challenge of Targeting in the Cyberdomain (NATO) and AI Now 2019 Report and Congress wants to protect you from biased algorithms, deepfakes, and other bad AI and A.J. Hillman, 1999; McMenemy, 2016; Mandal, 2016; C. Cath, 2018; T. Metzinger, 2019).

Dealing with the dynamics of the healthcare process

Healthcare dynamics has to deal with the multi-level dynamics and interplay of healthcare delivery, involving institutions, people, analog and digital machines, logistics and point of care activities. The dynamics of healthcare has to deal with the "in-between" aspects of care (inter-esse) or the analog or digital landscape in which healthcare takes place. We will have to deal with the evolving dynamics of the healthcare process, meaning the flow of interactions between cure and care providers and patients in an evolving healthcare landscape (ecosystem, evolution). Healthcare is seen as an evolving ecosystem in a landscape in which people and information flow in-between cure and care activities. Healthcare evolves in response to politics, economical changes, population dynamics and demographics. Each participant (human, analog, digital) occupies a niche in the healthcare ecosystem (White, 1961; West, 2015). The healthcare ecosystem is the community of patients, physicians and nurses in conjunction with the nonliving actors in the healthcare environment (intelligent and dumb machines, instruments, ... ), interacting as a system. At each point of action, value is created by the cure and care provider (man and machine) for the patient. Points of action are connected by means of logistics, dealing with patients, data, medication, machines, etc. ... .

Healthcare logistics has to deal with analog and digital logistics. They both have to deal with transport logistics (planning transportation), package arrival and unloading (identification and logging of incoming packages), content handling systems (systems for moving items in and out of storage), picking and packing parts (what belongs together, goes together), loading systems (onward shipment). While analog logistics has to deal with the trafficability and transferability of patients and providers through the healthcare landscape (terrain), digital logistics has to deal with the trafficability and transferability of healthcare data of patients and healthcare providers. The domains of analog and digital logistics are conceptually related, they both deal with "something" (entity with attributes and content) moving thorough a "landscape" (terrain versus digital landscape of computers and devices). A highway, analog telephone line or Internet connection are related connectors between points of action or production. However a highway and the Internet are as different as active and passive electronic components. The active components of the Internet can inject a cognitive activity into the system, while a highway resembles a passive component. Dealing with the basic concepts of logistics in designing and developing digital logistics allows for cross-discipline modeling and avoiding "spaghetti-designs" which contain loose ends which don't interconnect (anti-pattern). One way to partition and conceptualize (digital) logistics is to model it according to the layers of the Open Systems Interconnection model (OSI model). The presentation layer however has to be differentiated into formal symbols, syntax and semantics to allow for higher order traffic rules, regulations and processing. The International Patient Summary (IPS), openEHR, ISO 13606, and ISO 13972 standard resemble a standardized intermodal container for the storage and transport of clinical data (clinical archetypes) (A. Tapuria, 2013).

As an example, the International Patient Summary (IPS) is to digital logistics what the intermodal container (IC) is for transcontinental logistics. Clinical archetypes, and the intermodal container have to be designed and built for intermodal analog or digital transport, meaning these containers (IPS, IC) can be used across different modes of transport without unloading and reloading their cargo for the sake of transport. Intermodal freight or data transport use multiple modes of transportation, without any handling of the freight (container content) or data content (IPS-content) itself when changing modes (sea, highway or local road versus Internet or local network). Loading and unloading analog or digital content only happens at the origin and destination. Active logistical components also allow for offloading part of the symbolic, syntactic and semantic processing to intermediary systems, creating an activated and integrated digital supply and production chain. Hybrid analog-digital systems should achieve foundational, structural, semantic and organizational interoperability at all process steps (horizontal) and levels (vertical). While "Computer Aided Logistics Support" (CALS) is related to analog logistics, "Computer Aided Data Logistics Support" (CADLS) is related to digital logistics, but they both share fundamental principles and will benefit from Logistics Support Analysis (LSA). A step-wise approach to standardize (design) and implement (develop, deploy) formal symbols, syntax and semantics, resembles the staged approach of project Mercury, project Gemini and finally the Apollo program
(See also Intermodal data transport for massive offloading of conventional data networks and What is Interoperability?).

Bad logistics and operational inefficiency constitute a hidden cost, draining value out of the system. The challenge is how to avoid the loss of efficiency, effectiveness and value due to gaps in the flow of people and information in healthcare (empty niche in the ecosystem). Information (analog, digital) in the ecosystem should flow regardless of the physical barriers (distance, walls) at the right time and the right place to those people who need it to do their work (physicians, nurses,..) regardless of their location (intramural, extramural, trans-mural). As a complex system, the healthcare ecosystem could be analyzed and modeled by means of systems theory (Anderson, 2016; Kannampallil, 2011). Healthcare systems also evolve as they are exposed to evolutionary pressure and selection. Most national healthcare systems resemble island ecosystems, shielded from external competition by oceans of legislative barriers. Disruptive innovation acts like a sudden environmental and competitive change, which causes a catastrophe disrupting the ecological status quo, leading to a new dynamic equilibrium for the healthcare process.

Dealing with process integration in healthcare

Healthcare process integration deals with the defragmentation of the various processes that deliver healthcare to our patients. What are to be our guiding principles for healthcare process integration: the triple aim, the quadruple aim, the rainbow model of integrated care, etc. ... (W.N. Leutz, 1999; D.M. Berwick, 2008; T. Bodenheimer, 2014; P. Valentijn, 2015)? How do we solve the healthcare process fragmentation and inefficiency? Reducing semantic information and process fragmentation, inefficiencies, and value destruction, using process improvement and disintermediation of supply chains and process administration (e.g., health insurance administration) has a huge potential for overhead and cost reduction (H. van der Aa, 2015). About 25 percent of hospital costs are supply-related (J Byrnes, 2004). Process integration or hybridization is not the same as designing and developing a traditional rigid hierarchical management bureaucracy according to Frederick Taylor, Max Weber, Henry Ford, and Alfred Sloan. Industrial production evolved from Taylorism over Fordism to Toyotism. The latest step is the dematerialization of the leading process by digitizing the basic frame of production and of a product, which is being referred to as Teslism (M. Valentin, 2019).

Management by spreadsheet is not an example of process integration but is an example of error-prone reality exclusion from the decision process (J.P. Caulkins, 2007; S.G. Powell, 2008; J. Gilbert, 2012). Excluding aspects of work-floor reality from the analytical process leads to process performance deficits, which the biased and deficient analytical process is unable to deal with. An analysis is futile without data representing the healthcare process at the Point of Care (PoC). Management by proxy data instead of direct measurements at the PoC leads to a distorted view of clinical reality. Without validation and verification of analytical tools, we do not achieve Evidence-Based Management (EBM) but merely Illusion-Based Management. Understanding the requirements of process defragmentation and (semantic) integration goes beyond implementing basic and rigid mathematical models. Semantic heterogeneity between business processes is a fundamental problem for process defragmentation and integration (J.J. Jung, 2009). Service-line management (SLM) deals with the delivery (process, P4Q) and outcomes (P4P) of care (G.F. Longshore, 1998; M. M. Byrne, 2004). A Diagnosis-Related Group (DRG) can be regarded as a service line, which contains a group of clinical pathways. Evidence based clinical pathways could be used to improve a clinical process. Creating evidence based and SNOMED CT based e-clinical pathways, which can be shared between systems, would be helpful (A.D. Alahmar, 2020)
(See also SMART Guidelines (WHO) and Business Process Management for Healthcare (BPM+ Health)).

How do we deal with healthcare process improvement, its efficiency, effectiveness, and productivity? How do we make our healthcare process and workflow more effective and efficient? Workflow management and process management both deal with process analysis and improvement. Business Process Management (BPM) includes Workflow Management (WFM). Business Process Management (BPM) deals with the entire (healthcare) organization and focuses on its processes to improve their overall efficiency and effectiveness. Business Process Workflow Management (WFM) focuses on the orchestration and automation of business process workflows. Workflows focus on the people performing the tasks and their individual roles (people and instructions), while business process management focuses on defining the individual processes of an organization in order to improve the efficiency of those processes (continuous improvement). When dealing with workflow and process improvement it is important to take into account both human factors (workflow ergonomy) and management support (process transparency), otherwise you create a mess. While Business Process Management (BPM) and Workflow Management (WFM) are mostly model-driven, Data Mining (DM), Business Intelligence (BI), and Machine Learning (ML) focus on data. The integration of models and data is required if we want to succeed and Process Mining can help us to bridge the gap between our theoretical models and real world process data (W. Van der Aalst, 2004; W.M.P. Van Der Aalst, 2011; W. Van Der Aalst, 2011; W. Van Der Aalst, 2012; W. Van Der Aalst, 2016). Modeling healthcare processes in a graphical representation can be done with a Petri Net (PN) or Business Process Model and Notation (BPMN). Process mining or Automated Business Process Discovery (ABPD) could identify trends, patterns and details in clinical pathways and Diagnosis-Related Groups (DRG) (R.S. Mans, 2008; J. De Weerdt, 2012; Z. Huang, 2012; W. Yang, 2014; F. Caron, 2014; E. Rojas, 2016). Statistical process control (SPC) could also help us to to understand and manage healthcare improvement efforts with regard to QA and QC (J.C. Benneyan, 2003; J Thor, 2007). Healthcare processes may range from structured processes (Lasagna processes) to unstructured processes (Spaghetti processes), but we should beware of blaming the process for our personal incredulity or algorithmic limitations to deal with its complexity. We have to be aware of the limitations of conceptual models and the risk of a 'reductio ad absurdum' or simplification of a model below the usefulness threshold. A (digital) model is always at least partially defective in its representation of an analog process. The 'dimorphism' of causal structures between the mathematical (digital) model and analog reality has to be taken into account. This does not mean ignoring mathematical models and statistical analysis, but looking beyond the limitations of quantitative and qualitative models to integrate a process representing work-floor reality. Rationalization, standardization, and optimization of processes has to be synchronized (stepwise) with all aspects of clinical reality and with human factors and ergonomics. We should also be aware that without (politically) dealing with vested interests and silos in the organization of healthcare, as well as professional and financial silos (silo syndrome), process reform and redesign, is not possible (P Ensor, 1988; IGI Global, 2014, pp. 345-347; G. Tett, 2016; A Spithoven, 2016).
(See also The functional silo syndrome and Wil Van der Aalst (RWTH Aachen) and Workflow Patterns and Process mining tools (TU/e) and Process Mining .be (LIRIS) and Research group Business Informatics (UHasselt) and bupaR (UHasselt) and PM4Py and XES Standard (IEEE) and OpenXES).

Process integration has to deal with administrative and bureaucratic complexity. Administrative costs vary between countries and in time, but are not always easy to compare (S Woolhandler, 1991; DU Himmelstein, 2014; HJ Aaron, 2003). In 1961, administrative costs in the USA could reach 10 or 20% (HH Humphrey, 1961). Already in 1993, about 25% of hospital spending was being attributed to administrative costs (D Hurley, 1993). In the USA approximately 25% of health care spending may be considered waste, caused by failure of care delivery, failure of care coordination, overtreatment or low-value care, pricing failure, fraud and abuse, and administrative complexity (W. H. Shrank, 2019). Administrative complexity refers to both clinical process complexity and the complexity of the billing & reimbursement process. Clinical process complexity has to do with the lack of process integration due to inefficient and ineffective process design and development. Bureaucratic complexity refers to the administrative system within healthcare organizations and government bureaucracy. An efficient and effective bureaucracy should maintain operational, tactical and strategic healthcare process order, maximize efficiency and effectiveness and reduce fraud and abuse of healthcare insurance and social welfare by healthcare providers, professionals and patients. It should balance efficiency and effectiveness of care with humaneness of caring. The healthcare process should be designed to balance integrated process monitoring and control (PMC) capabilities with ease of use. Sufficient face-to-face time is one of the (quality) parameters of the process. Spending not enough face-to-face time on a patient is not a new phenomenon, but is not always seen the same way and it varies in time and between countries (TR Konrad, 2010). In Great Britain, average visit lengths for general practitioners are between 5 and 8 minutes, whereas in the United States and Sweden, they are 10 to 20 minutes or more (DC Dugdale, 1999). Notwithstanding the perceived increase in administrative and bureaucratic burden, the reason for not taking enough time for face-to-face time with patients is also due to an inefficient and ineffective healthcare policy, clinical process design, development and deployment. The reasons for the increasing workload among general practitioners (GPs) are multi-faceted and complex to deal with (ER Svedahl, 2019). The paperwork burden is one element of the increasing workload among nurses (S Trossman, 2002, C Lomas, 2012). Adding resources, such as medical scribes, to an inefficient and ineffective process is one option to decrease the burden on healthcare workers (C Bossen, 2019). We should be aware that in a fee-for-service system, reducing the administrative and bureaucratic burden will not increase the time spent on an individual patient, but will result in increasing the number of patients being dealt with. Reducing process waste would reduce profit for health care organizations (D M Berwick, 2019 and B C James, 2011). We should improve the ability to measure, understand, and feed back to healthcare workers and organizations integrated detailed clinical variation and outcome data at the operational, tactical and strategic level. Healthcare policy and system design should create an administrative structure that uses high quality clinical information to oversee the performance of care delivery and to drive positive change in an integrated healthcare system (B C James, 2011)
(See also Bureaucracy Is Keeping Health Care from Getting Better and The State of U.S. Healthcare: An Iron Cage of Bureaucracy and Waste gobbles up 25% of US healthcare spending, JAMA study finds and Professionalism and choosing wisely).

The healthcare process involves multiple analog and digital participants and intertwined and interlocking subprocesses. Healthcare overall will need to develop more horizontal and vertical integration as it will require a more process driven approach across the boundaries of institutions (transmural) and across disciplines (transdisciplinary) to cope with the increasing complexity of care for our patients, more polypathology, and over longer periods of time (extended scope, extended time). In addition the architecture of the cure and care process will require transformation to align it with the components of the technology supporting it. A process is not independent of the supporting technology, be it supported by paper or more advanced technology. The more dimensions and features of a situation (status) and a process a technology is capable to capture and process, the better becomes the representation of reality, both in space as well as in time. Our new healthcare will need to improve its capacity to maintain more complex relations between patients and cure and care providers and other process participants (relationship management, process management). Besides this improvement of vertical and horizontal (human) relations, we will need to improve our management capacity towards our (internal and external) resources to provide cure and care (resource planning) with the appropriate means at the right time and place. About 80% of health care activities consist of logistics, which does not get the attention it requires, and about 20% is pure medicine (L. Engelen, 2018 and P. Griffin, 2016). Medical activity is moving outside the traditional hospital into home care and in the process is becoming more dispersed. Distributed communication and logistics (people, hardware, data) is required to manage this new networked healthcare process. A distributed and re-programmable analog process, aligned with its digital mirror process, with process steps and data flawless moving back and forth from the analog into the digital process components would lead to a process balancing itself dynamically across analog and digital process participants. The decidability of a problem, given the data and context, would decide on analog or digital processing of the specific step in the overall process at any given span of control level (Entscheidungsproblem and axiomatic and ontological limitations). Constructing the medical network of tomorrow will not be possible with the tools of yesterday, a complex (new) process requires innovated processes and instruments to operate (logistics and resources, management and monitoring tools). The representation of the process is a (symbolic) narrative, which has to be understood by both human and non-human participants. The analog and digital representation of the process should be interchangeable in a qualitative and quantitative way between an analog and digital representation. We should always keep in mind that "it's the process stupid".

Process design and development has to deal with the variability of the process population, both patients, resources and healthcare workers, including digital participants of a hybrid analog-digital healthcare process. A private practice or a hospital is not an automated conveyor belt production line system as in a car factory and taking into account the empirical rule when designing and developing healthcare processes allows for differentiating routine from exceptional situations. The process steps in healthcare have to deal with multifactorial and multimodal logistics of people, goods and data (e.g. hybrid process-driven). The differential variability of various process components has to be taken into account and danger producing variability (risk generating) has to be monitored and controlled (e.g. Potentially Preventable Complications, Never Events). Process flow fractionating has to allow for adaptability and flexibility at varying n-tuples of σ from the mean (μ). The "mean" can be based on a multidimensional framework consisting of required process throughput capacity (quantity, volume), disease complexity or DRG's (scope, quality), or any set of principles which creates a meaningful process partitioning. The process impact of a disease can be quantified as an inverse function of the number of patients in which it occurs, combined with its impact on the health status (disease modes and effects analysis, DMEA), leading to process impact mitigation. For instance a common cold as compared to a rare disease (cfr. TFIDF). Process fractionation is multi-factorial, with some process components in general use, while others are limited to subprocesses. The choice for an analog or digital process component should be transparent for the process as such (allow for differential instantiation). Analog and digital process participants should be capable to switch operational states in different regions of n-tuples of σ from the mean (μ), ranging from routine civil and military operations to specific subpopulations, epidemics, industrial or natural disasters, war or terrorism (elective and semi-elective versus emergency and disaster care). Incorporating and excluding different analog and digital actors at different process modes should be built into the process management system (ranges of n-tuples of σ from the mean). Process optimization for an entire ∞ σ range of operations might not be possible in one process template and any attempt to do so may lead to overall process deterioration and loss of health and even lives of many patients. Process fractionating does not equal casuistry "a propos d'un cas" or hyperfractionation of processes. Process fractionating avoids unnecessary rigorism as opposed to laxism with regards to safety precautions and procedural adherence (risk avoidance and mitigation). Procedural quality and adherence does not equal mindless bureaucracy. Although the healthcare provider is involved in the process, the patient is committed with his health and life and should be protected against avoidable harm (Primum non nocere). With limited resources a limited range of n-tuples of σ from the mean could be the focus for starting process optimization (process impact analysis, risk analysis). The range from 0 to 2σ (high probability) could be differentiated from the range from 2σ to 3σ (intermediate probability) and beyond 3σ (low probability). For an approximately normal distributed population, the values within one standard deviation (σ) of the mean account for about 68% of the population; while within two standard deviations account for about 95%; and within three standard deviations account for about 99.7% (e.g. "three-sigma rule of thumb"). These are arbitrary ranges for the sake of simplicity and as an example of the underlying principle of rational process differentiation and plurality. Situations beyond a given n-tuple of σ, e.g. 3σ, may have to be dealt with on an ad hoc basis or by highly specialized medical (rare diseases), emergency or disaster-resistant systems (e.g. Level-I trauma center). The importance of individual process components relates to their impact on overall process outcome and risk contribution (e.g. failure mode and effects analysis). Although the manageability of (parts of) the process may collapse at high n-tuples of σ, traceability should be maintained as part of the overall learning process. Process design, development and operating from 'a priori' principles has to be verified and validated by 'a posteriori' concept (first principles), process and operational analysis in a never ending cycle (intertwined multilevel, multistage and multi-scale PDCA cycles, root cause analysis). We should avoid fact- and data-averse sophistic reasoning to justify procedural laxity, which is harmful for our patients due to an unacceptable level of Potentially Preventable Complications and Never Events (e.g. the lack of a sufficiently developed medical record, medication mismanagement, unsafe material and procedures, ...). Procedural laxity goes together with ambiguity of standards, leading to arbitrary process design and execution (e.g. vagueness, sorites paradox and casuistry). Patients having elective surgery while having a Mallampati score of I and ASA Classification of I, in the absence of other significant risks to their preoperative health, should not have a high rate of adverse events or mortality due to surgery (comorbidity, e.g. APR-DRG, SOI I, ROM I, low Charlson Comorbidity Index, ...)
(See also Handbook of Healthcare Delivery Systems, Yuehwern Yih, CRC Press, 19 apr. 2016 and Overall Preoperative Evaluation and Potentially Preventable Complications (New York State, USA) and Potentially Preventable Complications Reports (Texas, USA)).

Process improvement requires a serious and long-term commitment on all levels of an organization. In Out Of The Crisis (1982), W. Edwards Deming offered 14 key principles for management for transforming business effectiveness:

1. Create constancy of purpose toward improvement of product and service, with a plan to become competitive and to stay in business. Decide whom top management is responsible to.
2. Adopt the new philosophy. We are in a new economic age. We can no longer live with commonly accepted levels of delays, mistakes, defective materials, and defective workmanship.
3. Cease dependence on mass inspection. Require, instead, statistical evidence that quality is built in, to eliminate need for inspection on a mass basis. Purchasing managers have a new job, and must learn it.
4. End the practice of awarding business on the basis of price tag. Instead, depend on meaningful measures of quality...
5. Find problems. It is management's job to work continually on the system (design, incoming materials, composition of material, maintenance, improvement of machine, training, supervision, retraining).
6. Institute modern methods of training on the job.
7. Institute modern methods of supervision of production workers...
8. Drive out fear, so that everyone may work effectively for the company.
9. Break down barriers between departments...
10. Eliminate numerical goals, posters, And slogans for the work force, asking for new levels of productivity without providing methods.
11. Eliminate work standards that prescribe numerical quotas.
12. Remove barriers that stand between the hourly worker and his right to pride of workmanship.
13. Institute a vigorous program of education and retraining.
14. Create a structure in top management that will push every day on the above 13 points

Dealing with a project approach in healthcare

Healthcare projects have a public, organizational and a personal dimension. Healthcare activities work towards a goal: health improvement for society (public), healthcare workers and patients (personal). We have to deal with the transformation of healthcare systems as an overall project and the ongoing care for our patients as an intertwined set of individual projects. Healthcare projects range from international health policy development (WHO, OECD, EU) over national, regional and local healthcare policy projects up to personal and technological projects. The goal (scope) to be achieved drives the effort and resources to be used (budget and time). An integrated project driven (iterative, agile) approach to healthcare would lead to more transparency and manageability of cure and care for society and patients. Discordance between healthcare project content and targets has consequences for healthcare systems, organizations, healthcare workers and patients. Deliverables, milestones and work breakdowns into manageable and hybridizable processes and process steps into a flexible but manageable project, would increase transparency, manageability and accountability of healthcare. Transparency would also free us from regulatory illusions, as it would confront work-floor reality with political, legal and regulatory illusions or Janus regulations. Healthcare organization based upon a scope and result driven approach is based upon project management concepts such as "The Triple Aim" or "The Iron Triangle" as a framework for healthcare strategy (W Kissick, 1994; D Kindig, 2003; DW Berwick, 2008). Besides objectivity and transparency, humaneness towards healthcare workers and patients has to be an integral part of healthcare policy and organization, which is so-called "Quadruple Aim" (T. Bodenheimer, 2014; R. Sikka, 2015). Reducing the scope of a project and externalizing negative consequences is not the way to go for a recurring set of interlocked projects, such as treating patients. Including the quality of working conditions of clinicians and staff into the scope would be a "Quadruple Aim" and reduce burnout and dissatisfaction of the healthcare workforce (T. Bodenheimer, 2004; R. Sikka, 2015). Effective requirements analysis, project scoping and avoiding sub-par scoping, avoids problems during project execution and at project closure. Getting the scope right (internal and external consequences and stakeholders) is even more important than introducing new technology into healthcare systems as part of healthcare project performance improvement. Technology may deliver "everything but the kitchen sink", but this is not enough and also not the right way to improve the performance of healthcare projects (gold plating, feature creep, scope creep, law of the instrument). Introducing "shiny tech" alone will not be sufficient to transform the performace of healthcare systems. Transformation of (international) legal frameworks, standards, organizational culture and modernizing healthcare education as part of an integrated multilevel change management program is required. The physical, legal, scientific, technological, cultural and behavioral environment in which the individual healthcare project for an individual patient is performed, limits or enables its efficiency and effectiveness
(See also Value of life and Quality of life and Scope-Blindness: Confusing Trees with Forests and The Quadruple Aim: care, health, cost and meaning in work).

Healthcare organizations and workers do not understand or like project management language, so project management terminology has to be translated into something they can and want to work with. The Triple Aim or Quadruple Aim is such a attempt. The Quadruple Aim is the Triple Aim (1-3) with one additional element (4):

1. Improved patient outcomes refers to items such as scope, effectiveness and QC.
2. Improved patient experience of care refers to items such as stakeholder management (PROMS, PREMS).
3. Lower health care costs refers to items such as budget, time, efficiency and QA.
4. Higher healthcare workforce satisfaction refers to items such as stakeholder (human resource) management.

Modern day healthcare management improvement efforts are already being inspired by enterprise architecture (EA) standards such as TOGAF and project standards such as PMBOK, CMMI and the Deming cycle (PDCA), which have their (medical) equivalent in standards such as JCI (accreditation) and clinical pathways (based on CPM and PERT) as part of the growing need for international healthcare accreditation to improve the quality of care. For instance the International Patient Safety Goals (IPSG) can be regarded as a way of risk management in order to avoid issues with regard to patient safety (Primum non nocere, risk management and risk mitigation). It is important to explain "the forest" in order to understand "the trees" of a process (e.g. accreditation). Failure to understand the true scope of a process (forrest) may lead to "overforestation" of a system with "shrubs" and "weeds" instead of "trees" (process and system bloat and dysfunction) (Y Chan, 1997; Bleistein S, 2004). A lack of subject-matter expertise when designing and developing processes will hurt healthcare providers and patients and as a result cause project damage. Healthcare providers can be managed based on their performance for both quantity and quality of care (process, outcome) in a transparent way, when a process is well designed and properly monitored (quadruple aim, time and budget). Taking into account the "Quadruple Aim" reduces the risk of burnout and dissatisfaction of the healthcare workforce (T Bodenheimer, 2004; R Sikka, 2015). The so-called Project Management Triangle (called also Triple Constraint or the Iron Triangle) can be translated in the Healthcare Management Triangle (HMT) based on scope (features and quality, quality-adjusted life-year (QALY), time, and cost. Cost-offectiveness analysis has to be integrated into a balanced analysis and evaluation of process and project performance (triple constraint analysis) (E. Litvak, 2000). Healthcare process and project improvement efforts require both process and outcome monitoring and control, which is cumbersome in a paper based process (S. Chu, 1998; S. Chu, 2001; S. Chu, 2001). Digitization requires process redesign, because simply translating a paper based process into digital leads to locking in inefficiencies, which were invisible due to the non-transparent nature of a paper based process ( Organizational alignment as a pre-requisite for project success). It is somewhat surprising to see how healthcare is rephrasing old (industrial) ideas in new words and reinventing the wheel.

Dealing with information and communication in healthcare

Healthcare data and information can be dealt with at several levels and with different goals:

1. Point-Of-Care data and information in order to assist patients and healthcare professionals directly (clinical use, cure, care and caring)
2. Operational information in order to assist operational managers (line management)
3. Tactical information in order to assist tactical managers (middle management)
4. Strategic information in order to assist strategic managers (C-Suite or CxO level)
5. Strategic internal and external information in order to assist board members (board of directors, board of trustees)
6. Regional, national and international information in order to assist governments and international organizations (WHO, PAHO, PU, AU, OECD, EU, ECDC, CDC, ...)

Dealing with healthcare information and information sharing (communication) is important and there are many different types of healthcare information. Safe and secure communication contributes to a large extent to the safety and security of healthcare. A study by the American Joint Commission International (since 2004) about sentinel events, revealed that 70% of events were caused by miscommunication (Becket/Kipnis, 2009). Communication problems between healthcare professionals and patients are an important cause of safety and quality problems (Schyve, 2007). When dealing with communication, we have to take into account communication between healthcare professionals (physicians, nurses, ...), healthcare institutions, and last but not least between healthcare professionals and institutions and their patients. Without a decent communication process, automating a broken process will not improves things, but will even make it worse when adding digital participants to the process. Communication between man and (un-)intelligent machine and between machines requires an even more cautious approach than between humans. Machines do not (always) have the critical skills embedded into their behavior to deal with content problems or wrong meaning of information, they are limited up to the syntactical level of communication. Context and meaning are part of a safe communication environment, but are difficult (expensive) to implement for digital systems
(See also JCI, Inadequate hand-off communication (2017) and JCI, Sentinel Event Data - Event Type by Year and Communicating Clearly and Effectively to Patients and The Syntax and Semantics of Medical Language and International Association for Communication in Healthcare and S. Garde, 2007).

Some basic principles underlying the implementation of trustworthy medical information systems have to be dealt with. First of all, a citizen/patient-centric design instead of an institution or vendor-centric design. Citizen/patient-centred access and ownership of medical information over the entire lifecycle of medical (meta-)information. Open and international data (syntax, semantics) and process standards should be used, instead of national and vendor-specific standards. A comprehensive representation of process and situation information and machine-readability to allow for intelligent process and content processing at the PoC. Data protection, privacy, and confidentiality of personal data should be at the heart of the system. Informed or even true consent (validated and verified transparency) or other high quality lawful basis has to be dealt with. Auditability (audit trail), verification and validation of data based upon an accurate record of electronic records access, exchange, or any (meta-)content processing operation. Safety and security should protect against unauthorized or unlawful health data processing and accidental loss, destruction, or damage (ISO/IEC 27000). Identification and role-based authentication of all involved parties is mandatory by means of open and transparent standards, such as a trustworthy international electronic identification (eID) to allow for traceable, tractable, safe and secure authorization. Electronic identification should allow for unequivocal identification of healthcare providers and patients during the entire healthcare process (extramural, transmural, intramural). An electronic identification system should provide electronic (digital) signatures, electronic seals (origin, and integrity of data), electronic timestamps (trusted timestamping) (date, time link), system authentication certificates (certified electronic certificates), and electronic registered delivery services (ERDS, proof of sending and delivery) protecting against loss, theft, damage, or unauthorized alterations. In the interconnected world of the IoT the hacking of a single device, the so-called 'weakest link', could lead to major spill-over effects to the entire interconnected system (R. Roman, 2011; H. Ning, 2013). 24/7 Continuity of service of the electronic health record (EHR) exchange service is essential to guarantee continuity of care, either during normal modes of operation as in times of healthcare crises (natural or man made-disaster). An uptime of 99.99999% of critical healthcare systems should be mandatory, and fallback procedures should be verified, validated, trained and operational. An uptime of 99.99999% for critical healthcare systems means less than 3 minutes downtime in a year, even during a healthcare crisis (system availability, uptime service level agreement (SLA)). System performance goals should be defined during normal modes of operation as in times of healthcare crises (system capacity, speed and response time, service level objectives (SLO), performance goals). The entire analog-digital healthcare system should be robust, resilient, and recoverable (all-hazard emergency preparedness)
(See also Recommendation on a European Electronic Health Record exchange format and Salgo v. Leland Stanford Jr. University Board of Trustees (USA, 1957) and General Data Protection Regulation (GDPR, EU) and Integrating the Healthcare Enterprise^® (IHE) and WHO Family of International Classifications (WHO FIC) and SNOMED CT and LOINC and DICOM and ISO 13606 and ISO 13972 and HL7 FHIR and openEHR and EESPA Glossary and OAuth and electronic IDentification, Authentication and trust Services (eIDAS, EU) and ISO/IEC 27000).

There is a conflict of interest between the way information and communication in healthcare are being dealt with. The emphasis is on exploiting and monetizing healthcare data, not the safe and secure exchange of vital healthcare data between healthcare professionals at the Point of Care (PoC). Patients, physicians, and nurses are not the primary beneficiaries of the for-profit dataification of patient and healthcare provider data. Quite often, the in-line process at the PoC is being destroyed in order to generate out-of-process profitability (based upon flawed data, GIGO). Process design and development is mainly done off-line and asynchronous, and the PoC process collapses when in-line and synchronous data entry is being forced upon the PoC. This is not the way to go.

Dealing with strategic, tactical, and operational information is an issue in most healthcare organizations and healthcare professionals. Strategic information is needed for long-term planning and directions, while tactical information is required to achieve short-term goals to achieve efficient and effective performance in cure and care teams for the patient. The lowest level of management and workers in an organization deals with operational data. Each system deals with a different span of control in content and space-time dimension. Most health care systems nowadays only collect strategic information (management information) in a structured way, lacking added value at the point of care (PoC) teams and therefore lacking tactical and operational value for the physician, nurse and patient. The cure and care teams carry the burden of data input into awkward non-ergonomic systems, while management receives flawed data due to a lack of integrated multilevel information which hinders drill-down analysis and root-cause analysis of performance problems (disjunct in time and space). The generation of value for our healthcare practitioners and our patients from data, knowledge and understanding proceeds slowly and haphazard. The process of information gathering and usage should resemble a matryoshka doll or be part of an Enterprise Architecture (EA). Integrating Strategic Information Systems (SIS), Tactical Information Systems (TIS) and Operational Information Systems (OIS) is crucial for achieving high strategic, tactical and operational performance, both efficiency and effectiveness. Integrated data capture and processing in a patient-space-time information system (PaSTIS) to allow for collection, processing and analysis of patient-centered spatial and temporal information, would improve the efficiency and effectiveness of strategic, tactical and operational decisions and management. Expanding and collapsing the patient-space-time scale allows for multi-scale processing with methods such as scalespace or differential geometry as the patient becomes the (intermediary) "pixel" or "voxel" in the N-dimensional space-time continuum (population analysis is an aggregation of "pixels" at different space-time scales). The space-time scale can be variable and anisotropic, depending on the required precision. Varying the scale (and viewpoint) of epistemological "knowing" and ontological "understanding", allows for exploring and analyzing various organizational levels and viewpoints (e.g. Wong, 2011 and Sabharwal, 2011). The environment in which the patient evolves can be 2D- or 3D-digitized, so the space-time environment becomes part of the sampling of data and 4D analysis becomes possible (IoT, GPS, WiFi, RFID, QR codes, bar-codes, ... ). Each tactical and operational entity of the organization should iteratively receive feedback on performance and quality of process execution (almost) in real-time as part of a learning organization. Teams are supplemented with operational, tactical and strategic AI-systems for assistance. Feedback value should be created as close a possible to the point of care (POC), because avoiding problems at the point of action creates most value for the patient, such as avoiding Potentially Preventable Events (PPE) and Wrong-Site, Wrong-Procedure, and Wrong-Patient Surgery and other Never Events (fix early fix cheap). Care problems discovered only late in the process are more expensive to deal with than problems discovered early. It is even more beneficial to design the process to avoid mistakes and errors to happen in the first place. Monitoring adverse events helps in identifying weak spots in the process (e.g. short PDCA cycles). Deal with healthcare data in a proper way, which is both effective an efficient. Be aware of the "data lake versus data swamp" analogy when dealing with "big data". In a data lake different types of health data are being stored, such as images, lab data, ontological data, etc. It contains structured and unstructured data, non-decidable and decidable data. The information is encrypted and digitally signed, so it can be traced back to its origin
(See also The strategy that will fix health care and Geoffrey M. Jacquez, 2005).

Great care should be taken about the quality of analog and digital data and the validation of the relation with underlying clinical reality. Authenticity, and integrity of medical data is of vital importance (data authentication) (K. Abouelmehdi, 2018; A.K. Pandey, 2020). Tampering with medical data has serious consequences for human health and lives (A.H. Seh, 2020). The misrepresentation of analog causal relations in their digital counterpart is a frequent problem in digital process replication. Information lost or misrepresented at the Point-Of-Care (PoC) is inevitably lost for further processing, analysis and decision taking. Checkbox- and alert-fatigue are increasingly threatening the quality of PoC data and patient safety (J.A. Handler, 2012; E.A. Sparks, 2017, pp 451-465; Alert Fatigue; situational awareness). Kafkaesque bureaucratic demands do not improve the representation of clinical reality, but increase the PoC workload. Of course, false arguments in order to hide abuse and fraud should be carefully analyzed and being dealt with (logical fallacies). Cross-checking of cross-linked, multi-dimensional and multi-stakeholder data is more capable to detect abuse and fraud than a (massive) pile of unrelated data. Low quality data will lead to low quality analysis and wrong conclusions being drawn (garbage in, garbage out (GIGO)). Management by spreadsheet with spreadsheets containing a hodgepodge of data is not the way to represent clinical reality at the Point-Of-Care (S.G. Powell, 2008; J. Gilbert, 2012). Once the data are being created, reality disappears behind a cloud of numbers. The report being written in the end only represents itself or to quote Jacques Derrida: "il n'y a pas de hors-text". The sheer number of data (big data) being produced does not equal epistemological validity. Data quantity in itself does not equal data quality. A big pile of data with holes, resembling a Swiss cheese (i.e. missing critical data elements), will lead to flawed analytics. Computer simulations based on flawed data and flawed assumptions (models) may generate nice tables and graphs, but they represent only prejudices and a "pie in the sky". Data gathering, processing, analyzing and acting upon the results is not just 'retail business'
(See also R. Kitchin, 2014 and The 6 Common Ways Dirty Data is Created and 6 myths about big data and 85% of big data projects fail and Swiss Cheese and the Balkanization of Big Data in Healthcare).

How to deal with digital communication in healthcare as a complement to inter-human communication? At present we are still witnessing a kind of videotape format war due to incompatible systems being developed. Refusing to agree on open standards and creating vendor lock-in and information blocking is a common phenomenon due to commercial competition. "The use of eHealth and mHealth should be strategic, integrated and support national health goals. In order to capitalize on the potential of ICTs [information and communication technologies], it will be critical to agree on standards and to ensure interoperability of systems. Health Information Systems must comply with these standards at all levels, including systems used to capture patient data at the point of care. Common terminologies and minimum data sets should be agreed on so that information can be collected consistently, easily and not misrepresented. In addition, national policies on health-data sharing should ensure that data protection, privacy, and consent are managed consistently". One of the few countries with a decent e-Health system is Estonia, which already has an operational personal Electronic Health Record, Patient Portal, e-Ambulance and e-Prescription system. The Estonian system uses KSI blockchain technology
(See also Keeping promises, measuring results, Commission on Information and Accountability for Women’s and Children’s Health, WHO, 2011 and WHO Forum on Health Data Standardization and Interoperability, 2012 and Healthcare: building a digital healthcare system, WEF and ISO/IEC 27000).

Dealing with healthcare data for scientific research and public health is an important aspect of healthcare data management. Patients as a person and healthcare workers should be the primary beneficiaries of healthcare research. Ethics, human dignity, safety, security, confidentiality, privacy (anonymization, pseudonymisation) and informed consent are to be the guiding principles of dealing with highly sensitive healthcare data. Principles of FAIR data use (Findable, Accessible, Interoperable, Reusable) are meant to be guiding principles for scientific data management and stewardship (M.D. Wilkinson, 2016; M Boeckhout, 2018). We should also distinguish between Electronic Medical Records (EMR) and Electronic Health Records (EHR), which are used by physicians and Personal Health Records (PHR), which are online systems used by patients (T. Heart, 2017). Exploitation of personal health data is unethical (profit and knowledge asymmetry) and safety, security, confidentiality and privacy of Personal Health Records (PHR) is an important issue (Y Flaumenhaft, 2018). The Principles of European Medical Ethics (2010), Article 8: "Doctors must not collaborate in the creation of electronic medical databases that may jeopardise or weaken the patient's right to privacy, safety and the protection of his or her private life. To comply with medical ethics, any electronic medical database must be placed under the responsibility of a specifically designated doctor. Medical databases may not be linked in any way to other databases."
(See also Ethical Issues in For-Profit Health Care and The FAIR Guiding Principles for scientific data management and stewardship and European Commission embraces the FAIR principles and European Council of Medical Orders and General Data Protection Regulation (GDPR)).

Healthcare Communication and Content Management (HCCM) has to deal with the entire life-cycle of medical information, between humans, humans and machines and between (un)intelligent machines, and from cradle to grave and beyond. Communication and Content Management should be patient-centered and allow for flawless, safe and secure vertical and horizontal interoperability. It has to deal with multi-modal content related to healthcare logistics and Point-of-Care information. There is strategic, tactical and operational (Point-of-Care) information to be dealt with. There is clinical and administrative information for managing (operational) patient data (complete clinical documentation). Financial information is required for tracking revenue and managing billing submissions. Data for Quality Assurance (QA), Quality Control (QC), accreditation and regulatory compliance checking (Good Clinical Practice). We also have to be aware of the ethical aspects of medical data sharing (Kate Fultz Hollis, 2016 and Brígida Riso, 2017). Information in healthcare flows back and forth through an analog and digital space-time continuum, thereby creating a hybrid process which runs increasingly in a digital environment. Data gradually evolve into knowledge, understanding and shared analog-digital cognition, based upon a shared ontology.

The width (process and situation coverage) and depth (layers of meaning) of the information has to be dealt with. Electronic Health Record (EHR) representation and interoperability has to deal with several elements, such as a patient summary, ePrescription/eDispensation, laboratory results, medical imaging and reports, and hospital discharge reports. Technical interoperability only allows content to be exchanged between computer systems, and deals with connectivity, networking protocols (Open Systems Interconnection (OSI) model, TCP/IP, etc.). Interoperability and efficient an effective processing of healthcare data requires both semantic (vocabulary) and syntactic (grammar) interoperability. With semantic interoperability, the data is not only exchanged between systems but also understood by each system. Syntactic interoperability allows systems to communicate and exchange data, however, the interface and programming languages are different. Semantic interoperability focuses on "what" is being exchanged, while syntactic interoperability focuses on "how" it is being exchanged. Semantic unification of the healthcare ecosystem requires the use of interrelated semantic standards in order to allow for semantic interoperability and processing. Standards such as SNOMED CT, LOINC, WHO ICD, WHO ICHI, WHO ATC, UCUM (Unified Code for Units of Measure), and OHDSI OMOP Common Data Model deal with semantic unification, semantic modeling and semantic interoperability. Formats such as the International Patient Summary (IPS), ISO 13606, ISO 13972, openEHR, HL7 FHIR, JSON, REST, XML, and SOAP deal with syntactic interoperability. IPS, ISO 13606, ISO 13972, openEHR, and HL7 FHIR provide digital models (intermodal container) for semantic interoperability (clinical archetypes, clinical information models) (A. Tapuria, 2013). Compatibility between semantic and syntactic standards is an important aspect of overall interoperability (storage, transport, processing). Interoperability would allow for putting an end to process silos, information silos, information blocking, and vendor lock-in for healthcare systems in order to unleash the power of decidable data and process support
(See also semantic interoperability, Vendor Neutral Archive (VNA), medical device connectivity, Observational Health Data Sciences and Informatics (OHDSI)).

Ongoing fragmented parallel non-standards-based developments in healthcare systems entail a substantial duplication of costs and human effort (A. Geissbuhler, 2013). In addition the lack of harmonization for trustworthy reuse of health data (FAIR) risks patient safety. An integrated approach to healthcare terminology management is an important part of the implementation of health information technology with electronic health records. It is not an independent activity as no terminology can be an island on itself within healthcare. Terminology has to deal with several stakeholders, such as end users (patients, clinicians, nurses, ...), administration (billing, revenue cycle), patient and population monitoring (healthcare organizations, government) and decision support at all levels. Modern healthcare requires a Copernican revolution in the way we deal with healthcare terminology (Chute, 1998; Chute 2000). Clinical data processing should be independent of (national) billing and reimbursement data processing. Administrative and financial processing (billing and reimbursement classification) is a by-product of the integrated process for capturing patient care information (cure and care) and not a separate step. The flow of meaningful information has to be managed for both safety and security at all levels and in every situation. Both confidentiality and privacy are to be dealt with (authentication, End To End Encryption (ETEE), private/public key)
(See also Fact based MOdelling Unifying System and WHO Family of International Classifications (WHO-FIC)).

Healthcare systems, both analog and digital, should operate in ways that keep patient data safe, private, confidential and secure. Privacy refers to the right to control access to oneself and confidentiality refers to personal information. Health data trafficking is an ethical, legal and law enforcement problem as medical data breaches are increasing in frequency and scope. Patients' personal health information (PHI) puts a giant target on their backs for cyber-thieves that traffic in stolen medical records. Huge centralized patient data archives (safe or vault) are sitting ducks for cybertheft as no system can guarantee 100% security (medical data breach). Hubs store decentralized data, shared among other hubs instead of centralizing the data in a centralized system. Anyone involved in the design, development and deployment of digital health systems should think twice about putting personal health information into a system about diseases you wouldn't dare to tell your own mother about (risk of stigmatization; Jacobsson L., 2002; Lane J, 2010). The trend to data monetization in healthcare poses legal and ethical challenges with regard to human dignity, privacy, confidentiality, safety and security. Datafication, dataism, and dataveillance are eroding human privacy, as personal metadata and data are increasinlgy being monetized (J. van Dijck, 2014; J. van Dijck, 2017). The dataprocessing industry is mining for "gold" (data) in the lives an activities of citizens and people all over the world (life-mining, reality-mining, data exhaust, digital footprint ) (Q. Li, 2008; Y. Zheng, 2012). While primary data are being used with (some) consent, secondary data (metadata, data exhaust) are often monetized without consent. The use of trojan horses and clickwrap agreements is increasingly eroding the privacy and confidentiality of the private sphere, and commodifying human life and activity. Worldwide, healthcare providers, companies and even governments are increasingly monetizing sensitive healthcare data, without involving patients in the revenues and benefits of the process or even without their informed consent. Without a decent legal framework such as the (European) General Data Protection Regulation (GDPR) and the FAIR use of data for research we end up with data capitalism and surveillance capitalism in healthcare. Legislation and law enforcement, informed consent and an ethics committee should protect patients and healthcare workers.

Monetization of patient data, even when pseudonymized or de-identified, poses risks for the individual patients and populations involved, e.g. due to re-identification. Great care should be taken to protect the privacy of patients when using rich medical, behavioral, and socio-demographic data for scientific research (age, date of birth, gender, geographic area, educational attainment, income level, ...). (P. Ohm, 2010; M.A. Rothstein, 2010; I. Hrynaszkiewicz, 2010; L. Rocher, 2019). Several supposedly anonymous datasets have been released and re-identified using basic (demographic) attributes (L. Sweeney, 1997; H. Zang, 2011; Y.A. De Montjoye, 2013; Y.A. De Montjoye, 2015; L. Rocher, 2019). Data brokers sell location data from visitors to abortion clinics (S. A. Thompson, 2022). In a series of classical experiments by Latanya Sweeney, the use of someone's, age, gender and ZIP code, allowed for the identification of confidential medical data (L. Sweeney, 2000; L. Sweeney, 2013; L. Sweeny, 2015). Medical data sold to analytics firms might be used to track identities (C. Perry, 2011). The for-profit use of medical data is highly problematic from an ethical and legal point of view. The certainty of direct and substantial profit is not matched to the uncertainty of a positive outcome of the promised development, when handing over sensitive data. Privatizing healthcare data profits and socializing losses in a public healthcare system, treats firms' earnings as the rightful property of their shareholders, while losses are a responsibility that society as a whole must shoulder. The FAIR and well-regulated use for (inter-)national health goals, healthcare policy development, epidemiology, scientific research, quality assurance and medical treatment improvement is still underdeveloped. (A. Geissbuhler, 2013; R. Wilton, 2017 and The Monetization of Health Data 2 Models for Data Monetisation in Healthcare and Monetizing medical data is becoming the next revenue stream for hackers and The seedy underworld of medical data trafficking and Oregon lawmakers roll out bill to let patients get paid for health data and US states pass data protection laws on the heels of the GDPR and Opinion 05/2014 on anonymisation techniques. Technical Report, Article 29 Data Protection Working Party (EU) and Council of European Union. Regulation (EU) 2016/679. Off. J. Eur. Union L 119, 1-88 (2016) and How should health data be used? Privacy, secondary use, and big data sales and No silver bullet: de-identification still doesn't work).

Information reaches (only) the right people and (only) the right machines (only) wherever they need it and (only) when they need it. A hybrid analog-digital healthcare system has to manage the entire analog and digital data supply chain (data persistence), privacy, confidentiality, safety and security. The entire analog-digital process and its analog and digital participants have to deal with the safety and security of the information and the process (design and behavior). Healthcare workers, patients, application and system providers and organizations have to deal with integrated safety and security of the entire analog and digital data supply chain. The digital data supply chain will break at its weakest spot. The system should be safe and secure, but also user friendly, so you do not need a PhD in informatics to use it. A federated identity management system, based upon verified and validated public standards, encrypted, safe and secure, is a prerequisite for the creation of an integrated hybrid analog-digital healthcare ecosystem (take care of digital signatures and beware of identity theft). Information is encrypted and digitally signed, so it can be traced back to its origin for auditing (audit trail, checksums, check digits, fingerprints). The exchange of information requires process integration and Point of Care (POC) integration in an ecosystem suitable for both man and machine. Both the flow of information and its usage have to be dealt with. Therefore we will also have to deal with the safety and security of information and we are not dealing with sales and marketing information on social media, but we are dealing with human health and lives.

The risk of data breaches and privacy leaks has to be dealt with pro-actively. Security, safety, privacy and confidentiality of patient information should be a top priority (Singapore hack and Australian My Health Record). Safety is the prevention of accidents and security is the prevention of malicious activities by people and machines. Safety relates to any concerns about things that can go wrong by accident (i.e., without anyone or anything acting maliciously), whereas security relates to any concerns about malicious intentional behavior. Every process/situation has its inherent dangers/chances of being hurt as a result of human behavior in combination with the analog and digital environment (safety of the entire ecosystem). Avoiding these inherent dangers by following good clinical and digital practices, standard operating procedures (SOP), identity vigilance (Patient Identity Integrity, Fr. identitovigilance), pharmacovigilance, etc., can be termed safety. Apart from inherent dangers involved in a situation, there might be risks from external factors. These are more often a result of one person or group's will. Avoiding these dangers is what can be termed as security, such as protection by means of identity and access management for man and machine, firewalls, encryption, safety updates, etc. A zero trust security model or perimeterless security model should be standard (e.g. NIST SP 800-207, Zero Trust Architecture, ZTA).

Information security has to deal with confidentiality, integrity and/or availability of information (CIA triad, InfoSec triad). Studying the strategy, tactics and principles of cyberwarfare in cyberspace is important for implementing a secure analog-digital healthcare process. A decent system storing health records has an audit trail, contrary to paper based systems security breaches can be traced. In the European Union (EU), healthcare organizations have to deal with the European Patients' Rights and the General Data Protection Regulation (GDPR). In the USA healthcare organizations have to deal with the HIPAA Privacy Rule. Don't forget to use a decent password, firewall and antivirus software. However, we should have no illusions, as 100% safety and security does not exist
(See also Security controls and Communications security and OpenID Connect and Electronic identification (eID, EU) and Security requirements for cryptographic modules (ISO/IEC 19790) and Top 5 security threats in healthcare and Healthcare and Public Health Cybersecurity Primer: Cybersecurity 101 and State of cybersecurity & cyber threats in healthcare organizations and Cyber Attacks: In the Healthcare Sector and CCC diagnostiziert Schwachstellen im deutschen Gesundheitsnetzwerk (D) and Agreement between Interpol and Europol (Article 6: Transmission of information) and Assessing SNOMED CT for Large Scale eHealth Deployments in the EU and Trusted Exchange Framework and Common Agreement (TEFCA, USA) and Recognized Coordinating Entity (RCE, USA) and Security Requirements for Cryptographic Modules (FIPS 140-3, USA) and Interoperability Standards Advisory Reference Edition 2019 (USA) and Managed security service (MSS) and Secure Sockets Layer (SSL) and Shibboleth Consortium and public-key cryptography and cryptography and key length and The Onion Router (TOR) and ).

Privacy, confidentiality, safety and security are a primary concern when creating a hybrid analog-digital healthcare ecosystem for the common good. We should not confuse the common good with the ambiguous and mutable concept of public interest or mere legal compliance (κοινού συμφέροντος; B.J. Diggs, 1973; B. Douglass, 1980). Without keeping our patients, healthcare workers and the common good in mind, a hybrid analog-digital healthcare ecosystem will be built with proprietary standards (vendor lock-in) and controlled by commercial interests of the likes of Alphabet (Google), Apple, Amazon, Facebook or Alibaba. The American and Asian tech companies are also known as GAFA (Google, Apple, Facebook and Amazon) and BATX (Baidu, Alibaba, Tencent and Xiaomi). For them healthcare is just another domain of society to be conquered, marketized and commodified. If you are only capable to operate according to the profit-principles of a market, which are δόξα but not ἐπιστήμη, patients have to be turned into consumers (J.A. Murnane, 2008). These companies, which are digital from the start, move towards healthcare, but with processes which are truly digital from the start and which leads to major, disruptive change. The value generated by healthcare data might be lost for public healthcare when closed proprietary systems would come to dominate the hybrid analog-digital healthcare ecosystem (e.g. Apple's Pact with 13 Health Care Systems, HealthVault, ...). Google made a deal with Ascension, which has an impact on about 50 million health records (11 Nov. 2019). The deal will allow for the identification of individual patients, most of whom have no idea or understanding about what is going on with their data. ePrivacy has no meaning for these tech giants, as privacy-infringement is an essential part of their business model. These deals are not about facilitating open (standardized), secure and safe medical process improvement, but about analysis and monetization of health data. Patients are being dealt with as raw material and data points for analysis and monetization. The quants of the healthcare data industry are now mainly focusing on the analysis of systemic and systematic risks in healthcare markets for insurance companies, not on improving the bedside patient care process itself (e.g. nowcasting in healthcare) (William C. Hsiao, 2007; J. Mun, 2014; S. Sarigul, 2014). The marketisation of healthcare requires (data) transparency, commodification and risk management, not serving the common good (Martin McKee, 2012). At least these companies demonstrate the feasibility of a (proprietary, closed) digital healthcare ecosystem and their systems are game-changers as they show how to deal with health care data and monetize patient data (data capitalism). These projects are part of the emerging industry of health data refineries, which provide data preparation factories to transform raw data (primary data and metadata) into data ready for analytics and monetization (Data versus Metadata). These deals resemble the deal between Facebook and data giants such as Epsilon, Acxiom and Datalogix to match data gathered through shopper loyalty programs to individual Facebook profiles (Can Facebook Ads Drive Offline Buying?).

The healthcare process itself requires (open) standardization of data, redesign and redevelopment into a truly safe and secure hybrid analog-digital process, both its process management and at the point of care. The more sophisticated the digital companions become, the more they are capable to create added value for patients, physicians, nurses and the overall system by moving away from the mostly passive and/or incompatible digital systems of today, towards true physical-digital partnerships. It is not about off-line (post factum) or asynchronous monetization of health data, but about in-line (synchronous) process improvement at the place and time of the encounter between patient and healthcare worker (physician, nurse, ...). The entire system moves from 'post factum' to 'in-line' and even 'ante factum' prevention and process improvement. The WWW evolves from a web of devices (things) into a hybrid and intertwined part of everyday healthcare reality. A truly integrated process combining man and machine allows for extended cognition and even expanded cognition, integrating both analog and digital actors into a unified and hybrid cognitive process. Healthcare process hybridization not only increases cognitive capacity at the PoC, but also process decidability (scope, effectiveness), capacity (volume, efficiency) and quality. Allometric engineering of the healthcare process allows for allometric scaling and breaking the performance barrier due to the incompatible, inefficient and ineffective technology currently driving the healthcare process. Currently data monetization goes together with process flow destruction, due to locking-in process fragmentation. A plethora of incompatible systems only aggravates the operational inefficiency of healthcare processes by creating islands of process incompatibility.

From process, project and analog information to digital technology in healthcare

Integrating analog and digital processes into one truly hybrid process requires re-engineering the healthcare ecosystem and positioning digital systems into the appropriate (Hutchinsonian) niche. There is no magic bullet for creating an integrated analog-digital healthcare system (Diamond CC, 2008; Brailer DJ, 2007). Medicine takes place in a complex analog environment to which digital systems have limited access. Analog information needs to be captured, converted, represented and processed into a meaning-preserving digital representation. The outcome of digital processing has to be reconverted back into analog information and actions. This resembles a sandwich model with a hard interface in between the analog and digital world. No matter how powerful the processing capacity of information, the interface limits the validity of the representation of analog information. Human interaction plays a constitutive role in cognitive processing of medical information, which is absent in digital systems. Clinicians are cross-domain knowledge workers and there is just too much variation in their work for a machine to be able to replace them. Nursing requires intuition, empathy, touching, and physical and mental agility (human touch workers) (C. Le Clair, 2019). The Canadian Medical Education Directives for Specialists (CanMEDS) roles framework identifies 7 roles and 28 core competences for physicians and nurses. Six roles in addition to the central role of medical expert are identified in this framework, such as manager, professional, communicator, scholar, collaborator and health advocate. Any digital system which is intended to support and contribute to healthcare, should at least be capable to deal consistently with these roles and competences in order to support our physicians and nurses in their work. Digital systems nowadays still resemble a brain in a vat with a few peeping holes (interfaces) to the analog world (perceptive and supportive deficiency). How to deal with the challenges and limitations of the interface with regard to process, project and analog information? The technology supporting a more humane, project and process driven healthcare should match the way we work with our patients and not the other way around (adapt to human nature and capabilities or fail). Technology should support people and processes and should not hinder their activities (balance between positive and negative impact upon cure and care activities). The design of the system should guide the user through the process in a natural and intuitive way. System design should incorporate process best practices and deal with ergonomics and human factors in its design. The absence of integrated and interconnected multilevel operations control centers (OCC) in healthcare systems also limits the scale (size, complexity) of (reliable) operation for healthcare organizations on a strategic, tactical and operational level. Deficiencies in integrated process monitoring and control (PMC) capabilities at the operational, tactical and strategic level leads to value "leakage" and diminished value realization in (healthcare) organizations. Without a shared and consistent view on the state of the hybrid analog-digital organization and its participants and processes, there can be no process hybridization. The creation of a hybrid analog-digital system requires a deep understanding of human psychology and skills besides software engineering
(See also human factors and ergonomics and human-computer interaction and Lehman's laws of software evolution).

The costs and risks of data replication within paper-based systems are an important driver towards digitization (G. Geiger, 1995). However an important issue with digital technology in health care remains with the interface between the analog and digital world. This is somewhat related to conveying information in writing, where the interface is between the mind of the writer, the pen as the transferring instrument and the paper on which the information is being submitted. Illegible handwriting in paper based medical records is not a good vector for conveying information between people (Kozak EA 1994, Rodríguez-Vera FJ, 2002, Sokol DK, 2006). The To Err is Human report (1999 CE) by the Institute of Medicine (IoM), stated that medical errors caused at least an estimated 44,000 preventable deaths annually in the United States of America alone, of which 7,000 deaths are attributable to sloppy handwriting. But of course neither is a bad analog-digital interface an example of a rich interface for exchanging complex healthcare related information. Both User Interface Design (UI), and User Experience Design (UX, UXD) are important at the Point-Of-Care (PoC). Poor user interface design (UI), such as the look of the screens, the selection of what data is important to display to the specific user (lack of context sensitive and role base design) are signs of poorly designed software (J. Kaipio, 2017). A lot of (legacy) systems which are poorly integrated with regard to the clinical process are still abundant in hospital settings. The validity of healthcare data is related to the quality of the interface between the analog and digital world, which is in most cases non-ergonomic to say the least (K. Mandl & I. Kohane, 2012). There are two ways to convert data from analog to digital information, upon which AI and other numerical systems can act. The easy one is coupling imaging and laboratory devices to computer systems. It captures monodic information from RX, lab samples, etc. . This is progressing rapidly, but gives only a partial view on our patients. This is the easy problem of digital healthcare. The other problem is the interface with the clinical world of the patient and the physician and nurse. In this case the physician and nurse are the active part of the analog-digital interface. When screen time takes away precious time from face-to-face time with our patients, then "there is something rotten in the kingdom of Denmark". Human connections and interactions are key to the performance of organizations (J. Soler-Gonzalez, 2017; J.H. Gittell, 2000). An unergonomic interface causes isolation and alienation between the healthcare worker and the patient as a person. Unergonomic systems do not allow for adequate representation of human interactions between healthcare worker and patient. Systems which do not support relational coordination between healthcare workers, due to reducing the inter-human interaction by having to spend too much time as part of the analog-digital interface, harm healthcare workers and their productivity and quality of care. Data entered into a system based upon flawed observations due to the inefficiency and ineffectiveness of human-computer interaction creates garbage and wastes time which could be spent with the patient. The loss of productivity in healthcare due to bad interface design is often ignored with regard to EHR systems. When healthcare systems, without an understanding of human interaction in a clinical setting, continue to be created with non-ergonomic interfaces and systems, healthcare will increasingly suffer and clinical productivity and quality will (continue to) deteriorate. While Amazon, Google an Apple nowadays have digital assistants like 'Alexa', 'Google Home' and 'Apple Homepod', which can entertain you about the weather, music, news and other trivia, healthcare workers still have to deal with awkward interfaces. This is the hard problem of digital healthcare
(See also Why does health care software typically have such a terrible UI/UX?).

Documentation of clinical process data is necessary, but it is important to balance the cost of registration with the benefit of information sharing. The cost of clinical data registration should be balanced with the benefit of clinical information sharing, compliance and billing requirements. Nowadays the cost is on the clinical process, while the benefit is mainly on compliance with reimbursement regulations. Clinical Documentation Improvement (CDI) nowadays is focused on maximizing claims reimbursement revenue and pays less attention to improving quality of care. Clinical data are being converted into (measurable) monetary value, not into less tangible aspects of quality of care. The costs for paper based or digital information registration are not balanced to the clinical benefits for care. The discussion is not before or against digitalization (fallacy of bifurcation/false dilemma), but in understanding the value of registration of information within the context of the healthcare process as a whole. It is also not a matter of an 'argumentum ad antiquitatem' (appeal to tradition) in order to reject digitalization, but to balance both the analog and digital process steps in the entire healthcare process. Smart, intelligent and asynchronous registration systems which could take some of the in-line burden of the registration process at the Point-Of-Care are lacking. A physician and a nurse have to be capable to understand and analyze information and act accordingly, but the act of writing down information is not the core activity, understanding, analyzing and acting appropriately is (means and end). The burden upon the healthcare practitioners to convert their analog information into its digital representation is harmful (Howe, 2018). Administrative work already consumes about one-sixth of physicians working hours in the USA and lowers their productivity and career satisfaction (Woolhandler & Himmelstein, 2014 and Rao, 2017). A study of Woolhandler & Himmelstein shows that in the United States, the average doctor spends 8.7 hours per week on administration, which is about 14.5% of a 60 hour work week. Psychiatrists spent the highest proportion of their working hours on paperwork (20.3%), followed by internists (17.3%) and family/general practitioners (17.3%). The result is reduced face-to-face time with patients, and therefore wrong data and wrong decisions being taken based upon flawed observational data (decisions by physicians, nurses and CDS systems). For the moment the user interface (UI) problem is mainly considered unsolvable and as a consequence additional resources, scribes, are being assigned to the healthcare process (Bossen C, 2019; Schultz, 2015). Inefficiencies and costs are added to the point of care (PoC), while profits are generated at the point of production of flawed EHR-systems (transfer of value creation and value destruction). EHR systems are being produced asynchronous related to the healthcare process, while physicians an nurses have to produce data in-line (synchronous) with their cure and care activities. Lack of process understanding at companies producing EHRs leads to deterioration of process performance on the work-floor. Lack of intramural and transmural process integration, data standardization and data exchange capabilities, leads to deterioration of operational process performance (silo design paradigm). When healthcare information gets stuck in a cul-de-sac or process roadblock, it blocks the process-flow required for continuity of information and care. Proprietary healthcare systems and their process and information silos resemble a sinkhole or a so-called null device (/dev/null) in which healthcare information and process flow comes to grinding halt. Vendor lock-in may allow for milking (monetizing) the "data-cow", but it it is not in the best interest of the patient and healthcare workers. Most vendors want to monetize patient data, not to create a safe, secure, efficient, effective and open process. It is more profitable for a vendor (cheaper and easy) to design and develop a "data-cow" than a safe and secure high quality and high performance (open) process. A federated database system (FDBS), combined with open standards, is one way to avoid ruthless commercial exploitation by commercial companies, and to put the data at work at the "point of operation" instead of the "point of profit"
(See also P. Carayon, 2016 and Death By 1,000 Clicks: Where Electronic Health Records Went Wrong and The clinical burden of documentation and Why does health care software typically have such a terrible UI/UX? and human factors and ergonomics and International Ergonomics Association (IEA)).

Issues of poor design, and as a consequence misrepresentation of the situation of our patients are contrary to the principle primum non nocere (Hippocratic Oath). Bad UI (data entry and presentation) and work-flow designs harm and even kills patients (M. Zahabi, 2015; R. Marcilli, 2015; Shariat, 2017; J. Cole, 2018; J.L. Howe, 2018). Many EHR systems are still showing signs of patient harm resulting from flaws in their design and use (J.M. Walker, 2008; D.F. Sittig, 2011; S. Bowman, 2013). On the other hand we should not forget the risks associated with paper based medical records. Findings from a BMJ Quality & Safety journal report show death rates at two large hospitals fell by 15% when nurses ditched paper records and were given handheld computers instead (P. E. Schmidt, 2015). Let us now shift our attention towards the processing of healthcare data (K. Adane, 2013). An important issue is the unwarranted transfer of authority to the EHR, which results in a responsibility deficit in the healthcare process for the physician and the nurse working in a hybrid analog-digital environment. The underperforming EHR with regard to quality of cure and care is often overlooked due to a false perception of algorithmic infallibility. It is important for healthcare workers and organizations to understand and pro-actively manage the risks presented by algorithms, both in a hybrid analog-digital environment and in machine-to-machine operations (IoT). A badly designed and operated EHR system is a similar risk for a patient as a badly educated and trained physician and nurse. Quality standards for digital participants in healthcare should be as stringent as those for other healthcare workers. The absence of production control principles in EHR systems is harmful (e.g. Kanban, etc. ). The data of life should flow back and forth between the analog and digital world with as little deformation and distortion as possible. Digital systems equipped with rich interfaces back and forth to clinical reality can provide us with assistance beyond the limitations we have to work with nowadays in day to day practice. More processing and presentation power should be present within the clinical encounter and trajectory as we have at our disposal for the moment. More transparency is required from the interfaces between the analog (clinical) world and its digital counterpart. In addition, more cross industry sharing of best practices with regard to service and production management, use of transindustry platforms and incorporating a core set of health data types, formalization of health care workflows, and encoded knowledge would benefit health care operations (Mandl & Kohane, 2012).

Dealing with intelligent information processing in healthcare

Modern healthcare would benefit from integrating artificial intelligence (AI) into a truly hybrid analog-digital healthcare ecosystem. Integrating AI into redesigned (hybridized) healthcare processes could allow for the extension (quantity, more) and expansion (quality, different) of our understanding of healthcare processes. It would create a hybrid intelligence, combining the complementary strengths of heterogeneous intelligences (human and artificial) into a socio-technological ensemble. Hybrid Intelligence is defined as "the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other" (Dellerman, 2019). However, there is still some homework to do on artificial intelligence and on healthcare.

The demand for algorithmic and analytic pseudoscience leads to unwise strategic, tactical, and operational decisions, as people fail to appreciate the limitations of analytical models. As Kenneth Arrow once said: [During World War II, in the weeks prior to D-Day, ]some of my colleagues had the responsibility of preparing long-range weather forecasts, i.e., for the following month. The statisticians among us subjected these forecasts to verification and found they differed in no way from chance. The forecasters themselves were convinced and requested that the forecasts be discontinued. The reply read approximately like this: "The Commanding General is well aware that the forecasts are no good. However, he needs them for planning purposes." (M. Szenberg, 1993, pp. 46-47; A. Kling, 2017). It is essential to recognize which data you use, which assumptions you make, and which models you select will significantly influence your plans and, thus, your outcomes. Once a situation or process has been datafied, nobody questions the datafication process or the validity of its assumptions and models. The mathematical model becomes a substitute for the underlying reality. We need to understand the context to deal with the uncertainty of our models. Quite often, we just need something to base our projections on and create a sense of objectivity and control. Even if we don't have a decent model, we just use the model we have at hand: "Plans are worthless, but planning is everything." (Remarks at the National Defense Executive Reserve Conference, Dwight D. Eisenhower, 14 November 1957).

Artificial intelligence (AI) is one way to create a mathematical model of reality. AI has its roots in Newtonian mathematical natural philosophy, which deals with reality utilizing mere mathematical causes and forces, that is, as mathematical concepts or relations. It is part of the belief that reality can be mathematized, which is a metamathematical axiom about the relation of mathematics and reality (isomorphism of mathematics and reality, Pythagoreanism) (V. Penchev, 2020). In AI, we also proceed hypothesis-driven but do not achieve an anhypothetic level of knowledge and understanding (ἀνυποθέτω). AI can be implemented according to a classical view on (human) cognition (symbolic) or in a connectionist, sub-conceptual, or non-symbolic way. AI algorithms, in many cases, deal with random and probabilistic processes based upon a stochastic understanding of the nature of reality they deal with, as opposed to a deterministic approach. Artificial intelligence relies heavily on probability theory and large volumes of empirical data (experimentation and observation). Learning by experience in AI or according to Aeschylus’ Ἀγαμέμνων "πάθει μαθός" (learning by suffering), is a process which can be described by means of Hegelian dialectic (Phänomenologie des Geistes) or Bayes' theorem as it is implemented in Bayesian statistics. We are capable to create mathematical models of reality, but that does not equal understanding the essential nature of reality. In AI we rely heavily on mathematical, probabilistic, and statistical inference models but do not have a fundamental understanding of the essential nature of reality (J.B. Glattfelder, 2019). By handing over our understanding of a situation to AI systems that reduce reality to a data model (reification), we perform epistemicide on our innate understanding of a situation. For AI, the mathematizable attributes are the essence of the object mathematically and algorithmically modeled. In this context the work of Max Tegmark, Alain Badiou, and Quentin Meillassoux is of some interest (L.S. Purcell, 2010; J. Clemens, 2013; M. Orensanz, 2018). There is also the frame problem of AI (J. McCarthy, 1969 and 1981; D.C. Dennett, 1984)
(See also essentialism and non-essentialism and What does stochastic mean in Machine Learning? and mental representation and The frame problem).

As an introduction to AI, let us look at different approaches to create a mathematical model of reality. Mathematical models can be classified as mechanistic versus empirical or data-driven and deterministic versus stochastic. Mechanistic and deterministic models are being used in equation-based models (EBM), such as differential equations (continuous quantities) and difference equations (discrete sequences of numbers). Mechanistic and stochastic models are being used in agent-based models (ABM). Empirical or data-driven and deterministic models is what we use in machine learning (ML). Empirical or data-driven and stochastic models are being used in statistical models. So, we can differentiate between differential equations ("rule-based") and machine learning ("data-driven"). Differential equations are mechanistic models, where the logic, rules, structure or mechanisms of the system is predetermined (theoretical model and assumptions). They contain derivatives of some unknown function, which are called the solutions of the differential equation. Another approach is to use empirical or data-driven models, where algorithms will be trained by feeding them data. Machine learning is an example of this approach, by which we bypass the need for understanding, isolating and defining the mechanism of the system in advance. Either way, we should be aware of the fallacy of misplaced concreteness when dealing with mathematical models in healthcare. With regard to artificial intelligence there is also the discussion about the possibility of a synthetic sense of agency in those systems (R. Legaspi, 2019)
(See also Differential equations versus machine learning and machine learning in R and scalespace or differential geometry and color differential geometry).

Are both healthcare and artificial intelligence (AI) ready for each other? This article deals mainly with conceiving and designing a hybrid analog-digital healthcare process (structure, process, data, outcome), less with artificial intelligence (AI) (deep learning is a subset of machine learning, which is a subset of artificial intelligence). Without redesigning healthcare in order to integrate intelligent systems into the healthcare ecosystem, it will remain a fragmented effort. In addition AI needs to mature and adapt to the ethical and legal rules of the healthcare environment. Of course there has been some progress since the days of the Logic Theory Machine (A. Newell, 1956). Although in recent years artificial intelligent (AI) systems based on deep learning have made great progress, there is still a lot of work to do before they become anything more than "idiot savants" lacking "savoir faire" (common sense) (E. Davis, 2015; H.J. Levesque, 2018). Over the years AI has experienced several hype cycles and AI winters (D. McDermott, 1985; D. Crevier, 1993, p. 203; J. Russel, 2003, p. 24; J. Grudin, 2009). Questions remain with regard to its clinical value and its ethical and medico-legal impact (H.L. Dreyfus, 1992; D.S. Char, 2018; R. Challen, 2019; J. He, 2019; E.J. Topol, 2019). Clinical decision support system (CDSS) malfunctions, which occur commonly, require a better understanding in order to prevent and detect these malfunctions and other problems of AI systems (R. Caruana, 2015; A. Wright, 2016; S.E. Labkoff, 2017; E. Kilsdonk, 2017; R.A. Greenes, 2018; S. Van de Velde, 2018; D.F. Sittig, 2018; J.W.M. Brunner, 2018; G. Levy-Fix, 2019). The algorithms that feature in most AI research literature are for the most part not applicable in clinical practice (T. Panch, 2019; A. Rajkomar, 2019). The quality of the validation and verification process of medical AI systems by government agencies should adhere to best practices to ensure their reliability and safety in real-life clinical situations (E. Wu, 2021).

Winning a game of Jeopardy, Go, chess or no-limit Texas hold'em-poker, is still far away from the complexity of real world healthcare problems (F. Chollet, 2017, p. 325). Games like Jeopardy, Go, chess and poker also don't require an Hippocratic Oath in order to deal with the ethics of the game. Their limited and stable state space allows for a limited set of rules and regulations to be applied in solving problems within the complexity and structure of this microcosm. These systems will only inform you about what actions were implemented at the time, given the data which were being fed into the system. They will not inform you about the broader context about what is happening at the time in a real-life situation, which is a recipe for disaster if the broader context is markedly different from the model. AI systems have a lot of specialized knowledge, but lack common sense (savoir faire) and ethical awareness. Many artificial intelligence (AI) systems still have people working invisibly in the background to fill the gaps with reality which is still out of reach of the algorithms of intelligent systems
(See also The limitations of deep learning and Decisions, not data, should drive analytics programs and Leading With Decision-Driven Data Analytics and Closing the Data Value Gap (Accenture, Dublin, 2019)).

What is possible with AI in healthcare? Based upon (presumed) understanding of (human) cognition, different approaches to AI were developed within the limitations of their theoretical framework and computing capabilities. The basic concept of every AI system is to classify a view on reality into distinct classes by means of categories (features, parameters, variables), which goes back to Aristotle or the scala praedicamentalis of Porphyry. An iterative, multidimensional, multiscale, multi-classifier approach allows for climbing up and down the classification tree and creating a "Porphyrian tree" (coarse to fine grained multi scale classifier space or stack). Artificial intelligence (AI) can be divided into four main types: reactive machines, limited memory, theory of mind, and self-awareness. There is supervised (labeled training data, classification, regression, ...), unsupervised (no labeled training data, dimension reduction, clustering, ...) and reinforcement learning (RL) being used in machine learning (ML). There is also semi-supervised learning, which falls between unsupervised learning and supervised learning. We can also distinguish between narrow and general AI. Narrow AI nowadays outperforms humans in some very narrowly defined tasks (e.g. chess, Go, ...). Artificial general intelligence (AGI), which allows a machine to apply knowledge and skills in different contexts, is still in its infancy. We also have Good Old-Fashioned Artificial Intelligence (GOFAI) or "symbolic artificial intelligence", which resembles a traditional computer program. This type of AI depends on elaborate algorithms processing different kinds of input into the desired output. GOFAI is based upon representation or symbolic representations of what is supposed to represent reality (ontological fallacy and Whitehead 1926). There is also behavior-based AI, which uses biological systems as a model and which relies on adaptability. Another type of AI is based upon the principles of artificial neural networks (ANN). One of the first systems was the perceptron, invented by Frank Rosenblatt in 1958 (M. Olazaran, 1996). In 1970 Seppo Linnainmaa in his master's thesis laid the foundation for backpropagation (S. Linnainmaa, 1970). The basic principles of (ANN-based) AI go back to the work of people such as Geoffrey Hinton and the principle of backpropagation, but will need a paradigm shift in order to succeed in broad real world situations. Backpropagation enables computers to learn by iteratively adjusting the weights of a neural network in order to minimize the error between the model's prediction and a ground truth comparison. Convolutional Neural Networks (CNN) equipped with a multi-scale scale-space convolution set (kernel), resemble a stacked graphical Aristotelean categorization (each filter is a scalable geometric attribute). Convolution is using a 'kernel' to extract certain 'features' from an input (image). A (traditional) Convolutional Neural Network (CNN) is actually a Cross-Correlation-Neural-Network, rather than convolutional as the mathematical operation that it is doing is cross-correlation. Discrete convolution is defined to be the sum of a series times the other series reversed, while in CNN math, there is no reversal of any series, which renders it to be cross-correlation. Artificial neural networks can be a combination of convolutional neurons and perceptron-neurons. Another approach is to use a generative adversarial network (GAN). There is still a lot of work to do. Simplifying reality to fit AI by throwing away complexity is the wrong way around to solve real world problems. AI still breaks when the requirements of the problem it's trying to solve are slightly changed (which is why they work well in Go and chess on a closed-world assumption). Artificial neural networks (ANN) are often trained with a closed-world assumption, meaning the test data distribution is assumed to be similar to the training data distribution. When employed in a real-world environment, this assumption doesn't hold true, leading to a significant drop in performance of the system. It fails ungraciously due to Out-of Distribution Data (ODD), while still providing high-confidence classifications and predictions (I.J. Goodfellow, 2014; D. Amodei, 2016; D. Hendrycks, 2016). This is not a big problem in tolerant applications like product recommendations or social media (up to a certain point), but it is unwise to employ such systems in intolerant domains like healthcare, government, law enforcement, etc.
(See also Timeline of artificial intelligence and timeline of machine learning and types of machine learning algorithms and Meet the fake celebrities dreamed up by AI).

The use of large datasets for the training of models does not always protect against bias (skewed decisions) and variability (noise). In Machine Learning (ML), there is always a trade-off between bias (underfitting, too simple) and variance (overfitting, too complex) of a model, with the risk of generalization errors. Cross-validation techniques also do not protect against (human) bias in the data sets chosen for the "training dataset" and the so-called "unknown data" (e.g. "Google gorilla's", "Twitter image-cropping AI", "Facebook primates"). The Google, Twitter, and Facebook algorithms are only a few of several embarrassing examples of algorithmic bias (M. Garcia, 2016). The Dutch childcare benefits scandal is an example of social bias in automated decision-making algorithms with disastrous consequences (S. Alon-Barkat, 2022). Social and racial bias in AI algorithms are a major problem (K. Crawford, 2016; J. Zou, 2018; H. Ledford, 2019; R. Benjamin, 2019). The AI community has to do better on fairness, accountability, and transparency in machine learning systems (S. Barocas, 2017; M.B. Zafar, 2017; J. Buolamwini, 2018; N. Mehrabi, 2019). Partitioning a biased sample of data into complementary subsets for cross-validation does not necessarily represent the real world data universe in which the system will have to perform. This resembles the "Kubinyi paradox" in quantitative structure-activity relationship (QSAR) studies, where there is no relationship between internal and external predictivity, and high internal predictivity may result in low external predictivity and vice versa (H. Kubinyi, 2004). Reality exclusion from datasets is a profound problem in developing intelligent systems (e.g. biased WASP datasets). We should be aware of the sometimes arbitrary nature of hyperparameters in machine learning, which are used to manage and control the learning process. Feature engineering and feature creation can also introduce bias. Careful consideration with regard to performance metrics (error measures, accuracy measures, forecasting, prognostics), is required (A. Botchkarev, 2018). Classification evaluation metrics for classification models, such as the confusion matrix, accuracy, precision, recall (sensitivity), F1-Score, misclassification rate, and AUC ROC (Area under the Receiver Operating Characteristic curve) should be used appropriately depending on the nature of the problem. Class imbalance, where the classes are not represented equally, is also a problem to be dealt with (N.V. Chawla, 2009 pp. 875-886). Accuracy is not the metric to use when working with an imbalanced data set, and in such case, different performance measures should be used (accuracy paradox). Careful evaluation, independent verification and validation (IV&V) of a system is always required. Modern AI systems may beat man on well defined problems, but they are still far away from general applicability in health care or other real-life situations (ontological and epistemological limitations). We have to be aware of the vulnerability of shortcut learning or 'Clever Hans'-strategies based upon spurious or artifactual correlations in training sets, which may not be present in complex real world situations (O. Pfungst, 2011; S. Lapuschkin, 2019; R. Geirhos, 2020; cleverhans blog). Shortcut learning occurs whenever a model fits a problem on data not expected to be relevant or present in real-world scenarios (R. Geirhos, 2020). Artificial intelligence (AI) in healthcare has to be transparent, explainable (XAI), interpretable, and accountable in order to become part of the healthcare process (B. Letham, 2015; Z.C. Lipton, 2018). Bookkeeping of parameters, material and methods, and reproducibility are important in public and private healthcare (transparency). Demanding physician certification, accreditation and evidence-based medicine (EBM), while at the same time allowing for 'black box AI' is a contradiction and goes against the right to explanation. Opaque 'black box AI' exposes patients, healthcare workers and organizations to unacceptable risks and legal liability. Limited liability and warranty for AI systems and the small print and trojan horse strategies of tech companies, are not compatible to trustworthy deployment of these systems in healthcare
(See also Algorithmic Justice League and Adopting AI in Health Care Will Be Slow and Difficult and Artificial Intelligence Is Rushing Into Patient Care - And Could Raise Risks and AI in healthcare - not so fast? Study outlines challenges, dangers for machine learning and cleverhans blog and A majority of AI studies don't adequately validate methods and How IBM Watson Overpromised and Underdelivered on AI Health Care and Doctors Are Losing Faith in IBM Watson's AI Doctor and The Opportunities and Risks of Artificial Intelligence in Medicine and Healthcare and The "inconvenient truth" about AI in healthcare and Artificial Intelligence and Black-Box Medical Decisions: Accuracy versus Explainability and Machine Vision, Medical AI, and Malpractice).

Vast domains of reality still escape algorithmization or electronic execution of selection processes, which would be mandatory for artificial general intelligence (AGI) as opposed to present-day narrow AI or weak artificial intelligence. On average, present-day general AI systems perform on the level of a six-year-old child in first grade, with an IQ of about 47 (Liu, 2017). The deployment of ontologically and epistemologically limited AI over vast domains of modern society leads to "digital reification", where the basic characteristics of underlying technology algorithms are increasingly determining how we relate to reality through an algorithmic filter (J. Lanier, 2011, p. 11). The algorithmic filter of ontologically limited AI acts as a low-pass filter, blurring the fine grained and non-algorithmisable details of reality. The metaphysics (first principles), axioms and postulates of algorithms only allow for a biased and locked-in approach to and representation of reality. Objectivity of data does not equal impartiality of decisions. The amount of data being processed (quantity) is not unequivocally related to the quality of the decision taken by an AI system. The epistemological validity is not (only) a matter of data volume (L. Floridi, 2012). The validity of a decision (approximate truth of an inference) is a qualitative aspect of a decision, not merely related to a (big) pile of data: "Der Mangel an Urteilskraft ist eigentlich das, was man Dummheit nennt, und einem solchen Gebrechen ist gar nicht abzuhelfen." (I. Kant, 1787; W.R. Shadish, 2002, p. 34). Artificial intelligence systems are statistical-correlation engines which give answers without explanations and without uncovering underlying causal mechanisms (G. Minati, 2019). This approach is called "theoryless knowledge", which is empirically acquired and validated knowledge without accompanying theory development. It is related to "trial and error" and masks the underlying fact that most correlations are spurious (C.S. Calude, 2017). Like all empirical based generalization it is vulnerable to the "black swan" problem of induction when moving beyond the training set. Results "a propos d'un cas" are not sufficient to rely on when dealing with healthcare and human lives. In addition it creates a false sense of objectivity but is vulnerable to a lack of impartiality due to training set bias. Objectivity is about sticking to the observable facts without bias (intrinsic aspect), while impartiality is about being neutral and fairly giving all sides an equal value without bias (extrinsic aspect). AI will need to move beyond backpropagation if we want to teach computers to achieve unsupervised self-learning like that of human infants, according to Geoffrey Hinton (Artificial Intelligence Pioneer Says We Need to Start Over). Humans can learn concepts quickly in unstructured, unsupervised learning environments. Although AI systems are already capably to support medicine, they do not replace a specialist or a general practitioner who can deal with multimodal and unstructured information all at once. People have a (philosophical/intuitive) model in their mind about how the whole world works, which is (still) lacking for AI. Conceptual and symbolic thinking is a human capacity which AI systems (still) lack. Humans see both the trees and the forest with their capacity for multiscale and multimodal thinking. Physicians are capable to deal with ambiguity and pick up on subtle clues, which AI systems can't. These systems also don't match a human doctor's comprehension and insight. Physicians and nurses use both heuristics and genetic reasoning, which goes backward analytic and forward synthetic, testing hypotheses against medical decision algorithms based upon incomplete, multimodal and evolving real-life information. Backward analytic reasoning deals with inferences in terms of probabilities, while forward synthetic reasoning is based upon recursive application of medical knowledge (building blocks, decision trees) to arrive at more complex solutions from available knowledge components than are available from the building blocks themselves (poly-morbidity). The outcome of each iteration serves as the starting point for further iterations thereby permitting the generation of multiple complex solution pathways towards a diagnosis and treatment. A combination of heuristics and genetic reasoning should operate as a checks and balances system for cognitive bias
(See also Artificial Intelligence Incident Database and The Risks of Bias and Errors in Artificial Intelligence and Google 'fixed' its racist algorithm and The Humans Working Behind the AI Curtain and Behind Every Robot Is a Human and The AI Supply Chain Runs on Ignorance and Analyzing & Preventing Unconscious Bias in Machine Learning and A Little Review of Domain Adaptation in 2017 and The Role of Randomization to Address Confounding Variables in Machine Learning and The Politics of Images in Machine Learning Training Sets and Attacking Machine Learning with Adversarial Examples and Is AI Riding a One-Trick Pony? and Kritik der reinen Vernunft, I. Transzendentale Elementarlehre. Fußnote, Immanuel Kant, 1781 (Zweite, erweiterte Auflage 1787 and Experimental and quasi-experimental designs for generalized causal inference, W.R. Shadish, T.D. Cook, T.T.Campbell, Houghton, Mifflin and Company, 2002 and "Intelligence Quotient and Intelligence Grade of Artificial Intelligence, Liu, Feng; Shi, Yong; Liu, Ying, Annals of Data Science. 2017:4 (2): pp. 179-191).

Well designed systems could take some of the burden of process management and support from our physicians and allow them to focus more on what they can do better than machines. The quality of studies being performed to prove the usefulness of AI in healthcare has to improve with regard to real-world situations and methodology (D. Lazer, 2014; X. Liu, 2019; J. Wiens, 2019). For predicting social outcomes, AI is not substantially better than manual scoring using just a few features (J. Dressel, 2018). Although in retail and finance markets a performance slightly better than 50% (tossing a coin) may be acceptable, in healthcare the stakes are higher and the performance of a system should prove itself in real-life situations (no "Google gorilla's" in healthcare, caveat emptor, information asymmetry). Time series forecasting in healthcare also poses some additional challenges of life and death, compared to less critical domains such as finance, supply chain management, production, and inventory planning. Although behavioral economics, psychographics, psychodynamics or psychodemographics, digital footprints, and data analytics may allow for framing and nudging consumers to buy products or to make voters to vote in a certain way, it is not the way to go to with high stake health and healthcare decisions (e.g. S.C. Matz, 2017). The use of nudging ranges from the Behavioural Insights Team (BIT) to the Facebook-Cambridge Analytica data scandal. The digital and social tech industry approach to "move fast and break things" or "throw everything at the wall and see what sticks" is not acceptable in healthcare, where the stakes are "somewhat" higher than in retail, finance or politics (e.g. commitment versus involvement). In those subdomains of medicine where AI makes progress, e.g. process monitoring and control (PMC), logistics, imaging, etc., we should give it a (supportive) place in the healthcare process. A step-wise (iterative) approach, with extensive process validation and verification, will allow us to adapt and re-engineer the entire process to accommodate its new participants (e.g. neuroimaging and Tech giants tap into AI healthcare market). AI can be plugged into process monitoring and control (PMC) or directly into the diagnostic and therapeutic process. In both cases an ontological space has to be created in which man and machine can work together in order to avoid a Babylonian confusion. AI has to be able to convey meaning (semantics) to man and machine in a web of process-participation. No participant of the healthcare process can be an island and un-integrated systems won't work (R.A. Miller, 1990). Semantic un-integration will destroy information at every transfer of information in a process web, due to loss of meaning and information, resembling a "telephone game" (partial or non-overlapping and shifting blobs of meaning).

It is important to move beyond the "echo chambers" of the AI hype and the unrealistic sales and marketing stories (snake oil, pipe dreams, FITD, Frankenstein syndrome) (D. McDermott, 1976; B.R. Gaines, 1984; H.L. Dreyfus, 2012; G. Marcus, 2018; A. Narayanan, 2019; P. Taylor, 2019; S. Lapuschkin, 2019; M. Raghavan, 2020; R. Geirhos, 2020; E.M. Bender, 2021). According to Amara's Law "we tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run". It also interesting to read A sociological study of the official history of the perceptrons controversy (M. Olazaran, 1996). Aesculapian authority and professionalism is not something to be achieved easily (M Siegler, 1973). There is a middle way (via media) between the artificial intelligence (AI) dystopia and utopia. With careful integration, evaluation and validation, AI systems will have their proper place (niche) in a modern and integrated hybrid analog-digital healthcare ecosystem. Artificial intelligence and healthcare systems will have to change for this integration and hybridization to happen. In the meantime, let us not wait for AI to catch up and let's build better data and processes already, in order to deal with our present day challenges and to prepare ourselves for the future. Nowadays we have walls without a system, which has to be transformed into a safe and secure system without walls. Better data (syntax, semantics), communication and better processes will benefit both man and machine, first of all our healthcare workers and patients. We will need to create integrated environments or ecosystems that allow the integration of intelligent systems within the healthcare process. Stand-alone systems won't work (RA Miller, 1990). The integration of intelligent systems into the healthcare ecosystem requires a breadth of knowledge and collaborations that goes beyond AI which is being developed on an island within an artificial environment (VL Patel, 2009). We should try to avoid a "shazai kaiken" (apology conference) for not improving our healthcare data and processes to meet the challenges and demands of modern day healthcare. In the 1960's we went to the moon, which was an engineering triumph, and what do have now: "social media". Creating the digital equivalent of a shopping mall (online shopping), content recommendation systems or a digital gossip tool (social media) is not the most efficient or effective way of creating added value for the fundamental problems of modern society. A lot of present-day AI-systems have more commercial value for shareholders than real value for society as a whole. They are primarily meant to drain money (commercial value) from consumers, not to improve the quality of life of society (e.g. webshops, social media, online advertising, influencer marketing, finance industry, voter manipulation, bots, ...). Unicorns and successful initial public offerings (IPO) may satisfy shareholders, but the healthcare, social and ethical value for society is not the same as the financial profit for investors and shareholders. Technology follows the money, not social value for society. This way technology creates gilded cages in a social and ecological wasteland. Commodifying and monetizing consumer data is more profitable than improving the healthcare process and interoperability. Nowadays, it is still more easy to exchange gossip, fake news, alternative facts and conspiracy theories, on social media, than live saving healthcare data between healthcare professionals and organizations in a safe and secure way. The way we use the communication and cognitive potential of digital technology is not yet meeting the real challenges of modern healthcare or society. In the 1960's the Cold War, Sputnik and the launch of Yuri Gagarin lead to the Space Race to the moon. It took political vision by President John F. Kennedy (1917-1963 CE) and the commitment of the US congress, senate and an entire nation to get to the moon (The Decision to Go to the Moon). A lot of our brightest engineering minds are now working on digital shopping malls or digital gossip tools. Instead of creating a global cognitive and semantic "Healthcare program" like the Apollo program, healthcare systems are cognitive and semantically disconnected. The rocket with the healthcare communication and cognitive payload is not yet leaving the launch pad. It remains a strange situation that healthcare innovation is not capable to create the same political enthusiasm and resource allocation as space travel and commercial endeavours (Etzioni, 1964)
(See also L. Winner, 1997 and D. Acemoglu, 2019 and AI: Utopia or dystopia? (WEF) and Debunking The Myths And Reality Of Artificial Intelligence and On NYT Magazine on AI: Resist the Urge to be Impressed).

Go with the flow

Figure 1: Healthcare embedded in a matrix of interacting systems. Patient and care provider become embedded in a supporting process filled with communicating systems.

Figure 2: Increasing participation of interconnecting systems. The capabilities of the interposed systems to create true added value increases with their capabilities to process the content of the transfer.

Why should we hybridize the entire healthcare ecosystem up to a semantic level (coded meaning)? Isn't it enough to create ever more intelligent systems? Why should we bother about human–computer interaction (HCI) when we have AI (J. Grudin, 2006; T. Winograd, 2006)? Hybridization of cognitive systems would allow for cognitive integration, extension and expansion of the healthcare process. Cognitive extension refers to a higher capacity of cognitive processing, while cognitive expansion refers to adding new (non-human) cognitive approaches to the healthcare process. Process hybridization would allow for increasing the cognitive capacity of the process at the Point of Care (POC) beyond the seven plus or minus two units of information we are capable of in short-term memory. Hybridization of cognitive systems, such as analog and digital systems not only extends and expands cognitive ability, but also compensates for cognitive bias and algorithmic bias. Dealing with cognitive bias, on an individual and group level, is an important aspect of healthcare quality improvement (D.A. Redelmeier, 1990). Creating a hybrid cognitive architecture and process would allow for creating a high performance cognitive ecosystem in which both man and (intelligent) machine can participate, based on their capabilities and capacity. A digital ontology should represent man in the digital world (mirror of man) and the platform (digital web) should reflect the process (mirror of the process). The entire system resembles a multidimensional web of interconnected nodes, linked by means of process capabilities.

Both system-related and cognitive components put limitations on the performance of healthcare systems (M.L. Graber, 2005). Due to the limitations of the human mind and the uncertainty of information, we have to employ heuristics in order to allow for taking decisions in an acceptable time-span. However, judgment under uncertainty by means of heuristics is vulnerable to bias (A. Tversky & D. Kahneman, 1974; N.V. Dawson, 1987; J.N. Itri, 2018). Evidence-based medicine (EBM) is an attempt to introduce statistical decision theory in healthcare, in order to deal with flawed heuristics. Evidence-based medicine can be regarded as the medical equivalent of dealing with the issues being dealt with in prospect theory. Accepting human fallibility and integrating human heuristics with intelligent systems, could provide us with additional cognitive capacity in complex medical situations. It is not a one-way process, as common sense (savoir faire) and critical evaluation and integration will have to compensate for algorithmic bias in a system of checks and balances
(See also decision theory and heuristics in judgment and decision-making and prospect theory).

Developing an understanding of the potential, issues and risks of creating a hybrid analog-digital healthcare system and process, requires crossing the analog-digital divide. Besides a deep understanding of the political, cultural, ethical, legal, social, economical and medical environment in which a hybrid analog-digital healthcare system has to be designed, developed and deployed, a clear understanding of the technological possibilities and challenges is required. Underestimating analog and digital complexity, and a lack of subject matter expertise (SME) and resources (capital, people), is a constant factor in forecasting the time-frame and impact of technology development in society. An underestimation of the challenges ahead, a lack of understanding of the complexity, inadequate planning and ineffective management causes the usual delays and cost overruns when creating a hybrid analog-digital healthcare system as with most large and complex projects (e.g. Berlin Brandenburg Airport). More than half of the costs for implementing an EHR are required for change and less than half are ICT related costs (M. Rosenmöller, 2014, pp. 267-269).

Let us start with taking a look at the interactions between healthcare workers and patients and how they are evolving in the emerging analog-digital environment. Essentially the care-relation is a request and answer process between an individual (person) asking for the solution of a problem related to its health. In primitive societies this one-on-one relation remains more or less intact. In modern society however, due to the increasing cost of healthcare, this relation becomes embedded into large networks of interrelated stakeholders. Not only does the complexity of the inter-human network increases, but increasingly (un-)intelligent devices are participating in the healthcare network. Only low-tech care (family) stays largely out of this increasingly complex web of interacting individuals and organizations.

Due to the level of complexity the interactions in the healthcare web has reached nowadays, the patient and healthcare worker become hidden under layers of organizational complexity which also become increasingly unmanageable to sustain. The overhead of the system and the resources required to keep the system affordable are reaching their limits in Western countries while in other countries access to healthcare suboptimal. As in modern society there is no way back to the primitive situation, the only way out is to improve the efficiency of the entire process surrounding the patient care-provider relation (e.g. patient, physician, nurse, ...).

The communication network in healthcare consists of patients, healthcare workers and supporting technologies. Each patient is embedded in a social, cultural, economic and technological web of interactions. The same goes for healthcare professionals. Inter-human interaction and communication (verbal and non-verbal) in a healthcare setting has to be efficient and effective, but also humane. A patient is embedded in his or her web as a person, which is present in the healthcare setting. Healthcare communication is more than showing on a smartphone display to a patient with diabetes mellitus (DM) that his or her Hemoglobine A1c (HbA1c) is out of control. Conveying information between people has to take into account their social, cultural, and economic background. Without dealing with semantics and semiotics, we lose important information. The meaning of information does not exist in isolation from its sociocultural and personal context. Connections between fragments of information are not in the bits and bytes that encode the signs but in the minds of the people who interpret them (J.F. Sowa, 2000). A reductionist approach would reduce (financialize) the interaction to an exchange of a monetizable product with (only) monetary value, but this would destroy the intrinsic value of the interaction for both the patient and the healthcare worker. Communication and interactions consist of a system of levels, both vertical (sign, syntax, ontology) as well as horizontal (extent). Human-computer interaction has to be integrated and embedded into a hybrid web of communication and interactions enabling shared cognition and communication. When (un)intelligent machines participate in this web of interactions and communication we have to be aware of the vertical and horizontal deficiencies with regard to the fullness of interpersonal communication. Their digital representation of a situation is deficient with regard to the fullness of inter-human communication. Misalignment of analog (human) and digital communication reduces the efficiency and effectiveness of communication and interaction. Man-to-Man, Man-to-Machine, Machine-to-Man, and Machine-to-Machine (M2M) communication each have their specifics modes, strengths and weaknesses which have to be taken into account when conceiving, designing and developing the modern hybrid analog-digital healthcare ecosystem. In this context, full-stack development means the equivalency of the backend and frontend in semantic interaction. While the backend may interact directly with other digital systems, the meaning (semantics) of the information should be equivalent to the information presented and received from humans. Full-stack development in this context means full-meaning development, not only the grammar and syntax
(See also Computational semiotics and Actor-network theory).

Figure 3: Allometric engineering (process hybridisation) would allow for breaking the tight covariance among components of a process
by altering the variance of the process components relative to each other. The green curve represents the high performance process.

Healthcare, like any human activity, can be seen as a process that is defined by a set of transformations of input elements into output elements with specific properties, with the transformations embedded in a web of interactions, characterized by parameters and constraints. The primary healthcare process is still, to a certain extent, paper-based and limited by the capacity of such an approach. This approach is a zero-order process with no intelligence built in the process-data carrier (paper) itself. A first-order digitized process is more or less a dumb equivalent of a paper-based process as the digital support is limited to shuffling around digital documents with unstructured data inaccessible to digital processing of their content. A second-order process uses structured data (syntax) should allow for messages to be exchanged, but the digital process participants have no clue about the meaning (semantics) of the data. A third-order process conveys meaning (ontology) back and forth between the process's human and digital participants. In a hybrid analog-digital healthcare ecosystem or hybrid web, man and machine share a common ontology which allows both to process information in a shared and meaningful way. Man-to-Man and Machine-to-Machine communication at an ontological level can act as mirror processes that are mutually supportive. By creating a digital twin of healthcare process participants, which is capable of participating in a digital replica of the healthcare process, we can take some process burden from our patients, physicians, and nurses. A digital twin could interact with medical cyber-physical systems (MCPS) (I. Lee, 2010; I. Lee, 2011). The cost of replicating the healthcare process should be compensated by the gain in processing capacity and quality compared to an analog process. The digital twin of a patient could (should) range from an ontological replica to a virtual physiological human. Each level of representation can participate in certain aspects of the healthcare process, thereby creating a multilevel integrated process. The decisive element is the added value of the digital twin as an active participant in an integrated hybrid analog-digital healthcare process (e.g. Digital Twins in healthcare and K. Bruynseels, 2018). The (third-order) digital participants allow for extended and expanded cognition due to their capability to deal with the information's meaning. This third-order process breaks the boundaries of the efficiency and effectiveness of the entire process. This step resembles allometric engineering by breaking the tight covariance among components of a process by altering the variance of its components relative to another. The process's performance optimum would shift upwards and to the right, meaning it would have an increased capacity, and quality would only start to decline at a higher process volume (Figure 3). The digital process components are capable of establishing a new equilibrium of efficiency and effectiveness for the entire healthcare ecosystem in a redesigned process. Process hybridization would allow for dealing with the Baumol effect and cognitive capacity limitations in healthcare. It is not just a matter of introducing isolated islands of (incompatible) technology, but hybridizing the healthcare process into an analog-digital integrated process web. Creating a balkanized eco-unsystem of incompatible standards and systems will not improve overall process performance (T. Panch, 2019). Process myopia (shortsightedness) and a lack of understanding of the overall process-web requirements is an almost insurmountable barrier to healthcare-web performance improvement. Last but not least, legal and ethical considerations should guide the introduction of these systems in healthcare (K. Bruynseels, 2018). The commodification of patient data into digital twins for commercial exploitation is not the way to go. Due to the vulnerability of our patients and healthcare workers for ruthless commercial exploitation of their data for commercial profit, the safety, security, privacy, and confidentiality of their data has to be guarded and protected. Ownership of the data should stay with the person from whom the data are extracted.
(See also What is the ontological meaning of digital twin and how it came into existence and Improving healthcare using medical digital twin technology and DigiTwins and Project Baseline).

In order to reduce and avoid balkanization of the healthcare system, there should be no system, process or communication (data) silos. Syntactic (data formats/communication protocols) and semantic interoperability should be flawless. The healthcare ecosystem shares clinical information models (structure), terminology or concept models (meaning, semantics), and inference models/Problem Solving Methods (PSMs) (consequences and actions) for interoperability (A.R. Mori, 1998; A.L. Rector, 2001). Problem Solving Methods include algorithms, heuristics and root cause analysis. Clinical Information Models (CIMs, templates or clinical archetypes) enable semantic interoperability (A.L. Rector, 1991; A.L. Rector, 1993). Clinical archetypes represent a formal statement of agreed consensus on best practice when recording clinical data structures (A. Tapuria, 2013). CIMs define both the information structure and formal semantics of documented clinical concepts (A. Moreno-Conde, 2015). They facilitate organizing, storing, querying, and displaying clinical data; exchanging that data between different information systems; and performing data analytics. CIMs are defined in the form of archetypes, which define how data should be structured in order to be seamlessly stored in or transferred between systems. Archetypes are the fundamental shareable specifications of clinical information (D. Wollersheim, 2009). ISO 18308 deals with EHR information architectures and ISO 13606 defines interoperability standards (A. Tapuria, 2013). ISO 13606 (part 2) defines how archetypes should be formally represented for interoperability. ISO 13606 (part 2) follows a Dual Model architecture, combining a Reference Model (to represent data instances) with an Archetype Model (to semantically describe those data). The two-level (Dual Model) architecture separates technical concerns from clinical (S. Garde, 2007). ISO 13972 specifies clinical information models (CIMs)
(See also eHealth standards (CEN/TC 251, EU) and ISO/TC 215 (Health informatics) and Electronic Medical Record Adoption Model (HIMSS, EMRAM) and Reference Model of Open Distributed Processing (RM-ODP) and Archetype Modeling Language (AML) and Archetypes and Templates (openEHR) and Archetype Definition Language 2 (ADL2) and Archetype Object Model 2 (AOM2)).

We need to capture the meaning (semantics) of the patient-condition and the process web, not a mere pile of data-streams. Shuffling around paper or unstructured piles of passive and incompatible data does not add cognitive capacity to the process web or matrix. In order to be able to improve the support capacity of systems to manage complexity of modern healthcare, we need a process ontology as a model of the structure of the healthcare universe (Scheuermann, 2009). An ontology is a kind of controlled vocabulary of well defined terms with specified relationships between those terms, capable of interpretation by both humans and computers. This healthcare ontology is itself part of a larger set of ontologies, which provide us with a structural design pattern, out of which empirical phenomena can be explained and put together consistently in order to manage the healthcare process itself (like a set of matryoshka dolls). The (healthcare) process ontologies have to provide us with a description of the components and their relationships that make up a healthcare process, e.g.a Process Specification Language (PSL). Modern healthcare is not a linear unidirectional process, but a web of interactions between analog an digital actors, so the process ontology should be capable to deal with an hybrid, web-like process
(See also Ontologies in Medicine, Domenico M. Pisanelli, IOS Press, 2004 and Medical Ontology: Approaches to the Metaphysics of Medicine, Jeremy Rosenbaum Simon, New York University, Graduate School of Arts and Science, 2006).

A framework is required for representing information about healthcare resources in a machine readable way which can be shared in a hybrid analog-digital network of healthcare components (human and non-human, facility and device). The framework consists of symbols which denote concepts (hospital, ER, bed, physician, nurse, ...) and their roles (role filler, cure, care, ...), which express relationships between concepts (Nardi, 2003). A Resource Description Framework (RDF) allows for machine processing and management of healthcare resources. The RDF creates a graph of healthcare resources and their capability (processing quality) and capacity (processing quantity) in space and time. A healthcare ontology will have to define the capabilities of each healthcare actor (object) at different levels of an organizational hierarchy (hospital, department, ER, OR, physician, surgeon, general practitioner, nurse, devices, ....). These healthcare objects create a semantic ecosystem, which can be assembled into a virtual organization. A 'general surgeon' (class) 'is a' (relation) subtype or subclass of 'surgeon' (hyponym-hypernym relation, inheritance of properties, subsumption) with certain 'attributes' and their values (capabilities, properties, features, characteristics). These relations have the structure of copular sentences (sentences of the form A is B) or a subject, copula (is), predicate (a) structure. In informatics they use classes and their relations (inheritance). Each object (subject or class) 'has' (implies the meaning of possession) additional properties or attributes. The relation between the objects (subject, class) of the terminology, together with their attributes, and attribute values define certain meanings (semantics). The subject-copula-predicate structure is a characteristic of Indo-European Languages and it defines the way we see and classify reality. In this way we get an entity-attribute-value model (EAV). A 'general surgeon' (entity) can do surgery (attribute) on a 'gallbladder' (value), while a 'urologist' can do surgery on a 'prostate' (attribute value restriction). A procedural (biomedical) ontology allows for dealing with the characteristics of procedures, such as a 'cholecystectomy'. Combining the characteristics (semantics) of a procedure (resources required) with the capability (semantics) and capacity of the available resources allows for demand-supply management. The is-a-subclass-of relationships creates a taxonomy (classification) or tree-like structure that clearly depicts how healthcare objects relate to one another and branching out from general to specific attributes and capabilities. The healthcare taxonomy resembles a taxonomic ranking (e.g. Healthcare Provider Taxonomy Code Set). The concept of capability as an attribute is a fundamental element not only for a Healthcare Service Oriented Architecture (HSOA), but also for Healthcare Enterprise Information Systems (HEIS) and Resource Management Systems (RMS). When a 'cholecystectomy' has to be performed, the system manages and assembles the required resources in space and time, both human and non-human (building, devices), based on their capacity and capability (semantics-based resource management). The healthcare taxonomy allows for resource management systems to operate on the available resources, e.g. hospitals, healthcare professionals. A healthcare ontology allows for expressing healthcare data in ways that can be read by computers. As such it will create a topology of the healthcare system which can be fed into a network of local, regional, or national (and beyond) IT and AI systems capable to support the process flow of the healthcare system (request versus capability and capacity). One of the components of such a system is a service and profile ontology management system, something like a 'Ontology Language for Healthcare Services' (OL-HS) similar to a OWL Web Ontology Language for Services (OWL-S) and a set of linked NoSQL databases, each semantically linked to the analog-digital healthcare web and capable of semantic queries. The ontology consists of a human readable and machine readable component, which allows for analog-digital interaction and process management.
(See also Educational Publications about Ontology Basics, V. Dimitrieski et al, 2016 and Jiangbo Dang et al, 2008 and WHO Family of International Classifications (WHO-FIC) and SNOMED CT and SNOMED CT OWL Guide and International Standard Classification of Occupations (ISCO) and Global Medical Device Nomenclature (GMDN) and ISO Standards for healthcare, wellbeing and safety (ISO) and Ontoserver (CSIRO)).

The ExR universe

Figure 4: Managing digital relations.

Figure 5: Questions and answers in the digital world.

In a patient-physician encounter, the patient explains his symptoms to the physician but the information is also transmitted to the virtual digital companion of the physician which tells its real world counterpart what the anamnesis could suggest. It is crucial to understand the importance of a high quality patient history for a diagnosis (J. R. Hampton, 1975; G. Sandler, 1980; J.P. Kassirer, 1983; M.C. Peterson, 1992; E.P Balogh, 2015, Ch. 2; C. Rajasoorya, 2016). You don't learn the necessary skills by staring at a smartphone or a computer screen (K. E. Keifenheim, 2015). Clinical and technical exams can't compensate for a sloppy patient history (Sloppy and Paste). The digital companion does not compensate for clinical sloppiness (and vice versa). The physician performs its clinical and technical exams and its digital counterpart acts as its agent in the digital world to manage the interactions with the radiologist and the lab, etc. (clinical process and logistics support). During the entire process an intelligent clinical decision support system (CDSS) works together with the physician creating a shared cognitive environment. They work together as 'master craftsmen', highly skilled and experts in their 'profession', not as unskilled workers in an industrial process. A stethoscope or any other device not only transmits the information to a physician, but also to his digital companion for assistance - the Web of Things (WoT) and the Web of Ontologies interconnect to the web of persons (healthcare professionals). The analog and digital participants do not "think" in documents, but in processable information entities.

Part of the solution of the healthcare quagmire is to reinvent the entire process flow, with the goal to make it both perform more efficient (reduce cost) and more effective (reaching the right goal). This will require us to re-engineer the process and make it into a sustainable, lean, efficient and flexible system which is capable to deliver performance at the right place, time and person. Anything including the relation between care taker and provider should be optimized dramatically to reduce the overhead of the entire process and to refocus on the essential relation between patient and care provider (physician, nurse, ..). Simply converting the paper-based or digitized but siloed process to a networked workflow creates even more overhead and inconsistencies. Without process redesign, digitization does not work and even makes matters worse. Rethinking synchronous and asynchronous processing and offloading process steps with low added value from physicians and nurses to enable more patient oriented and high value added activity for those who can provide high added value.

It may be better to think about the new process as a digital universe which needs to be shaped according to its own laws and inhabitants. An E(P)HR (electronic (personal) health record) is such an inhabitant. A personal health record (PHR), is a health record where health data and information related to the care of a patient is owned and maintained by the patient. PHRs are not the same as electronic health records (EHRs), which are software systems designed for use by health care providers. An EHR can be linked to a PHR in order to exchange vital information, for instance at the moment of an Emergency Room (ER) visit in a life-threatening situation. Electronic medical records (EMRs) are a digital version of the paper charts in the clinician's office. An EHR focuses on the total health of the patient-going beyond standard clinical data collected in the provider's office and inclusive of a broader view on a patient's care. EHRs are designed to reach out beyond the health organization that originally collects and compiles the information. The information in the EHR follows the patient and therefore it is crucial to avoid geographic-, organizational-, semantic and vendor lock-in. Creating boundaries for life-saving exchange of information is not acceptable. A system should be built upon semantically intraoperable (internal clinical EHR datamodels) and interoperable electronic health record systems (e.g. ISO 13606, openEHR, HL7 Clinical Document Architecture (CDA)). In this context intraoperability does not mean proprietary protocols and data formats of one particular vendor, but an architecture which allows for processing of the semantics (terminology artefacts) required to capture the meaning of information. A software- and database-architecture which has to process semantics cannot be built from a semantics-unaware principle.

The personal health record (PHR) will over time evolve into a more complete representation of a person and become an ExR or ELR (Electronic Life Record) in itself capable of interacting with other systems or inhabitants of the digital universe. Safety, security, encryption, interoperability and identity management are crucial if this will ever become reality. Safety and security of any health record should be based on open, non-proprietary standards, which are verified and validated. The interaction between the analog an digital world creates a multi-verse of agents interacting across the analog-digital divide. A universe of bots connected with the internet of things (IoT) mirrors the physical universe of people and resources. Creating the hybrid analog-digital process then becomes equivalent to creating an interrelated ExR world with interrelated cause and effect relations. Medicine is usually understood as an applied science and the results of applied medical research are typically technical norms. Medical practice is relative to human goals and interests and the results of medical research concern facts about the relations between means and ends (causation). The epistemology of medicine is an epistemology of practice (τέχνη). Medical data and information processing in a hybrid human-machine environment requires a clear understanding of the epistemology and ontology underlying medicine. The semantic web is a step in this direction and it is part of the philosophical tradition of developing symbolic systems to represent knowledge. Creating the (medical) ontology underlying the (medical) semantic web depends on philosophical principles and the construction of the ontology reveals the underlying philosophical principles. Does the ontology describe conceptualizations or linguistic phenomena versus real entities? Are medical ontologies to be based on the principles of analytical ontology? (Cimino 1998, 2006 and Smith & Ceusters, 2003 and Rodgers, 2006 and Tomasi, 2016 and Semantic Conceptions of Information and Organising Knowledge in the Age of the Semantic Web, Liam Magee, RMIT University, 2010).

Each participant of the process, represented by its Electronic x Record or virtual persona (bot) becomes connected to the other stakeholders as in the physical world. The way they interact of course will be different and the interaction process differs (no paper shuffling equivalent). Some of the stakeholders in this flow may have no real world equivalent, but only act as virtual intermediates. Due to the internet, they can interact with the speed of light, so distances are no longer an issue. There should be a dynamic balance between the physical and virtual world and a topological map between both representations of the healthcare continuum. The inhabitants of this digital world, will be linked to real-world counterparts in the real world. The interfaces between this digital world become windows through which we interact to create a mesh of flows which run both in our physical world and in the digital world (a PC with a keyboard is such a window, albeit primitive). The internet of things is one such step towards this digital parallel world which becomes an internet of relations. The boundaries between the digital and physical world will become transparent and process flow will run back and forth through the physical analog and the digital world (e.g. 3D printing and robot surgery). The way we interface with the digital world needs a big improvement in both usability and capacity as today we are still not capable to interact in a way natural enough to allow for the required ease of use.

Figure 6: An ExR memory and digital equivalent creator.
An ExR becomes a supporting memory for us and from which a digital dataset can be reconstructed
representing our health status and history.

A patient his or her ExR will be part of the "database record" representing complex and multiple aspects of a person in the digital environment, with healthcare-related information (CT, NMR, lab, ...) part of this digital persona (an advanced database system with sockets linking it to its surroundings).

Each ExR acts like an entity which itself belongs to multiple types (situational types) and its relationships present themselves as roles. Ideally, the digital representation should accompany each individual throughout its life and provide a "memory" for all healthcare related events (live events would be a more general description). The internet becomes connected with the human net and physical and digital reality become intertwined through a rich back and forth transformation of analog and digital information through interfaces capable to transform complex information, not just multimedia as we have today buth true polymedia (more than text, sound and images). So we provide our digital mirror image with a rich representation to work with.

As a personal assistant the ExR also acts like the thread of Ariadne within the labyrinth of healthcare. It can signal the need for medical care either on-demand or triggered by smart devices (e.g. telemonitoring) and guide the patient and the care provider(s) towards each other when necessary. The care provider with the right skill profile will be contacted and guided towards the patient in case of an emergency. Wherever we go and wherever we are, nobody has to worry about finding appropriate care, the ExR will take care of that within the boundaries the patient allows it.

During a medical contact or intervention a physician or (his) assistant digital care-system could "ask" complex questions to the digital representation, e.g. in case of an emergency. The willingness of the ExR to share information will depend on the trust it has to its digital counterpart. Our digital ID becomes the key to our digital companion and the secure link through which interfaces can connect. A digital ID may in the end resemble a sort of xPod which we carry around and plug into the digital web when we want to identify ourselves and interact digitally. At that moment our digital persona is activated and ready to go (as a genie coming out of a lamp). A patient going through a CT-scan gets the result attached to its digital representation, although the data can stay at a server in the hospital or any secure vault. Whenever a physician needs the data, the virtual companion remembers the location and acts as an intermediary to ask for the data and provides them to the physician through its digital companion. The patient-centered healthcare system becomes a virtual reality. The data are the equivalent systems representing ourselves in-silico and the infrastructure nourishes and connects the data.

Unified Process Management

Figure 6: Healthcare process management system at work.

Unified healthcare process management and control requires an integrated approach to design and develop a hybrid healthcare process, which enables man and machine to work together in an efficient (process) and effective (outcome) process-flow (Business Process Management Body of Knowledge or BPM CBOK®). Point-of-care activities are connected using physical and (intelligent) digital transfer operations. Meaningful process data support process management and operational efficiency and effectiveness. Operational process and data chaos can never be solved at a tactical and strategic level. Keeping operations digitally unstructured and un-meaningful is a cause of unsustainable loss of operational efficiency and effectivity. An operational process and data chaos cannot be compensated using a Common Data Model such as the OMOP Common Data Model of the OHDSI. Creating only off-line and non-operational analytical value does not compensate for the destruction of operational value due to operational process and data chaos. Healthcare process modeling and healthcare process mining, in order to analyse, understand and improve operational healthcare processes, is needed. Checkpoints are to be built into the operational process itself to prevent process anomalies (risk mitigation). The integrated analog-digital system actively monitors risks and prevents issues during the entire healthcare process. A risk-based process design decides upon the type and granularity of checkpoints (e.g. accreditation) and capabilities (man, instrument, and machine) required at each point-of-care and during each transfer. Man and machine have to be integrated into a hybrid process, which takes into account and deals with their strengths and weaknesses. Patchwork processes and stopgap or band-aid solutions do not contribute to overall process efficiency and effectiveness improvement. In addition, they destroy time that could be spent with patients and colleagues. Unmanaged change or pinpoint optimization of a process makes things worse, as it creates process inconsistencies
(See also The process of inconsistency management: a framework for understanding and Detecting Inconsistencies Between Process Models and Textual Descriptions).

Unified process management requires unified process analysis, design and deployment. Integrating digital systems into the healthcare process requires subject-matter expertise of the operational, tactical and strategic levels of healthcare. In order to improve the overall process, not only the monetary return on investment (RoI) with regard to the capital invested should be taken into account. The return on effort (RoE) and the return on time (RoT) at the operational, tactical and strategic level have to be differentiated and taken into account. Extramural, intramural and transmural process scope, time, budget and quality have to be taken into account. A positive ROI at the strategic (overall, capital invested) level which is destructive (negative) at the operational level, destroys the net effect of physicians and nurses to be of value for their patients. An extramural, intramural or transmural positive RoI which is destructive (negative) at the other locations has to be dealt with. The transfer of patient care to the social network of a patient (caregiver) may be cost effective with regard to healthcare system spending, but can have a profound impact on the quality of life of both the patient and his or her social network, which has to be taken into account (caregiver stress and social isolation). A high monetary RoI can have a profound negative impact on the Return on Relationships (RoR) due to the negative impact on human relations, communication and well-being of healthcare workers, patients and caregivers. A RoR (relational capital) has to deal with both the number of relationships (horizontal, quantity) and their quality (vertical, depth, trust). The effect on scope, time, budget and quality has to be differentiated at each level and position in the overall process (RoI, RoE, RoT, RoR, extramural, intramural and transmural, operational, tactical and strategic). Effectivity and efficiency lost at the operational level cannot be compensated at the tactical and strategic levels. The promise of future gains with present operational losses has to be taken care of and carefully analysed, scheduled, monitored, managed and validated. An undifferentiated and high-level analysis of the RoI is part of the 'chrysohedonistic illusion'. Chrysohedonism is a misconception that wealth (only) subsists in gold or other forms of money. Undue focus is given to that which can easily but only partially be quantified in monetary terms (valuation restriction). The attempt to monetize the digitization of healthcare only in terms of monetary value creates the (misleading) perception that the negative impact on operational performance (efficiency and effectiveness, RoE, RoT, RoR) is modest, because it has only been possible to assign a quantitative or monetary value to the capital invested (RoI) at an insufficient level of detail. The negative impact of unergonomic systems which leads to (unmonitored) workarounds, shortcuts or desire paths is not taken into account in the total cost of ownership (TCO). It is therefore essential that (strategic) decision makers do not use the monetized numbers unquestioningly, do recognise the uncertainties associated with each, and do understand the assumptions behind them (European Commission DG Environment, 2018, p. 6) (See also chrysohédonisme).

The transformation from paper-based process into a data-based (digital) process requires redesign, redevelopment and extensive change management and training in order to succeed. Balancing analog and digital process steps requires a deep understanding of the healthcare process, rules and regulations. Electronic health records should not just replicate dumb paper charts and paper-based processes, otherwise they will stay as dumb as paper. An EHR-based system should also be patient centered as the data and the process has to follow the patient through the entire process and across the boundaries of health care organizations. The paper-based healthcare ecosystem lacks data analysis and management capabilities and stumbles around blindly, wasting resources and harming patients. In order to make the transition from a passive paper based process to an active digitized process, one should first and foremost analyze and model the work-flow according to best practices, because automating a broken paper based process will only get you an automated broken process in which workarounds become much harder. Compensating bad process design and implementation (unnecessary complexity hidden in the value chain) with a surplus of workforce and middle-management increasingly leads to even more loss of efficiency and effectiveness. Process inefficiency is a hidden cost and constitutes a hidden factory (cost of poor quality). Armand Feigenbaum estimated the endeavor within the hidden factory might be 15% to 40% of total company effort and healthcare organizations are no different. Failure of profit & loss indicators to take into account the cost of the hidden factory is a common problem in process monitoring systems. Patients and healthcare workers suffer from process inefficiency and ineffectiveness. Investment in efficiency and effectiveness by means of process improvement and automation only happens when the cost of inefficiency and ineffectiveness becomes visible and can no longer be ignored (process P & L transparency). A common mistake also happens when organizations believe they are unique and should do things in a special way and waste time and resources to create a Byzantine complexity instead of implementing best practices across industries or applying Occam's razor when redesigning processes. Inefficient and ineffective process design leads to the (implicit, hidden) development of (unmonitored) workarounds, shortcuts or desire paths (M.E. Flanagan, 2013). Inefficient and ineffective process design and implementation are a cause of operational 'bricolage' (S. Y. Teoh, 2012). A 'bricoleur' is a resource person enlisted when conventional procedures in daily life fail to work, and who utilizes whatever is at hand in the given situation to effect a solution (J. Smedslund, 2012). Another pitfall is personalization "ad absurdum" or hyper-personalization, without taking into account the potential destructive effect on the overall process beyond the boundaries of individualized optimization (unbalanced design). The negative effects of process design flaws can be ignored during design and development, as operational performance is invisible due to process simulation deficiencies and ignorance. Process flexibility towards patient-profiles requires flexibility in process management and control, which is not the same as unmanaged and uncontrolled hyper-personalization. Hyper-personalization in order to increase (healthcare) provider satisfaction, while ignoring the negative effects on patient care is a common problem in (healthcare) process design, development and implementation. Balancing process design, development and implementation requires looking at a system from different directions and dimensions, and back-propagating outcome into development and design (evolutionary design strategy by means of outcome-driven design). Reducing the demand of safety-related process steps will cause downstream quality problems, which cannot be remedied due to the fact that a critical step has been obliterated due to process design, development and implementation failure. The cost of poor quality can be categorized as internal failure costs or external failure costs. Internal failure costs are incurred when poor-quality healthcare process step results emerge before care is being provided to the patient. External failure costs are incurred after the patient has received a poor-quality service and are primarily related to the clinical point of care. Piling up errors before the clinical point of care is due to flaws in process monitoring and control or a lack of evidence-based process measure design and implementation (process measures, balance measures, outcome measures). The Total Cost of Ownership (TCO) does not always take into account the externalized cost of process failure due to low quality process design, development and implementation.
(See also Tools and Strategies for Quality Improvement and Patient Safety and Crossing the Quality Chasm: A New Health System for the 21st Century and Battles JB, 2006 and Buchert AR, 2016 and Gaw M, 2018).

Process design and development has to take into account the flow of information attached to the flow of patients (demand), healthcare workers, supplies and other resources (capacity/quantity and capability/quality). Process quality and safety is not about 'checklists', but about professionalism and a safety culture (the 'checklist' is commonly blamed, but it is the culture which is lacking). In the USA alone, an estimated 100,000 patients succumb to preventable medical errors or infections every year (S. Gordon, 2020). A process which encourages teamwork or Crew Resource Management (CRM) and promotes common patient safety knowledge and skills, would improve health care safety (S. Gordon, 2020). Designing a realistic process flow which reflects medical and clinical reality is only one aspect of process design and development. Process design and development suffers from Kafkian process design, when armchair designers create processes which reflect their administrative and bureaucratic dreams, but lack a close relation to work-floor reality. Failure to distinguish between operational, tactical and strategic process levels causes inter-level problems. Deciding on the location and process-flow-impact of process checkpoints and the decidability of information or the "actability" of information is even more important. Decidability is related to the information content of the data which is being presented to the human participant or the intelligent machine and which allows for taking a decision at the right place by the right process participant (process monitoring). The "actability" is related to the capacity of the information to get an action started or process-flow changed (alternative routing), which is the capability to control the execution a process flow (process control). A sloppy paper based medical record (PBMR) filled with illegible handwritten notes reduces the pressure on the physician or nurse filling the medical record, but increases the risk for downstream Potentially Preventable Complications (PPC) and Never Events (NE). People should be aware that they register data related to process monitoring and control, not a story (it is not about the 'checklist'). The paper based medical record (PBMR) is just another way of registering an representing information, just like a keyboard and a computer screen. The medium, paper (analog) or computer (digital), is the way the message is being registered and presented, but it is not the data or the underlying concept itself. Paper itself has no processing capacity, compared to a digital system which can decide and act based upon the stored data. A digitized process has an increased processing capacity compared to a paper based process. As such a well-designed and implemented digital process extends and expands our workforce. The essence however, is the quality of the overall process. Sloppiness with regard to safety in the transfer of critical information causes unnecessary morbidity and mortality. The To Err is Human report (1999 CE) by the Institute of Medicine (IoM), stated that medical errors caused at least an estimated 44,000 preventable deaths annually in the United States of America alone, of which 7,000 deaths are attributable to sloppy handwriting. An EPR which forces manual entry and/or replication of all kinds of superfluous administrative data in-line with the healthcare process (synchronous) instead of gathering this additional data asynchronous reduces the value of the system for healthcare workers. Pushing administrative data entry towards nurses and physicians wastes costly time and value of healthcare workers who have to focus on the patient and the medical content and process itself. Forcing physician and nurses to fill all kinds of administrative data which should be done by a cheaper administrative workforce causes havoc and burnout. Process flow and data management should be related to process outcome (positive impact) and the potential to harm the patient due to skipping a process checkpoint (negative impact, e.g. PPC and NE). Downstream outcome impact and potential harm should decide on upstream impact of forcing control of a process step (process step impact analysis, failure mode and effects analysis (FMEA)). Process design should take into account the Swiss cheese model in order to increase safety and reduce the impact of single checkpoint failure. Quality control (QC) procedures and systems should be operational at all times, as without them quality deteriorates, which became evident during surprise quality inspections by the Joint Commission (TJC) (Barnett, ML, 2017 and Hawthorne effect). Hospital organizational culture and human behavior is also associated with patient outcomes, which has to be taken into account in process design (Baggs JG , 1999 and Singer S, 2009). Alternating human and digital checkpoints could compensate for the hospital culture problem where nurses are being bullied by physicians and do not dare to speak up to them in case of a process problem. Personal and disease history data and medication should be transferred between electronic record systems (intramural, transmural) without the need to copy/paste such information. Medication reconciliation should ensure accurate and complete medication information transfer at interfaces of care (J.D. Wong, 2008). Nurses and physicians are to be presented context sensitive, decidable and "actable" data and the administrative pressure should be related to the impact of the data on the process, when data entry is to be synchronous with the in-line process itself. We should be aware that people who create sloppy paper based medical records (PBMR) filled with illegible handwriting may not be able to create flawless medical records and high process quality, even when equipped with the most advanced EHR system: "A fool with a tool is still a fool". There are limits on the achievable level of quality care within a given national, regional, local, extramural and intramural process design and implementation, available people (skills, attitude) and tooling (analog, digital) (Systematic design of healthcare processes and process architecture and process modeling and manufacturing process management and What is medication meconciliation?).

Process performance improvement not only requires flashy new tools and technology (the easy part), but also aligning changes in behavior and practices across multiple levels and areas of the healthcare ecosystem (the hard part). The performance of people remains central to the performance of many healthcare systems and organizations. Human performance modeling and human performance variation and human reliability have to be taken into account when designing a process, just as the performance, variability and reliability of non-human process participants. A process with 99% accuracy at each step, will only perform at 90% after 10 process steps and at 69 process steps it drops below 50% overall. There are a lot of hospitals that have implemented complex (digital) tools in hopes of solving healthcare process problems, only to discover that the root cause of the problem ends up being process and not tool related. Process redesign should reduce the human and (un-)intelligent machine potential for causing damage, while at the same time improving performance and outcome (e.g. fault-tolerant design and high availability). Regretfully relatively little effort has been put into the fundamental redesign from paper-based to digital processes towards intelligent outcome management and risk-driven process safety. A hybrid analog-digital process creates a triadic structure in which digital actants interfere with the traditional dyadic relation between patient and healthcare worker. We have to be aware of algorithmic containment, where the digital actant (computer algorithm) effectively contains the human actors in a process which is not aligned with the actual healthcare process. Moral and legal liability in a hybrid process are an important aspect of process design, development, monitoring an control (M. Hildebrandt, 2008)
(See also D. Scott, 1996 and C. O'Neil, 2017 and A Theory of Persuasive Computer Algorithms for Rhetorical Code Studies and ambient intelligence and pervasive intelligence and A fool with a tool is still a fool and Ways To Approach the Quality Improvement Process).

An important issue is a coordinated development of business process and the practices and tools in order to achieve the goals and targets of an organization. Just buying more software and dumping data into "the cloud" or into data warehouses does nothing to improve overall healthcare process performance. Information availability does not equate to usability, and databases filled with electronic versions of paper document structures as such do not improve health outcomes or organizational performance. The limitations of the data carrier (paper, legacy database, storage systems, dumb EHT) in many organizations determines the capabilities of business processes and limits the capabilities of an organization. Feature rich technology ("gadgetism" and "featuritis") itself does not equal business capability development. An enterprise architecture (EA) approach could bring all components of the organization in line with its real needs and requirement and guide them through the business, information, process, and technology transformations necessary to execute a coordinated transition from paper based (incl. dumb EHR) towards an integrated process, combining and amplifying the capabilities of all resources (alignment of resources in the organizational ecosystem). Processes can be supported by enterprise information management (EIM), healthcare orientend business process management (BPM), workflow management, business intelligence (BI) and a decision support system. Enterprise information management would structure and align all business and healthcare processes, data-systems and analytics systems to drive better healthcare outcomes. An active digitized process will enable process mining (Automated Business Process Discovery), which allows for developing and translating insights into process optimization and actions. This will improve health care outcomes for patients and reduce risks
(See also Role of enterprise architecture in healthcare organizations and knowledge-based medical diagnosis system and Healthcare Forum).

Taking a problem oriented approach, which is a structured organization of patient information per presented medical problem, allows for feeding structured data into a digital system (Weed, 1964, 1968; Simons, 2016). The patient and his problem(s) can be transformed into an Object-oriented (OO) system, in which a patient and his data become an object and its attributes. Based on these principles, a Problem Oriented Medical Record (POMR) can be regarded as an Object Oriented (OO) data system, which attaches healthcare problems (attributes) to the patient (object). It creates a concatenation of objects and attributes, which themselves become objects in an expanding and collapsing data-structure upon which can be acted (e.g. the principle of instantiation and inheritance). Feeding Subjective, Objective, Assessment, and Plan (SOAP) information into an intelligent system (creating a SOAP-E(xecute) process) instead of putting them to sleep on paper or a dumb EHR, would benefit both the clinician, the nurse and the patient. Transforming patient information into a structured representation, upon which can be acted is an important step in the process to create a digital representation of the patient and his health situation.

Each time a patient enters into a health care relation with an health care provider (point of care location), an intelligent system subscribes to the events associated with the patient and each event is being published to an event handler (publish and subscribe mechanism). The Subjective, Objective, Assessment, and Plan (SOAP) information of the Problem Oriented Medical Record (POMR) is the core element of an event-driven system. The system captures subjective (S) and objective (O) information, either through structured SOAP notes by the clinician and/or nurse or objective data (O) directly from sensors attached to the patient. The objective data (O) captured by the system are also presented to the clinician and/or nurse in a structured and human readable way (context sensitive and avoiding 'triggeritis' or 'alert overflow'). Of course this approach can be used in an extramural setting also (telemedicine). The subjective (S) and objective (O) information (event) create a condition (object) which is being assessed (A) after which an action is planned (P) and executed, by the clinician and/or nurse in collaboration with an intelligent system which monitors and manages the process 24/7 (Clinical Decision Support System (CDSS) and Enterprise Resource Planning (ERP)). The actions of the clinician and/or nurse are also being fed into the system as events (E), which trigger a condition and an action by the system when required (event condition action (ECA)).

A process has to run through analog and digital steps as easy as possible, while in the analog world staying attached to its digital counterpart and representation and vice versa. Moving information back and forth from an analog representation to its digital equivalent and vice versa can be done by means of a screen and a keyboard or a mobile device or touchscreen, but this is not always an efficient way in order to achieve an efficient process flow. The less intrusive the interface for the analog process the better. We could look at it as marshalling and serialization of information or materializing (physical representation) and dematerializing (in silico, bits and bytes). Marshalling and serialization of information can be done in different ways, which may fit better in a process flow. Marshalling is about getting parameters from here to there, while serialization is about copying structured data to or from a primitive form such as a digital byte stream. In this sense, serialization is one means to perform marshalling, usually implementing pass-by-value semantics. By labeling a patient or a syringe or a prosthesis with a machine readable code or tag (barcode, QR-code, RFID) we "marshal" and materialize the digital object, so its information can be transfered and dealt with in the analog world. A barcode, eID, or QR-code acts like a "usb memory stick", storing the identity of a patient, medication, prosthesis, OR, ER, physician, nurse, ambulance, etc. When the tag remains linked to one data space (EHR software system, ...), the object is "passed by reference", the tag and the object represent the same variable for the parameter. In this case, when working with the tag modifies the parameter variable, the effect is visible to the object's variable in silico. When the tag, such as the eID of the patient, becomes linked to another data space, it is "passed by value", the tag and the object's variable in silico have two independent variables with the same value (data space 1 and data space 2). If working with the tag modifies the parameter variable in data space 2, the effect is not visible to the data space 1. Materializing objects allows for continuing working with the object during analog process steps, which interfere less with the process than each time typing information into a keyboard or swiping over a touchscreen. When both a patient, and for instance his medication are tagged, a simple scan of patient and medication, can take care of linking the act of providing the right medication to the right patient. The same goes for surgery, etc. I have used marshalling, serialization, and passed by reference and value in an unusual way to point to the hybrid use of material and immaterial representation of objects and identifiers to which actions can be attached during an analog process. The same goes for an ontology such as SNOMED CT, with its analog meaning of "appendicitis" and the in silico definition of SCTID "74400008". Handwritten notes of course remain stuck in analog space and no digital process assistance can be provided.

Unified Process Improvement Management - PDCA

Figure 7: Multiscale PDCA cycles iteratively improving a multiscale process.

Process development is not a one-off activity, but an iterative, well-orchestrated and stepwise process itself. The ability to change and improve has to be built into the system itself. Iterative a priori modeling, a posteriori verification and validation and the ability to change the process in a well-managed and controlled way, has to be part of the overall system design. The system should be capable of self-management, self-control, self-improvement and (iterative) process optimization by means of man and intelligent machines. Process improvement always starts with asking the right questions and understanding the problem, not with data or a (favorite) technique ("Cart before the horse"). Identify critical subsystems and analyze how unwanted events and their causes arise and occur (infrastructure, process, outcome, frequency and impact). Depending on the situation and interposition of man in the process, we can either have a short or long, and an open or closed process feedback loop (process monitoring and control, human and machine error and human and machine reliability). Depending on the type of process or problem and the type of (available) data (variables), we could use a different approach, such as artificial intelligence (AI), machine learning (ML), deep learning, or statistical methods (statistical process control (SPC)). Deep learning is a subset of machine learning, which is a subset of artificial intelligence. There are many types of (machine) learning techniques available, such as supervised, unsupervised, semi-supervised, reinforcement (RL) and evolutionary learning (computational learning theory). Supervised machine learning can be split into classification (k-nearest neighbors, logistic regression, ...) and regression (linear regression, ...), while unsupervised machine learning can be split into dimensional reduction (principal component analysis, ...) and clustering (k-means, hierarchical, density, ...). Compared to machine learning methods, statistical models are less successful to hold categorical data, deal with missing values and large complex data sets. A combination of machine learning and statistics could be used, such as statistical learning
(See also Seven management and planning tools and Seven basic tools of quality and Machine Learning in R and Tidymodels and R Interface to 'Keras' and Statistical process control (SPC) and Good Data Analysis (Google)).

Self-improvement and (iterative) process optimization should be built into the system. The system is embedded in an ever improving iterative Deming Cycle operating on multiple organizational levels, based on integrated human- and machine-learning and other artificial intelligence techniques (Figure 7). The Deming Cycle operates at several levels of process integration and balances optimizations at (interconnected) multiscale levels: Point of Care (PoC), hospital, local, regional, national, etc. ... . Healthcare processes are hybrid analog-digital distributed systems, which could be designed, developed and managed by means of activity diagrams (UML, Petri net), etc., embedded into the Healthcare Management System (national, regional), Hospital Management System, and PoC Management System. Systems should allow to analyze, model, and enact processes in a closed loop for iterative improvement of efficiency and effectiveness. Avoid the usual spaghetti and unstructured pile of teams, tools and systems. Data aggregation and disaggregation meet the operational, monitoring, control and management needs at different levels (operational, tactical, strategic). The PoC itself could be intramural and extramural (primary care) of course. The system and process diagram should be both man and machine readable and the diagram is the process monitoring, control and execution system. Process management, control and the digital process execution components should be generated from the diagram itself, including the process measurement and execution components. Full and partial in-silico process simulation and evolutionary optimization should be possible from the diagram itself (Process Simulation & Optimization (PSO) Tool). Process simulation should support balancing analog and digital process steps, based upon process and outcome impact (P&L, cost/benefit, scope, time and budget, internal and external cost of poor quality). Optimizations should avoid unbalanced point-optimization (fragmented point solutions) resulting in global process deterioration as is often the case in self-interest driven optimizations. Patients and healthcare worker needs and capabilities have to be an integral part of the system (empathy, humanness, human factors and ergonomics). Process mining should confront event data (i.e., observed behavior and preformance) with process models (hand-made or discovered automatically). Process-centric approaches have to be integrated with data-centric approaches in order to understand the relation between dynamic behavior in relation to (theoretical) process models. We have to avoid the usual ivory tower syndrome process by closing the loop with the operational bottom (reality, data) of the organization. Strategic decisions have to take into account the everyday implications for patients and healthcare workers (physcian, nurses, ...). A nice powerpoint presentation or spreadsheet is not enough. Over time it should become a dynamic system whose self-organizing behavior is governed by differential equations rather than a sequential, algorithmic process (T. Van Gelder, 1995)
(See also Low quality healthcare is increasing the burden of illness and health costs globally and Eight Levels of the Analytics Adoption Model and The Breakthrough Series).

Unified Project Management

Figure 8a: Unified Project Management. Each care episode becomes a small project.

Figure 8b: Project progress Health status evolution with and without complication.

Unified project management allows for an integrated approach to project management in a hybrid environment with analog and digital project participants. A project is the instantiation of a hybrid process enabling a project flow allowing for a well-balanced analog and digital project monitoring, control and execution (PMCE). A project approach to healthcare allows for pre- and post-calculation of process, and outcome measures or a comparison of 'AS IS' with the desired 'TO BE' status of the patient. It is also important that quality metrics (input, process, outcome) need to be adjusted for underlying patient risk in each project or healthcare providers will avoid caring for high-risk populations ('AS IS' comorbidity, high BMI, smoking, ...). Integrated healthcare project management, goes beyond time and budget as an incomplete view on requirements and deliverables will have a destructive effect on project intake, performance and outcome. Each patient becomes part of a project to be dealt with in an efficient (process) and effective (outcome) way (scope or health outcome, budget, time and quality) (Figure 8a). The project has a scope (target), which is to restore the health status of the patient in the example from 40% to 100% (simplified) (Figure 8b). The intake of the patient is a fact checking step to create the 'AS IS' image, or baseline to start from, of the patient at the beginning of the diagnostic and treatment trajectory (comorbidities, anamnesis, clinical examination, current medication, existing lab results, socio-economic situation, ..). Structured data could be helpful in gathering the necessary data instead of repeatedly having to write everything down on paper (error prone). A patient's 'AS IS' healthcare profile allows for creating predictive analytics that can risk stratify patients. An intake APR-DRG with Severity of Illness (SOI) and Risk of Mortality (ROM), Charlson Comorbidity Index (CCI) or ASA Physical Status Classification, can be regarded as a basic approach to project scope differentiation (health status, disease screening, predict adverse outcomes, predict clinical outcomes) (McCormick PJ, 2018). Efficient and effective capturing of all relevant information required to create a diagnostic and therapeutic plan is mandatory to determine the health status baseline. Creating a diagnostic and therapeutic plan resembles defining the scope ('TO BE') and a work breakdown structure (WBS) with deliverables and (intermediary) milestones (on PM in healthcare see Janet M Payne et al, 2011). After the kickoff the diagnostic and therapeutic project starts, which can proceed as a waterfall or as an iterative and incremental approach. Certain forces will improve the health status of the patient (green arrow pointing upward), while other forces will decrease the health status (red arrow pointing downward). At t=9 an avoidable error happens, which causes the health status of the patient to deteriorate (Figure 8b). Although the problem is solved (t=11), the health status of the patient never reaches the intended scope (red line) compared to the original plan (black line). In project management terminology the event (complication) is an issue, which is something negative to the project that is happening. Before this, it was a risk, which is something that might happen. With an issue happening, the team has to figure out how to resolve it for the project to move forward. Identifying outlier project steps and/or outcomes allows for mitigating self-inflicted risk factors. With an identifiable risk, a healthcare provider (physician, nurse, hospital, ...) should establish mitigation plans that will (hopefully) eliminate the possibility of the risk occurring or reduce the impact if it does occur (risk mitigation, Best Practice Guidelines, Enhanced recovery after surgery(ERAS)). Examples of healthcare associated risks are so-called hospital-acquired conditions (HACs) such as adverse drug events, catheter associated urinary tract infections, patient falls, pressure ulcers, surgical site infection, central line associated infections, venous thrombo-embolism and ventilator associated pneumonia (VAP). Potentially Preventable Complications (PPCs) are harmful events or issues (e.g. accidental laceration during a procedure, improper administration of medication) or negative outcomes (e.g., hospital-acquired pneumonia, C. difficile colitis) that develop after hospital admission and may result from processes of care and treatment. Risk mitigation (risk reduction) is a systematic reduction in the extent of exposure to a risk and/or the likelihood of its occurrence. Once a risk occurs, it becomes an issue, which is what project management and professionals try to avoid. As an example accreditation is based upon risk mitigation (risk reduction) techniques in order to avoid harm to the patient. What would professional project management bring us, inspired by accreditation and supported by intelligent systems? Some thoughts on using project management principles and intelligent systems to support healthcare workers to achieve their goal and to reduce risk exposure for our patients ( Safety and risk management in hospitals).

An intelligent system which never sleeps and doesn't get tired becomes the project manager based upon adaptable diagnostic and therapeutic trajectories and in close operation with its human partners monitors and controls the project phases. Each care episode becomes a (small) project, easily traceable because of its associated data following the patient. Transfers of a patient between healthcare providers or in between departments of hospitals make no difference, as the data are attached to and owned by the patient. The patient and his data are inseparable. Rich data on project status flow continuously in an out the management system. Process and outcome measures are to be used for defining quality and cost targets (mortality, complications, readmission, patient experience, etc.). Project planning, monitoring and control and lessons learned analysis, have to reduce unacceptable healthcare errors, accidents, and injuries and as a result also reduce the per capita cost of healthcare.

Each interaction between healthcare professionals and with patients are regarded as transactions of which the efficiency and effectiveness needs to be guaranteed and therefore monitored (surgery, medication, ...). The health condition of the patient should improve graciously throughout the entire process. The system and its professionals involved in patient care should keep in mind the principle "Primum non nocere" or non-maleficence. The project should evolve towards a positive evolution of the health status of the patient without harm caused by process failure. Healthcare providers should to be in control of the project aimed at improving the health status of their patients and not let the project control them. Decent execution of basic processes such as surgical and other invasive procedures, medication management, etc. should be self evident and not add problems to the patient in need of professional help. Several aspects of a project contribute to failure of success of disease treatment: the level of accuracy of execution (professionalism), efficient and effective organizational procedures, control thresholds (variance thresholds, alerts), units of measure which make sense (speed of change and risk based, ER, OR, ICU), effective rules of performance measurement (earned health value rules), reporting formats which the prime actors understand and acknowledge (multilevel measurement and feedback loops), and finally practical process descriptions which are aligned to reality at the work floor.

Earned Health Management (EHM) becomes possible, because of the traceability of patient data and health status. Dealing with traceable patient data makes possible to evaluate the performance of healthcare with regard to its efficiency and effectiveness. For instance a patient presenting with symptoms such as a dull pain near the navel or the upper abdomen that becomes sharp as it moves to the lower right abdomen (usually the first sign). In addition loss of appetite, nausea and/or vomiting soon after abdominal pain begins, abdominal swelling, fever and the inability to pass gas. The working hypothesis in this case is appendicitis, which sets in motion a diagnostic and therapeutic project. For such a project we should take into account life-cycle costing, where you look at the "total benefit and cost" when a treatment succeeds or fails for the patient, his family, the healthcare provider and for society. In this case for instance the overall cost versus benefit of a laparoscopic versus a traditional appendectomy. Take into account direct costs and indirect costs or the patient, his family the healthcare provider and for society. Cost estimates are a continuing prediction, based on information known at a given point in time and attached to the patient data (scope, time, budget, quality). This becomes more important in complex diagnostic and therapeutic trajectories. The cost estimates are refined during the course of the treatment as additional detail becomes available and should be continued even after the patient has left the care setting or finished treatment (continuous when dealing with a chronic disease). Health value engineering should deal with getting more out of the treatment for the patient and society in every possible way. A health status baseline is made at each transition in the process (should include assumption about direct and indirect costs) and the Diagnostic and Treatment Breakdown Structure (DTBS) is put in place for the next phase in order to achieve the health scope baseline (a DTBS is a Work Breakdown Structure or WBS). Execution of the scheduled DTBS according to plan and appropriate change management along the way gets the patient to the achievable health scope baseline, from "as is" to "to be". The health scope baseline lists the limitations for the period under consideration for expenditure of funds, funding constraints, and diagnostic and therapeutic assumptions (requires qualified professionals and supporting systems). The working hypothesis (differential diagnosis) at the start of the trajectory provides the initial guidance for diagnosis and treatment, e.g. appendicitis based on symptoms and signs. A project schedule, in this case for appendicitis, contains all the (initial) activities, types of labor (surgery, ...) and resources (personnel, lab, OR, ...) which the (intelligent) healthcare management system monitors and controls. The healthcare management system guides the patient through the entire project using RFID and/barcodes to track and trace the patient through his diagnostic and therapeutic schedule, supported by a critical path analysis, PERT diagram, or Gantt chart. The healthcare management system continuously monitors and controls the consumption of all resources compared to the work required to be accomplished for the diagnostic and therapeutic project schedule. The system continuously monitors and controls the cost variance where positive variance is good, and negative cost variance is bad. Ideally the actual costs match what was budgeted and the cost variance is zero in an ideal situation, but this depends on the complexity and predictability of the trajectory
(See also Clinical pathway and Standard operating procedure (SOP)).

A dynamic change management system keeps track of the diagnostic and therapeutic project along the way and continuously monitors and controls risks and issues which would require an adaptation of the diagnostic and treatment plan. Both man and machine work together to keep the trajectory of the patient on track. The monitoring and control system performs failure mode and effects analysis (FMEA) on the diagnostic and therapeutic activities. The system should continuously identify and prevent diagnostic and therapeutic problems before they occur, reducing the risk of sentinel events and system error-related occurrences (delay in treatment, negligence, wrong-patient, wrong-site, wrong-procedure, medication errors, op/post-op complication, unintended retention of a foreign body, fall, nosocomial infections, ... ). Rate-based and sentinel indicators allow for monitoring the entire project. They can be generic or disease-specific, and related to either structure, process, or outcome (e.g. rate of contaminated wound infection, hospital-acquired infection, ...). Sentinel events indicate poor performance by man and/or machine and they are used for risk management and process improvement (e.g., patient misidentification, wrong surgery performed, patients who die during surgery, patients who die during the perinatal period, ...). The system monitors events and triggers further analysis and investigation (e.g. based on the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) outcome measures grouped into seven categories: Mortality, Safety of care, Readmissions, Patient experience, Effectiveness of care, Timeliness of care, Efficient use of medical imaging)
(See also Health care quality and Quality of Care Monitoring Framework and core indicators (WHO) and Donabedian model and ICHOM and Agency for Healthcare Research and Quality (AHRQ)).

Earned Health Management (EHM) as a tool allows for measuring the actual performance of the diagnostic and therapeutic activities against the original plan for this patient. The healthcare management system continuously identifies areas where the diagnostic and therapeutic project is different from the original plan, and identifies variances and trends that influence the diagnostic and therapeutic costs. Planned Health (PH) can be compared to Earned Health (EH), etc. . All diagnostic ans therapeutic projects and activities are being planned, monitored and controlled in a program management system which is capable of managing resources (man and machine).

For those who are familiar with professional project management it should be clear that the principles of project management can be applied to healthcare taking into account the sometimes dynamic nature of a diagnostic and therapeutic plan. The main issue is that healthcare workers are not accustomed to the principles of project management and its language. The tools which support projects in other industries need to be tuned to healthcare projects and its users. Clinical pathways and accreditation are examples of an attempt to bring project management to healthcare. However, the productivity gains which can be achieved in healthcare are limited because of the Baumol effect. It is mainly the cost of poor quality (COPQ) or cost of diagnostic and therapeutic failure, which can be reduced by process improvement at least for the near future.

It is easy to see the similarities between SOAP notes and agile project management techniques such as SCRUM. In SCRUM, on each day of a "sprint" (daily iteration), the team holds a daily scrum meeting called the "daily scrum". A SOAP note (Subjective, Objective, Assessment, and Plan) represents a similar agile approach to healthcare. Each day the subjective (S) and objective (O) information of a patient is being discussed and assessed (A) after which the plan (P) for the next iteration is being designed and executed (diagnostic and therapeutic actions). A diagnosis (anamnesis, clinical examination, lab, RX, ...) is like gathering the requirements which will lead to executing a therapeutic process. One should understand that medicine is more than just staring at lab results, it also involves important anamnestic and clinical skills in order to gather important clues to build a set of requirements. Someone who understands the fundamental principles of project management and is capable to see through the labyrinth of acronyms (PMBOK, PRINCE2, SCRUM, XP, ...) will see that medicine is a project driven activity although (most) physicians are unaware of the jargon being used in professional project management. You have to evaluate the project status according to the project plan (differential diagnosis), asses risks and issues, adapt and execute after which the cycle iteratively continues. In medicine a differential diagnosis resembles the analysis upon which a project plan is being built. Either you get a waterfall model or an iterative project. The signs and symptoms of an (uncomplicated) appendicitis leading to an appendectomy can be regarded to represent a waterfall approach. A patient representing with a set of signs and symptoms leading to multiple possible underlying diseases will lead to a more agile and iterative approach based upon a differential diagnosis at the onset or even evolving during one or more diagnostic and therapeutic iterations. Medicine in such a complex case resembles a non-linear and stochastic process based upon probabilities. Healthcare, like engineering is a matter of analyzing requests, requirements development and management, planning, monitoring and controlling a project. A diagnostic and therapeutic trajectory requires a (flexible) work-breakdown structure (WBS). Every so-called project management wonder weapon has to adhere to these fundamental principles and it is the ability to act accordingly to the fundamental requirements of the problem to be solved which defines failure or success. However the capacity for non-linear thinking distinguishes a physician and nurse from an engineer and technician, leading to sometimes "unpleasant" confrontations in mixed teams. Thinking agile and in building blocks instead of rigid project trajectories facilitates communication between healthcare workers and project engineers. The turf wars between adherents of various project management methodologies quite often miss the point of understanding the underlying fundamentals of the problem to be solved. It is a matter of assembling roles and activities demanded by the nature of the problem to be solved. The methodology adapts to the problem and not the other way around. Waterfall or agile project management involve the same activities only assembled in a different way. You have to take control of the project, but not always in the same way (bureaucracy versus understanding). Failure to grasp the fundamental nature of the problem and how the situation has to be brought under appropriate strategic, tactical and operational control creates havoc. Of course the span of control capability with regard to the fundamentals of the disease process also defines the potential of successful treatment. Clinical pathways (integrated care pathways) are a way of applying project management principles to healthcare. It is based upon the adaptation of the principles used in industrial project and quality management and standard operating procedures (SOPs) (Gail, 1999, p. 3 and Ozcan, 2017, p. 462). Applying the principles of the Capability Maturity Model Integration (CMMI) to healthcare would allow for bridging the divide between healthcare and engineering maturity development and allow for alignment and transfer of maturity improvement principles between healthcare and engineering. Think of it as developing a kind of Lorentz transformation between two reference frames. One could also imagine developing a Manifesto for Agile Healthcare Development similar to the Manifesto for Agile Software Development. Maybe someone should write a Guide for the Perplexed in order to reconcile healthcare with project management principles
(See also Agile manifesto for physicians).

Unified N-dimensional and Multi-scale Analysis

Figure 9: Health status change vector (scope, z-axis), as a function of time (x-axis) and cost (y-axis) of treatment.
Origin (0,0,0) is the "as is" situation at intake (baseline), while the vector points at the desired,
and in this case, achieved "to be" situation (time, cost, health status).

The analysis of health care data (health care analytics) is required at different levels and situations. "Off-carpet" intelligent technology has to be part of the entire healthcare process, not only "Office-IT". First of all we have Point of Care (PoC) data (operational), which support identitovigilance (patient identification tracking), patient history and current (operational) status, clinical decision support and reporting. Closed control loops involving man and machine have intelligent process monitoring capacity. Feedback systems which close the operational control loop, have to integrate both man and machine in one integrated and hybrid process. Operational Procedures and Systems combine Operational Technology (OT), with human operational supervision and self-monitoring and the IoT, into one integrated hybrid process. Human participants, IT and OT have to be part of an integrated hybrid process, with the elimination of process (step) gaps. Supervisory Control and Data Acquisition (SCADA) systems which monitor a project-driven healthcare-process are part of the operational process. Hospital and population based data (tactical and strategic) deal with trend analysis, pharmacovigilance and clinical audits. Analytics for clinical research deal with the identification of clinical trail candidates and predictive medicine. Point of Care (PoC) analytics should assist physicians and nurses in remembering preventative measures, identify patients with potential care gaps and risk factors, monitor patient compliance with prescribed treatments and finally report (anonymous) clinical data to disease, treatment and outcome registries. A Point of Care (PoC) process analysis system documents the "as is" state at patient intake (baseline) compared to the "to be" or future state in relation to the real gain in health status at the end of the process (fit gap analysis, Figure 9). It evaluates if the health care process was capable to achieve the goal of improving a patient's health status (effectiveness and efficiency). Keep in mind the Triple Constraint of project management when planning and performing a project (diagnostic and therapeutic) to treat a patient (multi-factorial optimization of treatment, revenue cycle management (RCM), earned value method (EVM)). Multilevel operational, tactical and strategic analysis, should move from 'a posteri' analytics to 'a priori' predictive analytics (D.B. Nash, 2014; K.A. Gould, 2017; E.C. Norton, 2018). If you have no idea about what predictive analytics can do, read the book Moneyball: The Art of Winning an Unfair Game (2003) by Michael Lewis, or watch the movie 'Moneyball' (2011) on sabermetrics as a starter. Predictive analytics by means of (real-time) operational data, such as SNOMED CT, LOINC, ATC (WHO), and other structured (standardized) relevant PoC data, allows for a healthcare and (operational) process improvement revolution. As an example, forward-looking healthcare analytics predicts the (potential) impact of an epidemic based upon the immune status of a population and the available resources to deal with the epidemic (B Jester, 2018). Pre- an post-calculation (PDCA) will teach you the validity of the analysis and the improvement in both efficiency and effectiveness. Trend analysis should assist long term population health monitoring, prediction of demand, and effective resource allocation. Trend analysis should also assist short term resource planning and allocation, such as in case of epidemics, natural disasters or terrorism (ER, ICU and OR resources) on a local, regional, national and international scale. Pharmacovigilance should support collection, detection, assessment, monitoring and prevention of adverse effects with pharmaceutical products. Multi-domain clinical audits support improving patient care and outcomes through systematic review of care against defined standards and the implementation of change
(See also Plan-do-check-act (PDCA) and Agency for Healthcare Research and Quality (AHRQ) and Healthcare.ai and Tools for Healthcare Machine Learning (R) and Predictive analytics in health care - Emerging value and risks and Implementing Electronic Health Care Predictive Analytics: Considerations And Challenges).

Figure 10: Healthcare process multi scale data-driven monitoring and control (PMC) loops.
A data pyramid collapses and expands data representation in relation to the distance to the operational point of care (POC).

The strategic, tactical and operational decision-making units (DMU) are optimized for economy of scale, while balancing size against the diseconomy of scale in relation to the process monitoring and control capabilities available to a healthcare organization. Due to the limited monitoring, command and control capacity of the traditional healthcare process, the optimal range for hospital size is between 200 and 600 beds (Giancotti M, 2017). Multi-scale data-driven monitoring and control (PMC) loops allow for implementing monitoring and control at the appropriate level in relation to the span of control of a decision-making unit (DMU) and integrated and embedded into the overlying and underlying scales of control, from operational over tactical to strategic. A data pyramid not only spans multiple levels of process organization and integration, but at each level creates a DIKW pyramid. The system should create a common operational picture (COP) in order to align omni-directional decisions and actions at all levels and branches of the health care process(es), from (inter)national to the individual patient and healthcare worker level. A N-dimensional spiderweb may be a more appropriate metaphor for an integrated healthcare system. Data produced at one location in space and time create ripples which propagate through the web. Disjunctive and non-connected data representation and fragmented data-flows (information silo) lead to distorted analysis and decisions by acting against the underlying reality. The conceptual and ontological deficit of healthcare ecosystems creates waste and reduces efficiency and effectiveness of the overall system. Process waste can be compared to bilge water, leading to (hidden) overhead to keep the "healthcare vessel" afloat. Putting misaligned data, which are an ontological misrepresentation of underlying reality, into a decision process causes havoc. The quality of a decision is only as good as the quality of the information from which it is being derived. Misrepresentation of the underlying clinical reality is a cause of strategic, tactical and operational errors. The longer the feedback loops, increases the risk of intermediate misalignment of activities (Figure 10). By reducing the feedback loop, the time between an event and validating it, the process reduces the cost of change/adaptation of diagnosis and treatment. A short process feedback loop resembles a test-driven design (TDD) approach. Sampling density should be aligned to the potential rate of change of deviation from an optimal diagnostic and therapeutic trajectory (risk based approach). Sampling can be synchronous (in-line) or asynchronous related to the diagnostic and therapeutic trajectory, depending on the potential urgency of analysis and action. Sampling density at an intensive care unit (ICU), operating room (OR) and recovery should have a shorter temporal interval with regard to other departments. Scaling the density of vectorial and scalar parametrization is also related to the temporal density of change of the underlying disease processes. Intelligent dynamic process monitoring and control, related to the patient status, allows for alignment of analysis and action to the underlying clinical situation. Pushing monitoring and control to the early stages of an agile healthcare process and in the end using predictive data, based upon inflow predictions, reduces process and resource waste and sentinel events. Prevent instead of repair process errors and increase process safety by putting risk management and issue prevention into the operational process itself, thereby creating an adaptive process (fix early fix cheap, Cost of Change Curve).

Observational and real world data versus a Randomized Controlled Trial

Real-time digital health data provide opportunities for healthcare process monitoring and control (PMC) and also for research. Real world data (RWD) and its derivative real world evidence (RWE) are becoming increasingly popular in medicine. With regard to data-driven empirical evidence, we should be aware of the underdetermination of scientific theory by evidence (Duhem-Quine thesis). Observational study design and analysis, emerging statistical methods, provide opportunities for large scale observational studies to complement evidence from experimental methods, treatment heterogeneity, and effectiveness estimates tailored toward populations, sub-populations and individual patients. However, we should take care to distinguish (large sets of) observational data, e.g. "big data", from research data from prospectively collected clinical data or (double-) blinded, randomized controlled trial (RCT) (Level I clinical evidence and hierarchy of evidence). As with a randomized controlled trial (RCT), careful study design and analysis of the population data is required for observational studies (K.F. Schulz, 1995; K. Benson, 2000; J. Concato, 2000). Replacing randomized trials in favor of quick and dirty observational designs is not the way to go (J.P.A. Ioannidis, 2001). Most observational studies do not compare to a (double-)blinded, randomized placebo-controlled trial, not even when they are peer-reviewed (H. Sacks, 1982; T.C. Chalmers, 1983; G.A. Colditz, 1989; J.N. Miller, 1989).

Hypothesizing After the Results are Known (HARKing) is a problem to be dealt with (N.L. Kerr, 1998). It is also important to know about a possible conflict of interest or if someone "has a horse in the game". Statistical analysis of observational data should be handled with care. Dealing with outliers and assumptions of the underlying distribution is an integral part of statistical analysis (D.L. Streiner, 2018). You should be aware that the normal distribution is not always the natural distribution; in reality, skewness more often is the case. Reducing skewness by removing data you do not like, is not always the right thing to do (e.g. abnormal lead concentrations in the Flint water crisis). In real life, the so-called normal distribution is not always the "natural" distribution of natural events. Skewness and multi-modality are common and should be expected when dealing with real-life data. Outliers should be carefully analyzed, as they may signify an alert (Flint water crisis), a discovery, essential information (fraud, intrusion), a natural phenomenon or a result of mixed distributions. Always be aware of the context of the data and use methods adapted to the type of data you work with. Careful exploratory data analysis, and being aware of Anscombe's quartet is important (F.J. Anscombe, 1973). Data cleaning and preprocessing should not equal data fiddling. In statistics the multiple comparisons problem or "multiplicity problem" (multiple inferences) illustrates that one can find any particular pattern in random data if there is enough data and computing power. The phenomenon is also known as the look-elsewhere effect, where an apparently statistically significant observation arises by chance, because of the sheer size of the parameter space to be searched. Data dredging (data fishing, data snooping) is another problem when dealing with large data sets, bias and sloppy statistics. Data mining is vulnerable to spurious correlations, underfitting and overfitting. We should also be aware of Simpson's paradox (amalgamation paradox) when dealing with frequency data. When the volume of research and data becomes more important than the quality of research, we have a problem. In an observational study we may find a positive or negative correlation between variables, but this correlation does not necessarily leads to the conclusion of a causal relation. For instance there is a positive correlation between cigarette lighter and lung cancer, but the confounding variable is smoking. Another example is the highly statistically significant correlation between stork populations and human birth rates. Remember, covariance is just a measure of correlation, and correlation is a scaled form of covariance
(See also Statistical hypothesis testing and Design of Observational Studies, Paul R. Rosenbaum, Springer Science & Business Media, 2009 and Randomized Controlled Clinical Trials, Christopher J. Bulpitt, Springer Science & Business Media, 2012 and Robert Matthews, Storks Deliver Babies (p = 0.008), Teaching Statistics. Volume 22, Number 2, Summer 2000 and Guide to Biostatistics (MedPage) and Observational Health Data Sciences and Informatic (OHDSI) and Comprehensive repository of data science and ML resources and Why is the Box-Cox transformation criticized and advised against by so many statisticians? What is so wrong with it?)..

It all comes down to the way we deal with inferential statistics in order to draw conclusions and trends about a large population based on a sample taken from it. We should be aware of the ongoing replication crisis in biomedical research, mainly due to the failure to adhere to good scientific practice (GSP) and sloppy statistics (C. G. Begley & J. P.A. Ioannidis, 2015; J. P. A. Ioannidis, 2016). As an example, a team at biotech company Amgen found that it could not replicate 47 out of the 53 cancer studies it had analyzed (M. Baker, 2016). Research findings may often be simply accurate measures of the prevailing bias (J. P. A. Ioannidis, 2005). The ongoing discussion between frequentist versus Bayesian statistics is also part of the discussion on the replication crisis. There is also the confusion between a Bayesian P(H|D) (a posteriori belief) and frequentist P(D|H) (likelihood of the data) concept (hypothesis p(H)). Some people believe this replication crisis is because of the use of frequentist inference and that a Bayesian approach is an alternative that could solve this crisis. These approaches differ in the way they deal with uncertainty and their interpretation of probability. Frequentist and Bayesian methods differ in the way they test for significance and difference between groups. The main difference between the frequentist statistics and Bayesian statistics is the use of prior probabilities in the Bayesian approach (conditional probability). Confidence intervals and p-values are an important aspect of frequentist statistics being used for significance-based hypothesis testing. One of the pitfalls is to rely too much on the principle of statistical significance alone, as it doesn't tell you everything about the truth of the hypothesis you're exploring (Matrixx v. Siracusano, Supreme Court of the United States, 22 March 2011). In the Bayesian framework, the Bayes factor is the equivalent of p-value and an alternative to classical hypothesis testing. Every approach has its strengths and weaknesses, but first of all, it should be used with a proper understanding of the basic rules of high-quality research and experimental design. There are several ways science tries to deal with the replication crisis. Initiatives such as the Cochrane organization, produce quality reviews of medical research. Initiatives such as the Appraisal of Guidelines for Research and Evaluation (AGREE) evaluate the process of practice guideline development and the quality of reporting. High quality medical journals with a proper review board and statistical screening, such as the The New England Journal of Medicine are to be preferred over the dozens of low quality biomedical journals and predatory journals without a decent review process and sloppy statistical validation of research papers (D.G. Altman, 2002). You can make data say whatever you want if you slice it and dice it specifically enough. But whatever the reason, once data are manipulated to get the desired results, their validity goes out the window. It is also interesting to keep in mind social constructivism (or socioculturalism), which claims that the creation of knowledge cannot be separated from the social environment in which it is formed (A. Kukla, 2000)
(See also Guide to Biostatistics (MedPage) and Some tips about statistics on medical research and Challenges in irreproducible research and Misuse of p-values and What statistics should I know to do data science? and Frequentist and Bayesian approaches in statistics and Frequentist vs Bayesian - which approach should you use? and Frequently asked Bayesian statistics questions and Metropolis-Hastings algorithm and Markov chain Monte Carlo (MCMC) and Akaike information criterion (AIC) and Journal Citation Reports (JCR) and Preclinical reproducibility and robustness and Cabell's blacklist).

Terminology and ontology

Figure 11: Ontological web - Ontology-Oriented System (OOS).
Red dots (new data), blue dots (old, pre-existing data), white dots (nodes in the ontological web).

In order for people and digital systems to understand each other, not only must they be able to exchange words or data with each other, they must agree about what they mean (J.J. Cimino, 1998). Interoperability is about people understanding each other, people and information systems understanding each other, and information systems understanding each other. I will not deal with the problem of medical jargon and "interoperability" between physician and patient. An integrated healthcare system requires both functional and semantic interoperability, which is the ability to transfer information between people and information systems, and have people and information systems use the information in a meaningful way. A common terminology (ontology) allows for the exchange and distributed processing of information and meaning. A digital healthcare system should not only be capable to hold raw data or singular terms and only deal with raw or lexical information, but also be capable to deal with higher order syntactic and semantic information in order to deal with knowledge management, data integration and decision support (Bodenreider, 2008, ontology). An EHR should be capable to deal with syntax and semantics, as we do not need (more) dumb repositories. Information driven healthcare is not the equivalent of putting raw data, such as ECG-, CT- and MRI-data, etc. in the cloud, but to convey meaning (semantics). An ECG could lead to a diagnosis of a myocardial infarction (MI), which is the semantic information which needs to be dealt with. The raw data are only stored for future reference and validation (audit trail). A terminology (lingua franca), which is capable to deal with semantics, should be part of an information driven healthcare system. Every EHR-architecture should take care of proper terminology binding, which is the link between its information model and terminology artefacts (semantics), based on a common (public) health information standard or information representation (medical ontology). The terminology binding includes additional metadata about the binding and allows for meaningful data capture, data retrieval and querying, information model library management and last but not least semantic intra- and interoperability. Being able to capture and process (marshalling, unmarshalling) the message or meaning behind a term, phrase, and sentence (lexemes, tokens, semantics) requires higher-order processing capacity of an ExR system, which is still lacking in most (dumb) systems
(See also What makes an EHR "open" or interoperable?).

An ontology in information science is an agreed upon codification of the "Ding für mich" into a "Ding für uns". An ontology defines underlying concepts and then assigns human-readable code-words or labels that are used to refer to the concept definitions. It describes aspects of reality by means of a stack of subject, copula and subject complement relations (Indo-European copula). The ontology allows for semantic differentiation and integration, by means of its hyponymy and hypernymy relations. It resembles on a conceptual level a stack or pyramid representation of multi-scale feature spaces and an ontology is for language what a scalespace (stack) is for geometry. An ontology allows for semantic differentiation, resembling a multi-scale feature space which can be expanded or collapsed, depending on the amount of detail achieved or required. The depth and width (scale) of a concept resembles the depth and width of a isotropic or anisotropic Gaussian blob, covering a space (manifold) of meaning, representing a certain concept of (part of) reality. Reasoning by means of an ontology, resembles convolving reality with the ontology. Reasoning based upon an ontology (A copula B) resembles reasoning with algebra (A = B). An ontology relates to an algebraic approach of reality as is the case for an Indo-European language applying syllogisms, while a logographic system (e.g. Chinese) resembles geometry (images). Both algebraic and geometric multi-scale differentiation and integration could be combined into an integrated system. It is as if switching from algebra to geometry, depending on the nature of the problem and the tools available for analysis and processing. Both algebraic and geometric processing steps can be embedded into algorithms
(See also ISO 704:2009 (Terminology work - Principles and methods) and ISO 10241-1:2011 and description logic and algebra and computational geometry).

A EHR-ecosystem requires syntactic and semantic capabilities for dealing with the ontology. Piles of unstructured and dispersed data in an EHR do not contribute to operational, tactical and strategic efficiency and effectiveness of healthcare. Data aggregation is the key in terms of making data accessible, computable and actionable. How the data are processed and analyzed determines if meaningful insights can be generated from the data. The relation between symbols, syntax and meaning determines the processability of data. Syntax is related to the grammar applied to data. A context-free grammar (CFG, Phrase-Structure Grammar) should allow for dealing with expressions (syntax), not only individual terms. Dealing with the syntax of a formal languages requires a syntax specification for interoperability, such as the Augmented Backus-Naur form (ABNF) (Internet Standard 68). A context-free grammar grammar will do for intra- and inter-machine exchange. For human-machine communication we will need high quality Natural-language processing (NLP) from man to machine, and Natural-language generation (NLG) for machine to man communication. The real-world performance of NLP is a well kept secret, but in general is a well below what is clinically acceptable (S. Meystre, 2006; D Demner-Fushman, 2009; A Névéol, 2018). Although NLP systems may perform well on natural-language understanding task sets such as GLUE (General Language Understanding Evaluation), SuperGLUE, SQuAD (Stanford Question Answering Dataset), and SWAG (Situations With Adversarial Generations), understaning real-world (clinical) language is outside this range. Computers do not understand words, and techniques such as Bag of Words (BoW) or Term Frequency-Inverse Document Frequency (TF-IDF) are no substitute for capturing and understanding semantic meaning. Statistical correlation and learning shortcuts does not equal learning the actual skill (R.T. McCoy, 2019; T. Linzen, 2020)
(See also Semantics and semiotics).

Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of NLP in a realistic clinical setting should be taken into account, not only in carefully crafted but limited (biased) specialized data sets. The success rates in real life situations are in most cases less than 70%, which means that any system which operates on the extracted data has a deficit of about 30% from the start or requires additional supervision and assistance from its users. They have better things to do, such as taking care of their patients. In order to understand natural language precision (positive predictive value) versus information recall (sensitivity), and F₁ score (harmonic mean of precision and recall), you should have a basic understanding of the meaning of "true negatives", "false negatives", "true positives" and "false positives". Precision is the relation between true positives and the total number of true positives and false positives. Recall is the relation between true positives to the total number of true positives and false negatives. The challenge is to have very high recall, combined with a very high precision, which should both be higher than 99.99% in order to be of real use in a healthcare situation with real patients. Anything less than 99.99% NLP accuracy is not acceptable in a clinical situation, when subsequent processing of data is to be used for clinical decision support (CDS) and process management and control (PMC). Control and management of NLP-systems should not take away resources from patient care. Besides NLG, visual and feature rich presentation of information in an interactive way is also important. Ontology-preserving analog-digital communication is important, so not only lexical and syntactic, but also semantic. Man and machine need to speak a common language in order to exchange information back and forth between the analog an digital world. We need common symbols and concepts (syntax) and an agreement about their meaning (semantics). Concepts need to be classified (taxonomy, nosology) and their associations and relations need to be defined (thesauri, synonyms and antonyms). Rules need to be defined about which relations are allowed and make sense (ontologies). I have given an overview of all these aspects of knowledge sharing as it provides a broader view on the term ontology as it is being used in computer science. To create a hybrid man-machine and analog-digital collaboration, all aspects of communication, knowledge sharing and knowledge processing need to be taken into consideration. A simple pick list of words is not sufficient to embark onto the path toward a truly integrated analog-digital collaboration and process
(See also Logic and Ontology and parsing and named-entity recognition and NLP’s generalization problem, and how researchers are tackling it and MNIST database of handwritten digits).

Figure 12: Ontology expansion and coverage (color) of the clinical domain (grey).
Red and green represent the (expanding) capability of a computational ontology (sayability),
while blue represents the extra-computational (human) understanding of "clinical reality" (grey),
of which the boundaries are unknown to man and machine.

When representing clinical reality by means of computational ontologies for in-silico processing, we should be aware of some fundamental principles and limitations. As with any participant in a clinical process we should be aware of the capabilities and limitations of an ontology-driven digital system. Knowledge of the clinical world is limited both by the technical capabilities of an intelligent digital system (interfaces and processing capacity) and the language it uses (computational ontology, syntax, symbols). A system does not have direct access to reality, given that the most it can know is that which is filtered through the system's interface with reality and its presentation of reality. The limitations of computer interfaces and computational ontologies, limit the operational capability of physicians and nurses to represent clinical reality (meaning) into digital systems. These limitations also limit the capabilities of the system to convey meaningful information to the physician or nurse (VL Patel, 2002). The quantity of data does not equal quality of information (meaning). We have to make a distinction between epistemology and ontology. Epistemology is concerned with the questions "What do you know?" and "How do you know it?" (knowledge and justified belief), while (an) ontology is concerned with "What is there?" (basic categories of being and their relations). Epistemology deals with the relationship between the knowledge of an intelligent (digital) system of reality and reality. The epistemological capabilities of a digital system will be limited by its ways and means of interaction with clinical reality (e.g. A/D convertors, screen, keyboard). Representing what is there, will be limited by the representational capabilities of the computational ontology of the digital system. Computational ontologies refer to formal representations of shared and reusable knowledge, which is a subset of what is traditionally meant by an ontology (the nature of reality). The reusability refers to computational reusability within the computational limitations of a digital system. The question is how does the ontology represent reality and what is the reusability in-between machines and between machines and man? A computational ontology consists of a description of concepts (classes), individuals (objects), roles (properties, relationships) within a given domain (here we deal with the clinical domain).

Ontology design can be either monolithic (one file) or modular (ontology modules). Representing clinical reality by means of a modular ontology has to deal with the coverage, cohesion and coupling of the ontology. Ontology module coupling refers to the degree of relatedness between ontology modules (inter-modular independence), while cohesion refers to the degree of relatedness of classes in a module (intra-modular cohesion). Modular ontology design adheres to "low coupling, high cohesion" principles. The coverage of an ontology (Figure 12), relates to the coverage of the analog domain by means of the digital ontology (content completeness problem). An ontological representation of reality does not include the ontic or concept-independent aspects of reality. Creating a (gold) standard from which to evaluate the concept coverage and accuracy of an ontology is an important aspect of ontology validation. The reference standard itself imposes a certain world-view upon the ontology. The computational ontology itself will impose this (implicit) world-view on its users. Computational ontologies and semantic applications operate within the constraints of their epistemological and ontological limits. The digital ontology limits and enforces a certain relation between the analog and digital domain. Reasoning (logic) within and with a computational ontology is also limited to lower order logic (description logics, first-order logic). The coverage, cohesion and coupling of an ontology has an important impact on the quality of the overlying system which is limited in its capabilities by the underlying ontology. The "understanding" of a clinical situation by an intelligent system is limited to the boundaries and structure of the ontology which it is provided with or which it creates itself (machine learning). Only what is sayable within the boundaries of the computational ontology, has any meaning for the digital systems based upon the ontology. Computability and actionability of data is based upon the capacity of the digital system to extract meaning from its data. Therefore it is important to be aware of what is knowable and sayable by a digital system. How do the boundaries of knowability (world representation) shape and limit its behavior (actions). Representation of clinical reality by means of a digital ontology is always defective in comparison to clinical reality (Figure 12). Ontological standardization (lock-in) limits the evolvability of the system and puts boundaries on what can be represented (representational deficit) and being dealth with, which we have to be aware of. We should be aware that "the map is not the territory" and "the word is not the thing" (Alfred Korzybski Science and Sanity). As Ludwig Wittgenstein wrote in his Tractatus Logico-Philosophicus: "The limits of my language are the limits of my world". What is called "big data" may refer to the volume of the available data (bytes, symbols, syntax), more than to size (coverage) or depth (meaning) of representation of clinical reality. Volume of data (quantity) does not equal intrinsic and contextual representational validity (quality) of data (Loshin D, 2011, pp. 129-146). Naive reliance on quantitative width does not compensate for a lack of qualitative depth (Coiera E, 1991). Quantity of data does not represent epistemological validity and increasing the quantity of data as such does not increase epistemic value
(See also Schulz, 2009 and Halper, 2017 and map-territory relation, Alfred Korzybski and applied ontology and ontology engineering).

The estimated size of the medical vocabulary being used in modern healthcare, amounts to about 200,000 terms (Axel Karenberger 2015, p. 19). For the moment SNOMED CT (Systematized Nomenclature of Medicine-Clinical Terms) is the most comprehensive and expressive precoordinated clinical terminology worldwide (325,000+ concepts), but let us take a deeper look at machine processing of medical information. The internal structure of SNOMED CT, consists of a poly-hierarchy and description logic (formal definitional attributes). This logic improves precision and efficiency of queries and supports decision support. It allows for identifying patient cohorts for population analysis and epidemiology. It facilitates identifying patient cohorts for certain conditions. SNOMED CT facilitates the identification of subsets for use as input criteria for clinical decision support systems. It makes it easy to do precise queries, such as "find all conditions where the causative organism is Escherichia coli O157:H7". SNOMED CT also makes it easy to do large aggregate queries, such as "find all patients with cardiovascular system disorders". Having a central terminology management system based on SNOMED CT eliminates duplication of effort and provides a common definition of data via common concepts/terms across the healthcare ecosystem. A clinical terminology such as SNOMED CT creates a bridge between man and machine, but also imposes an ontological, logical and philosophical framework upon this relation. First of all one has to be aware that computational ontologies such as the Basic Formal Ontology (BFO) and SNOMED CT are systems based on analytic philosophy. They represent not only a certain way of representing our knowledge of the world, but also the way we represent reality itself. Computational ontologies, such as the BFO and SNOMED CT, are designed to represent entities, universals, classes, and relationships within domains of knowledge and to allow for semantic processing and interoperability. We need to represent the conceptual knowledge of medicine in a structured and formally well-understood way as a prerequisite for machine processing of medical information. Machine readable knowledge representation and reasoning requires some formal representation (ontology) and systems to manage it. Surrounding systems should be capable to develop and maintain the ontology being used to represent knowledge and machine reasoning. It requires concepts (terminology) classified in a meaningful way (taxonomy) and a way to allow for machine reasoning (logic) with these concepts.

Assembling a pile of words (vocabulary) is not sufficient to deal with medicine in meaningful way in a hybrid analog-digital world. We need a formal ontology to do the work of creating a formal and meaningful representation of knowledge. Let us start at a basic formal ontology. The Basic Formal Ontology (BFO) is a top level ontology. It is a upper level, domain neutral ontology, developed with rigor and a theory which avoids previous ontological mistakes. BFO is a computationally tractable fundamental ontology which remedies data silo problems and is suitable for querying and domain specific ontologies can be derived from BFO. There are several BFO implementations which are realizations of the BFO technical specification as a program or software component. One such family of ontologies is the Web Ontology Language (OWL) (Protégé). OWL has three increasingly expressive sub-languages: OWL Lite, OWL DL and OWL Full (OWL Guide). The expressiveness of the ontology refers to its structure and formalism and the extent and ease by which an ontology can describe domain semantics. More formal ontologies have greater expressiveness and structure and inferential power, but this comes at a cost, so there is always a trade-off between semantic clarity versus the time and money required to construct the ontology. The cost-benefit considerations of an ontology for medical use have to take into account the environment in which it will be used, a classical "dumb" EHR or a highly sophisticated and "intelligent" system, which can be an assistant for our physicians an nurses in their daily work. Putting a highly expressive ontology into a low quality system will not bring more benefits than just using a "dumb" vocabulary to register information. A terminology is only one part of the system, but dealing with the ontology (reasoning) in a meaningful way is another aspect to be taken into account. Dealing with a formal ontology in OWL is done by means of a Description Logic (DL), which is a formal language which is being used for representing knowledge and reasoning about it (concepts, syntax, semantics, logic). Description Logics provide the logical underpinning of OWL, the standard ontology language for the semantic web. OWL DL is so named due to its correspondence with description logics and allowing for maximum expressiveness without losing computational completeness and decidability. When working with systems using Description Logic (DL) one should be aware of the characteristics of DL as DL does not replace medical experience or intuition gained through years of clinical practice, but Description Logic (DL) does allow for automatic reasoning within the boundaries of the ontology. Description Logic (DL) is a logic-based formalism for representing knowledge about concept hierarchies and supplied with effective reasoning procedures and a Tarski-style declarative semantics
(See also Semantic theory of truth and Nardi, 2003). It is being used to represent the knowledge of an application domain, in this case medicine (its "world"), by first defining the relevant concepts of the domain (its terminology), and then using these concepts to specify properties of objects and individuals occurring in the domain (the "world" description).

Description Logic (DL)

Description Logic is equipped with a formal, logic-based semantics which allows for machine reasoning and allows one to infer (deduce) implicitly represented knowledge from the knowledge that is explicitly contained in the knowledge base (clinical decision support and clinical process support and management). DL allows for classification of concepts and individuals and the classification of concepts determines subconcept/superconcept "is a" relationships (called subsumption relationships in DL) between the concepts of a given terminology, and thus allows one to structure the terminology in the form of a subsumption hierarchy (taxonomy). Classification of individuals (or objects) determines whether a given individual is always an instance of a certain concept (e.g. instantiation of a "class"). An ontology defines the semantic similarity or distance between terms/concepts, by means of "is a" relations and "has" certain attributes. Changing the definitions and the relationships between concepts will change the ontology and the behavior of the system when applying machine reasoning (medical and legal implications). The quality of the relation between the concept and the individual, which is assumed to represent the concept, determines the usefulness of the ontology with regard to real world problems. One should always be aware of the arbitrariness and cultural specificity of any attempt to categorize the world into an ontology (e.g. Emporio celestial de conocimientos benévolos in El idioma analítico de John Wilkins (1942) by Jorge Luis Borges). No machine reasoning power can compensate for a defective relation between concept and its instantiation. Let us consider the way of reasoning being used in Description Logic. Description Logic (DL) lies in between propositional logic and First Order Logic (FOL). They are a family of languages for knowledge representation, which are mostly a subset of First Order Logic. They cannot deal with problems which go beyond the limitations of their logical framework, which one should be aware of when using them for clinical decision support. Compared to First Order Logic (FOL) Description Logics have the advantage of always being decidable, which is not the case for First Order Logic. Description Logic as such is a decidable fragment of FOL and efficient reasoners exist for DL. Because of this, it is possible to make logical deductions based on Description Logic and to create (compute) new knowledge from existing knowledge. Description Logic distinguishes between definitional (meaning) and factual information (assertion). Each data structure is uniquely interpretable either as expressing a factual assertion or as expressing (part of) the meaning of a word (Marconi, 1997). For this we have T-Boxes (terminological box) or the world's rules (as described in the knowledge base) and A-Boxes (assertional box), which are the relations between and properties of individuals. A DL-reasoner provides reasoning with T-Boxes and (multiple) A-Boxes and performs consistency checks of A-Boxes, T-Boxes or both. A DL-reasoner could retrieve all individuals of a concept, all concepts of an individual or check for subsumption, etc. Description logic (DL) reasoners can apply logic-based techniques to assist with clinical decision support reasoning and clinical process management and control.

From a user point of view, a DL would also allow to shift a user interface from selecting codes (vocabulary) to describing conditions (intelligent user interfaces). However one should be aware of the limitations of a reasoner within the boundaries of the Description Logic. Something interestingly to know when using DL in medical applications is that DL does not make the Unique Name Assumption (UNA) or the Closed-World Assumption (CWA), but makes the Open-World Assumption (OWA). Let us take a look at OWL DL, which makes use of the characteristics of DL. OWL DL (description logic) offers maximum expressiveness, while retaining computational completeness and decidability, which is what is needed for a healthcare ontology. There is always a tradeoff between expressiveness and complexity. When allowing for a very expressive language, one has deal with the tractability of reasoning by doing a form of reasoning that is somehow less demanding. Expressiveness (expressive power) relates to computational complexity, which is why one has to balance the combination of representation complexity and reasoning methods that can solve the problem in a reasonable time and with reasonable computing power (Rector, 2008). Computational complexity doe not scale linear with the expressiveness of the formal language and so the reasoners and algorithms are limited in their capacity to deal with the complexity of medical information. Semantic processing requires a machine processable ontology capable of being dealt with by means of description logic (DL, terminological systems, concept languages). Knowledge representation systems are built upon the foundations of Description Logic (DL-KRS). Description Logic languages are the core of (healthcare) knowledge representation systems, taking into account both the structure of a DL knowledge base and its associated reasoning services. The advantage of an ontology, such as the OWL 2 Web Ontology Language, is that it allows for using description logic (logic-based semantics) in order to model, create, develop and verify the ontology by means of semantic reasoners, using for instance the method of analytic tableaux, such as OWL 2 EL based description logic reasoners, which can be used for SNOMED CT (e.g. OWL/Implementations and List of Reasoners and ELK and Knowledge Representation System Specification (KRSS) and Spackman, 1997). OWL 2 EL is an OWL 2 profile which trades some expressive power for the efficiency of reasoning, such as with SNOMED CT which contains very large numbers of properties and classes. The International Edition RF2 package of SNOMED CT can be converted to and OWL XML/RDF format and so DL reasoners can be applied to SNOMED CT. The principles of description logic (DL) go back to Aristotle's syllogisms (premises and conclusions) and the Arbor Porphyriana. Aristotelean logic can be viewed as a logic of concept description
(See also Introductions to DL by Ian Horrocks and Terminology Management With SNOMED CT At Kaiser Permanente by Jamie Ferguson).

Symptoms, signs, etc. become embedded into an Ontology-Oriented System (OOS), which becomes the basis of a knowledge-based diagnosis built upon on a well-defined ontology. The ontology transforms the relationships among symptoms, signs, personal history, etc., into a machine-interpretable model (Figure 11). The real world data (RWD) collected at the point of care and transformed into a machine-processable ontology can be used by physician and nurses to gather real world evidence (RWE) of point of care diagnostics and treatment. Interlocking PDCA-cycles would allow for multistage and multiscale process monitoring and control. The shorter the feedback loop the more impact it can have on healthcare as it happens. Intertwining operational, tactical, strategic, national and global process monitoring and control (PMC), would transform local, regional, national and global healthcare into an intertwined learning system. The analytical system has to take into account the diagnostic criteria, on which specific patient conditions may be classified under a specific disease, into its inference engine (association of signs and symptoms=diagnostic criteria). An inference engine could apply genetic reasoning, meaning crossmatching both regressive analytic and forward synthetic reasoning in order to analyze the supplied information. The OOS includes a specification of conceptualizations that constitutes evidence-based clinical practice guidelines. It represents the elements of an evidence-based guideline by specifying its attributes and defining the relationships that hold among them.

Figure 13a: N-dimensional space

Figure 13b: A.I. Experiment example, as part of TensorFlow (animation).

The meaningful data attached to the patient create an N-dimensional ontological space or landscape which can be used by patients, physicians, nurses, scientists and AI systems for visualization, analysis, diagnosis, treatment support and process management (Figure 13a) (role-based, safe and secure). a (digital "colleague") within a hybridized analog-digital process would allow for breaking the cognitive barrier as it would extend and even expand the cognitive capacity and dimensions of the process ( Magical Number Seven, Plus or Minus Two). Feeding the system with clinical data such as SNOMED CT based data (325,000+ concepts), laboratory data such as LOINC, etc., would create a vast data universe upon which intelligent systems could be built. As such the system not only supports individual diagnosis and treatment, but also population studies and epidemiology on a regional, national and international scale (Figure 13b). It will be very important to avoid process lock-in, data lock-in and vendor lock-in due to proprietary or regional standards. All too often data coming from one system are just gibberish or gobbledygook for another healthcare system. This situation creates unacceptable risks for our patients and healthcare workers and does no allow for large scale analysis and (operational) monitoring of health data (J.C. McClay, 2002).

Working with SNOMED CT

Figure 14: SNOMED CT Diagram of "Pulmonary edema caused by chemical fumes (disorder)" (SCTID 10674871000119105).
The "associated morphology" of acute edema was used in a SNOMED CT Expression Constraint Language (ECL) query.

We need to improve operational process and decision support at the Point of Care (POC) for our healthcare workers, in-line and in real time (close the loop). Put data to work at the PoC, instead of only being a bureaucratic burden. The SNOMED CT Expression Constraint Language (ECL) can be used to create bounded sets of clinical meanings, which can be used to capture meaningful information from electronic medical records. I will use a somewhat formal notation in my examples, but in reality a user of a SNOMED CT enabled electronic medical record system, will only have to work with the preferred term, such as "abdominal pain" or a synonym and not with the (unique) SNOMED CT Identifier (SCTID) "21522001" of "abdominal pain". The availability of preferred terms and synonyms in each language is the responsibility of the SNOMED CT National Release Center (NRC) in each country. It is the responsibility of vendors to develop safe, secure and interoperable SNOMED CT enabled systems.

An example of a SNOMED CT expression which would give us lung disorders associated with edema: " <19829001 |Disorder of lung|:116676008 |Associated morphology|=79654002 |Edema| ". As an example you can use the tool for "Expression Constraint Queries" of the SNOMED International SNOMED CT Browser for parsing and executing this expression, which would give you the SNOMED CT Identifier (SCTID) of lung disorders associated with edema, such as "10674871000119105" or "(Pulmonary edema caused by chemical fumes (disorder))", etc. (Figure 14). The end user only has to understand the meaning of "Pulmonary edema caused by chemical fumes", while the underlying intelligent healthcare system will deal with the SCTID "10674871000119105" and its attributes. Another example would give us infectious lung diseases, caused by Streptococcus: " < 40733004 |Infectious disease|: 363698007 |Finding site| =<< 39607008 |Lung structure|, 246075003 |Causative agent| =<< 58800005 |Genus Streptococcus| ". Apply the SCTID's, which are the result of the query in your ECL query tool, to your SNOMED CT enabled EHR or population health record (PopHR) in order to retrieve all patient records which contain these SCTID's. Healthcare process alerts and process execution could be triggered based upon SNOMED CT data, such as an alert on tuberculosis " << 56717001 |Tuberculosis| " or we could automatically execute an intelligent clinical pathway, based upon this SNOMED CT Identifier (SCTID) (P.C. Dykes, 2003)
(See also SNOMED CT Search and Data Entry Guide and Data Analytics with SNOMED CT and Decision Support with SNOMED CT and SNOMED International APG Parsers and Powering clinical data analytics with SNOMED CT (video demonstration)).

Clinical process and decision support with SNOMED CT

Another example shows what a Clinical Decision Support System (CDSS), a Laboratory Information Management System (LIMS), in combination with a SNOMED CT enabled EHR could do. We have a patient with 21522001 |Abdominal pain|, 95545007 |Hemorrhagic diarrhea|, 61261009 |Hemolytic anemia|, and 166717003 | Serum creatinine raised| in our EHR, which it combines with information coming from our LIMS, which has provided the LOINC^® code 10851-4 (Escherichia coli O157:H7 [Presence] in Stool by Organism). Based upon this information, the CDSS suggests a diagnosis of 111407006 |Hemolytic uremic syndrome| (HUS). The SNOMED CT diagnosis can also be mapped to ICD-10-CM code D59.3 (Hemolytic-uremic syndrome) for billing
(See also SNOMED CT (G. Wade, 2013)).

An example of clinical decision and process support for an appendicitis, based upon SNOMED CT and LOINC^® (it is a simplified example). Acute appendicitis is the most common abdominal emergency requiring emergency surgery (D.J. Shogilev, 2014). A patient (male, 15 years) sees his family physician with (SNOMED CT) 422587007 |nausea|, 422400008 |vomiting|, 79890006 |loss of appetite|, 386661006 |fever|, 14760008 |constipation|, 116289008 |abdominal bloating|, 249504006 |flatulence|, 01754002 |right lower quadrant pain|, 35611005 |rebound tenderness | and 73307001 |McBurney's sign|. The family physician refers the patient to the local hospital, with a suspicion of 74400008 |appendicitis|, accompanied by a safe and secure digital transfer of his medical record by means of a standardized digital referral letter, which in this case does not contain any comorbidities. Medical data are encrypted and transferred by means of the International Patient Summary (IPS), which includes the most important clinical facts required to ensure safe and secure healthcare (standardized digital referral letter). It does not matter which EHR the family physician or the hospital uses, both systems can handle the structure and content (meaning) of the standardized digital referral letter. As the system of the family physician and the hospital both use SNOMED CT, the data can be understood by the hospital EHR, without the need for copy/paste of text by a physician. The emergency physician and registered nurse can see the nausea, vomiting, etc. displayed by the SNOMED CT enabled Electronic Health Record (EHR). A diagnostic ultrasound (graded compression technique) points to a appendicitis. Lab results from the LOINC^®-enabled Laboratory Information Management System (LIMS), show an increase of white blood cell count (6690-2, Leukocytes [#/volume] in Blood by Automated count), neutrophil percentage (770-8, Neutrophils/100 leukocytes in Blood by Automated count), and C reactive protein (14634-0, C reactive protein [Titer] in Serum or Plasma)).

The Alvarado score points to a very probable acute appendicitis. The international, evidence-based, SNOMED CT and LOINC^® enabled, Clinical Decision Support System (CDSS) suggests a diagnosis of 85189001 |acute appendicitis|, which the ER physician confirms. The ER physician activates the evidence based clinical pathway for a laparoscopic appendectomy of the appendix. In the meantime, for risk stratification, the CDSS provides several assessment scales (SCTID: 273249006), such as the 273270000 |ASA physical status classification| and the 762713009 |Charlson Comorbidity Index| (CCI). The medication system also takes care of 430193006 |medication reconciliation|. The clinical process support system (CPSS) and Enterprise Resource Planning (ERP) system manage, monitor and optimize the hospital resources required for the 6025007 |laparoscopic appendectomy|. The pathology report confirms the diagnosis of a 85189001 |acute appendicitis|. The physicians and nurses involved in the process get feedback on the process as it evolves. After the patient is released from the hospital, the team gets feedback about the entire process for evaluation, learning and can make suggestions for improvement if necessary
(See also Decision Support with SNOMED CT and ED pathway for evaluation/treatment of child with suspected appendicitis and Inpatient pathway for the evaluation/treatment of the child with appendicitis and Comorbidity).

Afterwards the EHR and billing and reimbursement system take care of mapping 74400008 |appendicitis| to ICD-10-CM code K35.80 (unspecified acute appendicitis), 6025007 |laparoscopic appendectomy| to ICD-10-PCS code 0DTJ4ZZ (Resection of Appendix, Percutaneous Endoscopic Approach) and WHO ICHI code KBO.JK.AB (laparoscopic appendectomy). From the ICD-10-CM code K35.80 (Present On Admission (POA) = Y) and ICD-10-PCS code 0DTJ4ZZ, a grouper classifies this into APR-DRG 225 (Appendectomy) with a severity of illness (SOI) of 1, and a risk of mortality (ROM) of 1, as there are no comborbities for this patient. The results are also being sent to the family physician, who can integrate them in his own EHR (SNOMED CT, LOINC^®). The patient can view the information in his 'Personal Health Viewer', which is also SNOMED CT enabled. The medical record now has the entry 161532008 |history of appendicitis|. After a few years our patient moves to Spain and his Spanish family physician, with his consent, sees 161532008 |antecedente de: apendicitis|. After another few years he moves to Denmark, where his Danish family physician, with his consent, sees 161532008 |sygehistorie: appendicit| in the SNOMED CT enabled EHR. The patient can always view the information in his 'Personal Health Viewer' in his own language. Informed consent, security, privacy and confidentiality of patient data are to be taken care of and should not be compromised
(See also SNOMED International SNOMED CT Browser and LOINC^® and Interactive Map-Assisted Generation of ICD Codes (I-MAGIC) and ICD-10-CM/PCS Medical Coding and ICD-11 and International Classification of Health Interventions (WHO ICHI)).

You do not have to learn the SNOMED CT Identifiers (SCTID), which you can easily retrieve from a SNOMEDT CT browser, and learning the ECL is also no rocket science. SNOMED CT provides the SNOMED CT E-Learning Platform for learning all about SNOMED CT and the Expression Constraint Language (ECL). Combining SNOMED CT with LOINC, ATC-codes (WHO), the International Classification of Health Interventions (ICHI, WHO), ICD-11 (classification, WHO) and population data (demographics), would allow for ultra large scale multidimensional exploration and monitoring of population and patient health data. Understanding the difference between a clinical classification such as SNOMED CT and a statistical classification, such as ICD-10-CM (CDC), ICD-10 (WHO) and ICD-11 (WHO) is important. Classifications such as Diagnosis-Related Groups (DRG) are being used for classifying and grouping hospital cases.

Unified Information Flow Management

Figure 15: Unified Information Flow Management.
Information follows the patient independent of an extramural or intramural setting.

Every patient and every healthcare provider is in a supply and demand combination requiring resource management and capability management. Current healthcare system suffer from vendor lock-in as they are application centered and not process and data centered also due to a lack of decent standards. Information about care demands and supply capabilities flows though the system attached to the patient and provided to the appropriete healthcare provider and institution (Figure 15). One could imagine an encrypted blockchain of medical data (Summarized Electronic Health Record) stored in a distributed database, data lake or cloud which maintains a patient's continuously-growing list of health data records. The blockchain consists of a patient's unique identifier, his complete indexed history, and a secure and encrypted link to his distributed health records (a centralized system would be to tempting and vulnerable for hackers). Each record is spatio-temporal identified (Maged N. Kamel Boulos, 2018). All patient records and history are chained together and stay attached to the individual patient. A patient has control over the permissions on whom to share his data with, such as fair use for clinical research and pharmacovigilance (Ethics in Clinical Research). In case of an emergency a physician should have secure access to life-saving information as defined by the patient and controlled by a strong identification system and ethics (e.g. diabetes, heart disease, ...) (Kate Fultz Hollis, 2016). Patients could use mobile devices to assign and restrict access permission to their data and to provide public keys. Distributed patient consent allows for fair use, clinical research and trials enable data sharing, audit trails, and clinical safety analyses (Phase IV clinical trials).

In order for data to flow through the system, participating systems should be modeled appropriately. A three level modeling paradigm consists of three layers, conceptual, logical, and physical (e.g. a entity relationship diagram (ERD) of a database). The conceptual model establishes the entities, their attributes, and their relationships. The logical data model defines the structure of the data elements and sets the relationships between them (e.g. archetype). The physical data model describes the database-specific implementation of the data model (e.g. a graph database, relational database, document database). The lingua franca of the distributed and hybridized system could be based on SNOMED-CT, LOINC, WHO-FIC, DICOM, CEN/ISO 13606 and HL7 FHIR or openEHR. CEN/ISO 13606 is a CEN and ISO standardized reference model, consisting of an archetype object model (AOM), data types, and termlist. OpenEHR is an open-source standardized reference model, consisting of an archetype object model (AOM), data types, and termlist. CEN/ISO 13606 and openEHR adhere to the two level modeling paradigm (conceptual data model, logical data model). HL7 FHIR is a specification for API's including a limited set of content models. Terminology binding specifies in archetypes and templates what codes belong in which fields. Value sets represent the permissible codes that can be used to populate a coded data item.

The clinical data handover at each transition of healthcare station (intra- and extramural) takes care of the ISBAR (Identify, Situation, Background, Assessment and Recommendation) process. This way the walls between intramural (departmental) or extramural healthcare disappear as it is no longer the physical organization of healthcare which determines the flow of data, but the demand (based on the position within the healthcare chain or web) of the patient for the appropriate type of healthcare from the cloud of healthcare service providers. Online reputation management will have to be part of this system. A decent encryption standard such as Rijndael (AES) is also crucial for the system to be safe. Blockchain technology is also capable to transform the healthcare system into a patient centered system. Blockchain technology could provide a new model for health information exchange (HIE) by making electronic medical records more efficient, disintermediated, and secure. It could avoid the Cambridge Analytica scandal to happen for healthcare. We should be aware that blockchain technology has its own advantages and disadvantages (T.K. Mackey, 2019). Blockchain technology is not a magic wand
(See also Blockchain: A tool with a future in healthcare and KSI blockchain (Estonia) and exchange of electronic health records across the EU).

Figure 16: Unified Process Flow Management between man and machine.
Information (meaning, semantics) flows through the process, independent of an analog or digital process step.

Because of the semantic and syntactic unity of systems and communication, a hybrid analog-digital process is capable of crossing the analog/digital divide, and a flexible process flow can be designed and implemented (Figure 16). The process flows through analog or digital steps, depending on the best way to move forward. There are no analog/digital process-communication silos.

Figure 17: Unified inter-system connectivity instead of incompatible "wall sockets".
Information transfer between systems by means of a safe, secure and standardized communication channel.

The interface between man and machine has to be intuitive and ergonomic, but the same goes for inter-system information transfer. Patient Data Management Systems (PDMS) should deal with safe and secure collection, integration, retrieval, and interpretation of the multi-source and multi-variant data not only in a hospital setting, but independent of the location of a patient or healthcare worker (F.A. Mora, 1993; B. Thull, 1993). Communication protocols are vital for connected healthcare systems, as protocols are to communication what algorithms are to computation. Digital healthcare ecosystem fragmentation from data stream up to semantic interoperability is a common problem (S. Sherlock, 2006; D. Kalra, 2007). The situation still resembles the global chaos in incompatible electric wall sockets (Figure 17). Network sockets, protocol stacks as part of communication protocols for safe and secure sending and receiving data between systems, have to be standardized between international healthcare systems. Open API's (OpenAPI Specification, Swagger Specification) and safe and secure digital identification systems are required. A service-oriented architecture (SOA) requires a standardized application programming interface (API) or Web API, such as SOAP (Simple Object Access Protocol) or REST (Representational State Transfer). SOAP is an official W3 protocol, which is extensible, neutral and independent of a programming model. Medical device connectivity should preferably be based on IEEE 11073 SDC (service-oriented device connectivity), which enables interoperability among PoC medical devices and data exchange between PoC devices and clinical and hospital information systems (avoid vendor lock-in). Anything which does not use safe, secure and open standards should be avoided in order to avoid process fragmentation and data lock-in
(See also Web Services Description Language and SOAP Version 1.2 Part 1 (W3) and XML Protocol Working Group (W3) and OpenAPI Specification and OpenAPI Specification (GitHub) and Swagger and ECDC (GitHub) and ECDC surveillance tools and CDC API's and Socrata Open Data API and OAuth and eIDAS (EU)).

The main problem for healthcare system interoperability and process integration is located on a higher level, the semantics of inter-system communication (meaning of message content). Unified inter-system communication can be seen as a three-layer structure with a communication layer (OSI model layer 1 to 6), application layer (OSI model layer 7), and knowledge layer (ontology), allowing for creating a distributed semantic healthcare ecosystem. The OSI model lacks an Open Terminology Layer (OTL) for an ontology. The ontology would allow for distributed cognitive extension and expansion of information processing and problem solving. There should be no system (API) or communication (meaning) silos as the healthcare ecosystem exchanges meaningful information by means of Clinical Information Models (CIMs, templates or archetypes) enabling semantic interoperability (A.L. Rector, 1991; A.L. Rector, 1993). Systems share information models (structure), terminology or concept models (meaning, semantics), and inference models/Problem Solving Methods (PSMs) (consequences and actions) for interoperability (A.R. Mori, 1998; A.L. Rector, 2001)
(See also WHO Forum on Health Data Standardization and Interoperability and ISO 13606-5:2010 (Electronic health record communication - Part 5: Interface specification) and Fast Healthcare Interoperability Resources (HL7, OSI model layer 7) and openEHR and Internet Engineering Task Force (IETF)).

Unified Resource Management

Figure 18: Healthcare supply and demand management web or healthcare exchange (HCX) for a patient centric healthcare.
Patient and care provider resources become embedded in a hybrid web containing connected healthcare units (society, hospitals, primary care, digital agents, ... ).

A healthcare supply and demand management web or healthcare exchange (Figure 18) would be capable to act as a mediator, managing healthcare supply and demand in a transparent way. Management of healthcare resources should become more dynamic, integrated and responsive. In case of emergencies this would help to save lives, while in normal situations it would optimize the allocation of healthcare resources. This will require open, transparent, safe, secure, and well-regulated on-line platforms, ExRs and data standards. A unified system should be capable to respond in a dynamic way when a patient or a situation demands a healthcare service. The information flow should be independent of either an intramural or extramural environment. The entire healthcare web is a semantic information using system, not just the healthcare exchange (HCX) in itself, but the entire healthcare system in which it is embedded (Semantic Web). The relation between patient and provider should become the essence of healthcare resource management, while the environment becomes an attribute of the relation instead of the essence as it is in an institution-centric healthcare system. The medical demand management system manages the push and pull of intramural and extramural medical resources (dynamic healthcare resource monitoring and management) (Figure 18). It should have a certified and validated registry and overview of all analog (hospitals, healthcare workers) and digital (AI agents, ..) healthcare resources and their capabilities and capacity (in case of an emergency). The system could combine individual patient health data with public health intelligence (PHI) to detect health events as (or evenbefore) they unfold in order to act as an early warning system (EWS) (A. Kahn, 2011; M. French, 2013; T.J. Carney, 2015). Digital agents could be recruited as an additional resource when their skills and capacity is required and should be certified and validated (artificial intelligence agents as an uberized service workforce up for auction). The hybrid analog-digital system should be decentralized (federated) and self-synchronizing in order to remain operational in case of an emergency. Speed should be combined with operational precision beyond what is possible with human and analog participants only. An efficient and effective OODA-loop (observe-orient-decide-act) applied to the healthcare operations process allows for an agile and flexible response in all situations. The technology to build such a system already exists. One could match supply and demand through peer-to-peer systems acting as a virtual system or use a more centralized, but open, platform or regional node approach.

The operationally centralized (collaborative) but technically federated model, resembles an healthcare exchange (HCX), acting as a broker between supply and demand (healthcare resource trading system). The HCX complements and supports the emergency telephone number system (911- (US) or 112- system (Europe), ...) in case of an emergency (heart attack, stroke, epidemic, natural disaster, terrorist attack, ...). Different healthcare platform models are possible depending on the level of integration and intelligence of the healthcare system, such as hosting of healthcare resources where the platform doesn't get involved in the patient to provider transaction itself, active management of healthcare transactions where the platform fosters trust among patients and providers to facilitate a larger number of transactions and finally platform governed healthcare transactions where the platform sets one or more contractual terms (SLA, outcome) for the healthcare transaction and exercises control over the performance of the transaction. A centralized system resembles an on-line platform or broker such as Airbnb, Uber, Alibaba, Tencent, Amazon or Booking.com, but it is a public, safe, secure, well-regulated, open and transparent broker of healthcare services (data are not to be sold to the likes of Cambridge Analytica, the social quantification industry, private enterprises). Standards will need to be set for safety, security, efficacy, and fair use of health and medical data (socieconomic, clinical, laboratory, ...) for research and learning to improve management and coordination of healthcare requests (fair-use exemption, anonymisation and patient empowerment). The resources present and available for providing care, should be summoned for service in a dynamic way by a system which is capable to manage the human and non-human resource pool in a healthcare providing entity. The system should have information on the availability of resources (ER, OR, ...) in case of an emergency, a natural disaster, terrorist attack or any other crisis situation. The system operates as an Incident Command System, (ICS), which is EDXL, NATO STANAG, GDPR and SNOMED CT compliant, managing medical surge capacity (hospitals, ER, OR, ICU, laboratories, ...), assessing need and locating (GIS data) and in case of an emergency (FTM, fleet tracking and monitoring, Performance Monitoring and Profiling). The healthcare system should be resilient in case of an emergency or natural or man-made disaster (Olushayo, 2017; Kruk, 2017 and Alliance for Health Policy and Systems Research). Surge capacity should be back-propagated into the supply chain and production capacity (surge logistics and production capacity). Backpropagation (BP) of increased demand into the supply chain should determine supply chain risk and provide the system with effective decision support to carry out risk management of the (healthcare) supply chain ('what if' scenario, low frequency & high risk) (I am using BP in a somewhat different meaning here). Going backwards into the supply chain until the 'Customer Order Decoupling Point' is reached, will clarify the need for locating strategic stock management (inventory investment) in case of a surge in demand in order to deal with production lag and surge capacity (J.A. Carlson, 1973; Supply Chain Disaster Preparedness Manual, Hazard Vulnerability Analysis, Emergency Operations Plan). It will require transnational coordination in space (geographic) and time when dealing with a natural or man-made disaster or pandemic wave (production capacity, advanced logistics, facilities, human resources, ...). In case of a request from a general practitioner (GP) or patient it should be able to guide the GP or patient to an appropriate healthcare resource, while in case of an emergency it should be capable to guide (summon) the appropriate resource(s) to the patient(s) (e.g. app 112 BE). The system for regular healthcare and emergency healthcare are integrated, so the emergency system is fully aware of resource status, location and availability when needed (capacity and capability management system, Common Registry of HealthCare Actors and Healthcare Service Level Agreement). The system should be capable to analyze and optimize its response in regular and in crisis situations as a learning system (PDCA cycles). High quality and standardized point-of-care data should be part of the information the system can use to manage resources and improve processes (e.g. SNOMED CT, LOINC, GIS data, capacity, capabilities, ...). Phone calls or faxes won't do in case of a sudden rise in demand, although non-digital backup systems may be needed in case of a collapse of digital communication systems as part of a Healthcare Continuity Plan (HCP) and Hospital Preparedness Program (HPP)
(See also OASIS Emergency Management TC and Incident Command System (USA) and Federal Emergency Management Agency (FEMA) and Emergency Response Coordination Centre (ERCC, EU) and Incident & Crisis Management System (ICMS, Belgium) and Data Analytics with SNOMED CT).

Figure 19: Slow and fast surge capacity in case of emergencies and disasters. Either the emergency (e.g. epidemic) is slowed down to the capacity of the healthcare system and/or the healthcare system is ramped up to meet the demands of the emergency.
Having an intelligent system integrated into the healthcare system would allow a faster (capacity, efficient) and better (capability, effective) response to emergencies (epidemics, terrorism, natural disasters, and war).
Building standardized (semantic) communication (CCS), CDS and CES into a (global) healthcare system would also allow for rapid implementation of best practices.

An emergency resembles a ripple effect, which propagates through society as a wave or tsunami (amplitude or scale versus propagation speed). The system should provide simulation and modeling tools for creating effective crisis/emergency response plans with regard to amplitude and propagation speed (medical surge). The system could run simulations to detect weaknesses in the emergency response capabilities of a hospital, city, region or even a country (e.g. automated and intelligent Health Emergency Preparedness Self-Assessment (HEPSA)). Simulations and exercises would allow to prepare hybrid teams to respond effectively (capability) and efficiently (capacity) to complex and large scale emergencies (Figure 19). Walkthroughs, tabletop exercises, functional exercises and full-scale exercises allow for optimizing processes in regular situations and in case of emergencies. The system acts as a facilitator guiding participants through a discussion or rehearsal of one or more scenarios. The status, location and capabilities of healthcare facilities and healthcare workers are available for the system, so it is capable of a rapid response in case of emergencies. A Unified Resource Management (URM) system keeps track of each type of resource and its characteristics and in this way is capable to optimize the response to patients entering healthcare entities in an unplanned way (about 30 percent of cases on average). The activity itself should generate its own data, so no overhead is being created to translate activity into data. The degree of monitoring, tracking and tracing of patients and healthcare resources will depend on the healthcare situation, location, emergencies and the complexity of coordination required (vary in time, granularity and location). The system should be capable of day to day operation and surge capacity in case of an emergency. This should minimize the slow response rates due to the complexity of coordination in for instance emergency rooms. Everything and everybody is considered to be a resource with its own characteristics (semantic tags) and is actively assisted in its work planning and execution. The process of information gathering and treatment providing develops into a hybrid system consisting of an interaction between physical and digital data extraction and treatment generation. Both clinical decision support systems (CDSS) as clinical treatment support systems (CTSS) such as robots and bots interact with patients and (human) healthcare providers, building at transparent and unified system. The system will have to be a learning system with the capacity for improving its performance. This way the unified healthcare organism intertwining human and non-human resources in a dynamic way could become a reality.

Such a virtual, agent driven, system also leads to disintermediation or the removal of analog and digital middlemen (platforms) from the healthcare supply chain. It has the ability to connect analog and digital healthcare resources to each other and to patients, without or at least much less intermediaries (lean care and revenue cycle management). It could lead to an "uberisation" of analog and digital healthcare resources, with the revenue streams being siphoned away from present day (intermediary) healthcare organizations now acting as analog middlemen, when certified and validated digital middlemen are allowed the replace them. Blockchain technology would be able to avoid the need for centralization or could complement the system. Blockchain technology has its own advantages and disadvantages. A blockchain network would enable disintermediation with regard to payment and insurance processing with (transnational) predefined smart healthcare contracts. Blockchain-based smart contracts, linked to the eID of healthcare providers and patients, could manage and coordinate operational execution and billing of clinical pathways (K.N. Griggs, 2018). Integrated smart devices (IoT) could be linked with smart contracts and write records of all relevant events on the blockchain and the medical record of the patient, either PHR and/or EHR (P. Zhang, 2018). Of course, this would require a substantial redesign of our current 'dumb', convoluted and passive healthcare process, incompatible EHRs and billing systems. Our healthcare process would need to be deconvolved from its administrative overhead and intransparency. Combining blockchain and the IoT would also allow for structural health monitoring (SHM) to enable transparent health information sharing among involved parties and autonomous decision making (B.W. Jo, 2018; K.N. Griggs, 2018). It will be important to deal with privacy, confidentiality and the resiliency of blockchains to possible attacks (Z. Alhadhrami, 2017). As healthcare applications of blockchain demand stringent privacy, this would also require zero-knowledge proofs (J-J. Quisquater, 1989). In the process, beware of resistance to change and drain of income from the (public) healthcare system to the (private) stock market ( Smart contracts in healthcare and Does Blockchain Have A Place In Healthcare? and The decline of America’s middle classes and Amazon: Health Care disruption by disintermediation and P. De Philippi, 2016 and M. A. Engelhardt, 2017).

The legal and ethical consequences of such a system, either based on platform technology or blockchain technology are enormous. Big data related to an entire healthcare ecosystem, not only refers to (explicit) data, but also to (implicit) metadata and digital traces (digital footprint, digital shadow, data exhaust), which is a cause of information asymmetry, mass surveillance , and privacy issues. In Europe we have to take care of Article 7 and 8 of the European Charter of Human Rights in order to deal with the protection of and respect for a person's private life (ePrivacy, article 7) and personal data (GDPR, article 8). Coupling a digital ontology, such as SNOMED CT and LOINC, with healthcare process information and user metadata would create a healthcare panopticon of unknown dimensions. Human dignity, safety, security, confidentiality and privacy of operational and medical data are important considerations when designing, implementing and operating such a system. The system should be humane and usable for patients and healthcare-workers, not an instrument of data monetization in the hands of data capitalists. The algorithmic fallacy and bias underlying so-called intelligent systems has to be avoided by carefully integrating human and digital participants in an integrated system using checks and balances to avoid runaway decisions and actions. Human judgment should be capable to complement so-called algorithmic objectivity. A unified system equipped with one-sided surveillance algorithms could be used as a digital information panopticon. Such an intelligent system would allow for a constant computer-based scrutiny of healthcare workers and patients as they participate in healthcare processes. As such it becomes part of the emerging culture of surveillance. The social quantification industry acts as a data colonialist, capitalizing on social data and increasingly on health data (N. Couldry, 2019; F. F. Wherry, 2019, pp. 274-275). Invasion of the healthcare system by the social quantification sector would lead to social bias and discrimination and behavioral influence, which would have devastating effects on healthcare workers and their patients. It would open the doors to financialization of healthcare, data capitalism and capital accumulation in healthcare without limitations (C. Kyung-Sup, 2012; F. Stein, 2018). Platform-technology can become a virtual enclosure, similar to propriety standards and physical enclosures, causing monopolization of information, communication and monetization (application platform lock-in). Proprietary platform technology and standards in healthcare, such as we see with social media, leads to the balkanization of healthcare and dividing the healthcare ecosystem into neo-feudal fiefdoms with the users (customers) as neo-serfs. Commercial, proprietary platform-technology poses the risk of diverting the benefits and profits outside the healthcare system itself. The system could achieve a monstrous commercial efficiency, which leaves no room for humaneness.

Tech companies mainly focus on secondary (commercial) use of health and healthcare data and not on improving the safe, secure and open primary use of data at the PoC or for exchanging data between care providers (process), as this has little added value for them. Making money from health data transactions is highly regulated and less lucrative than monetizing data for secondary commercial use (private profit, commodifying data for sale, selling and monetizing data assets, marketing and commercial service-development purposes) (A. Geissbuhler, 2013). Without first digitally standardizing and integrating (safe and secure) the primary process (PoC), improving patient care or clinical research (FAIR secondary use) will get nowhere (single-source data entry, semantic interoperability) (A. El Fadly, 2011). Exploitation of health data allows for a monetization and profitability, which goes beyond the physical barriers of real-world (healthcare) processes. It could uncouple data profitability from the actual performance of the underlaying healthcare process, thereby creating a parallel for-profit data-based business, which uncouples itself from patient and healthcare-worker reality (data as such do not equal knowledge and understanding). The relative (hyper-) elasticity of digital resources outperforms the relative inelasticity of natural (human) resources and allows for revenue expansion beyond the boundaries of real world activities. Native digitized processes suffer much less from diseconomies of scale than analog or human-processed processes. The intrinsic variability of human behavior and cognitive limitations, limits the capacity and capability of a double anthropocentric process such as healthcare. The more you convert either patients or healthcare workers into processable data, the more your can break the tight covariation of process performance to human processability (patient) and performance (healthcare workers). Digital data performance instead of primary process performance disruption only refers to disrupting the profit-barriers, not the process-barriers hindering healthcare workers and patients (financialization of healthcare). The Point of Profit (PoP) is being uncoupled from operational performance at the Point of Care (PoC). The commercial value and profit generated in such a way, only represents itself, no underlying physical-biological reality, but it could be used to manipulate real-world behavior of the hybrid analog-digital system. From a valuable assistant and digital "colleague" it could become a kraken and a "weapon of math destruction", developing into an opaque, unregulated, and incontestable black-box dehumanizing healthcare.

The digital industry still acts as if they operate in a wild no-mans land, or as John Perry Barlow told the WEF in 1996 with the Declaration of the Independence of Cyberspace: "Governments of the Industrial World, you weary giants of flesh and steel, I come from Cyberspace, the new home of Mind. On behalf of the future, I ask you of the past to leave us alone. You are not welcome among us. You have no sovereignty where we gather." The declaration resembles the archetypal theme of the "sacerdotium" (digital spiritual church) bound to rule over the "regnum" (analog temporal state). This declaration is also the digital equivalent of the Treaty of Tordesillas (1494 CE) or the doctrine of discovery (Age of Discovery). The 'Declaration of the Independence of Cyberspace' considers the vast 'digital continent' a terra nullius to be conquered, colonized and exploited without any interference by the analog 'savages'. It is their version of manifest destiny "to overspread and to possess the whole of the digital continent which Providence has given to them for the development of the great experiment of an independent cyberspace". Their 'libido dominandi' or 'will to dominate', will cause havoc as the digital quantification industry invades healthcare with their ruthless commercial agenda, which commodifies both patients and healthcare workers (narrow focus on commercial value quantification capabilities). Digital colonization allows for breaking the tight relation (covariance) between space and time which exists in the analog realm and to create boundless digital value, which has no equivalent in analog reality. Digital value (New World gold) can then be transferred back into analog reality to their shareholders, where it can be used to conquer a vast analog territory and assets, or going from 'bits to its'. "There are two ways of conquering a foreign nation. One is to gain control of its people by force of arms; the other is to gain control of its economy by financial means." (US Secretary of State John Foster Dulles, in the 1950s) Their digital armies in cyberspace allow them to conquer vast stretches of the analog domain once they transfer their expanded digital territory (profit) into analog assets. They succumb to the psychological addiction to corporate (economic) expansion, a psychological (instinctive) substitute for triumph in war and territorial conquest for the "merchant caste". Tech companies create a "pax internetica", in which their "Digital Empire" becomes a hegemonic power and adopts the role of a "global digital policeman". They divide the world according to the principles of the Peace of Westphalia: "whose realm, their data".

The digital conquest and colonization of the 'healthcare continent' (Terra Nova) by the ruthless commercial exploitation of these digital locusts or conquistadors, will create a 'fait accompli' and leave patients and healthcare workers with a highly marketized, monetized and profitable, but dehumanized wasteland: "Auferre, trucidare, rapere, falsis nominibus imperium; atque, ubi solitudinem faciunt, pacem appellant" (Publius Tacitus). They behave as socioeconomic imperialists or hegemons: "When economic power desires to be left alone it uses the philosophy of laissez faire to discourage political restraint upon economic freedom. When it wants to make use of the police power of the state to subdue rebellions and discontent in the ranks of its helots, it justifies the use of political coercion and the resulting suppression of liberties by insisting that peace is more precious than freedom and that its only desire is social peace." (Reinhold Niebuhr, Moral Man and Immoral Society: A Study in Ethics and Politics). "While free markets tend to democratize a society, unfettered capitalism leads invariably to corporate control of government." (Robert F. Kennedy)
(See also D. Lyon, 1994; D. Lyon, 2001; D. Lyon, 2018; S. Zuboff, 1988; B Grosser, 2014; S. Zuboff, 2019; C. O'Neil, 2017; N. Couldry, 2019 and What does the panopticon mean in the age of digital surveillance? and An apple a day keeps the algorithm away).

Global Unified Resource Management for dealing with global challenges

Our world is a biological village

It is not only a matter of depth of healthcare process integration, but also of width of process integration and capability (monitoring, robustness, resilience, actability). Nowadays we are dealing with a global challenge, an aging population, chronic and infectious diseases acting on a global scale. "Communicable" and "non-communicable" epidemics, pandemics and dealing with natural or man-made disasters, requires and efficient and effective response (analysis, coordination and action). Global phenomena not only demand global monitoring but also coordination and defragmentation of action (control, actability) (L. Hufnagel, 2004; B.S. Cooper, 2006). A quick, coordinated and focused reaction is essential to inhibiting the global spread of an epidemic in order to avoid "divide et impera" or "emptying the ocean into a thimble". Sadly enough we are political, emotional and cognitive incapable to deal with global threats and phenomena in a coherent and consistent way (νόμος versus φύσις, δόξα versus ἐπιστήμη). Our political, social, economic, organizational, emotional and cognitive capabilities, span and scope of control, cannot deal with this global scale of phenomena (complexity, experience, workforce capability, geographic dispersion, administrative tasks and support, organizational environment) (D. Doran, 2013; D. Jones, 2013, paralysis by analysis, cognitive dissonance, ...). We have to redesign healthcare systems in order to deal with non-communicable disease (NCD) and new pathogens on a global scale (actability). We are entering a new 'Age of Pandemics' in which globalization, life style, environmental degradation and climate change will combine to produce new "communicable" and "non-communicable" epidemics and pandemics (A.M. Prentice, 2006; A.S. Barnes, 2011; S.S. Morse, 2012; M. Harrison, 2016, p. 128-146; S. Bevins, 2016). Globalization, globalized travel and trade, and climate change are causing a global exchange of pathogens which dwarfs the Columbian exchange in extent, speed and impact (virgin soil epidemic). We are now exposed to the same phenomenon, on a global scale, which decimated native American populations after the European invasion of the Americas. The increasing concentration of people in cities and megacities creates a high-risk and highly vulnerable environment for spreading infectious diseases. Global supply chains increase supply chain risks and exposure to natural disaster, pandemics, etc. (L. Faught, 2006). Globalization of production, consumption and life styles (McDonaldization) creates a boiling frog or camel's nose phenomenon (sorites paradox, creeping normality): "Principiis obsta, sero medicina paratur: cum mala per longas convaluere moras" (Ovidius, Remedia Amoris, 91-92). Globalization resembles loss of bio- and genetic diversity, which increases our vulnerability to "invasive events", which are capable to exploit the global weakness of our globalized ecosystem. "Non-communicable" disease such as obesity, diabetes and "communicable" diseases do not require a visum or work permit, and they board planes, ships and public transportation at no cost and we provide them with almost unlimited resources and logistic support (R.F. Grais, 2003; B.S. Cooper, 2006; P. Bajardi, 2011).

While our globalized economy and jet set (super-spreader) enjoys the benefits of globalization, the poor (uninsured, precariat) pay the highest prize of the "communicable" and "non-communicable" disease burden. A deterritorialized global economy with a territorialized healthcare system does not work. We cannot have a globalized economy and interconnected global community without a globalized healthcare ecosystem and decent and affordable universal public healthcare. This is not a "socialist" idea, but it is about the robustness and resilience of the health system and society (P.D. Marghella, 2005; J.G. Bartlett, 2006; C. Nemeth, 2008; S. Abimbola, 2018). While healthcare and social security were developed in the past to counter the rise of socialism (Bismarck) or communism (post-WW II), now we are faced with a socioeconomic and health challenge that demands a more substantial transformation of the structure and organization of society itself. The production of health problems and diseases has to be dealt with, which demands a different approach to the externalities of socioeconomic activities.

Hope for the best and prepare for the worst

Infectious diseases operate globally, just as criminal organizations or cybercrime, but we still deal with healthcare systems, data, and early warning systems in a very primitive and fragmented way. During epidemics and pandemics we can also witness the consequences of ignoring International Patient Safety Goals (IPSG) with regard to the risk of spreading health care-associated infections. Integrating global, regional, or even national healthcare processes seems to be outside of human reach. Intelligent surveillance and process support systems for non-communicable disease patterns, disease vectors and infectious patterns and agents would save lives and avoid potential non-communicable and communicable "pandemics" with devastating outcomes. It is certainly more useful than monitoring shopping behavior or 'likes' on social media, although Google, Facebook and Amazon nowadays probably have better social, behavioral and economic surveillance and early warning systems than most national healthcare systems. Our preparedness for nuclear war and (bio)terrorism is better than for a pandemic
(See also Budapest Convention on cybercrime and After Ebola and Zika, Most Countries Still Not Prepared for a Pandemic (World Bank, 2017) and Pandemic (USA) and Community Mitigation Guidelines to Prevent Pandemic Influenza (USA, 2017) and Why Do We Need Standards? and Implementation of the International Health Regulations (2005): report of the Review Committee on the Functioning of the International Health Regulations (2005) in relation to pandemic (H1N1) 2009 (WHO) and Vector-borne diseases (WHO) and European Centre for Disease Prevention and Control (ECDC), Coronavirus Disease 2019 (COVID-19) (CDC) and West Nile virus (CDC) and Ebola virus disease (WHO) and Ebola (EU) and Bill Gates: The next outbreak? We're not ready (2015)).

The health risks of global travel are widely known, as the European invasion of the Americas and the slave trade brought smallpox and yellow fever to the Americas (D. Alden, 1987; J.P. Chippaux, 2018). Infectious diseases, when introduced on virgin soil, have decimated indigenous peoples of the new world (from a European perspective), ie the peoples of the Pacific and the Americas (P.J. Roberts-Thomson, 2014). A globalized world can lead to the spread of infectious diseases at an unseen speed, and cause a global disaster, compared by which the Spanish flu was a "local epidemic" (e.g. Airport malaria and A. Wilder-Smith, 2005). Airborne, food-borne, vector-borne, and zoonotic infectious diseases can take an airplane or board a ship too and lead to rapid spread of infections (A. Mangili, 2005; A.J. Tatem, 2006; Institute of Medicine (US) Forum on Microbial Threats, 2010; A. Findlater, 2018). Airplanes and ships act as disease vectors, similar to mosquitoes but faster and on a global scale (L.P. Lounibos, 2002; O. Floerl, 2005; A.J. Tatem, 2006; Z. Huang, 2012; A. Findlater, 2018). International transport, and international travel facilitate the spreading of disease vectors such as the (Asian) tiger mosquito (Aedes albopictus), the yellow fever mosquito (Aedes aegypti), which are vectors for the transmission of infections such as yellow fever virus, dengue fever, and Chikungunya fever. Arboviruses, such as West Nile virus (WNV), Usutu virus (USUV), and Rift Valley fever virus (RVFV), are spreading (M. Pfeffer, 2010). In 2015 the WHO proposed the names of several potential viruses that can cause the next outbreak and pandemic (Q. Zahra, 2021)
(See also WHO publishes list of top emerging diseases likely to cause major epidemics (WHO, 2015) and WHO R&D Blueprint and WHO Global Outbreak Alert and Response Network (GOARN))

Illegal trafficking in (exotic) animals causes the global spread of zoonoses. Even legal animal trade is a cause of outbreaks of invasive infectious diseases, such as monkeypox (M. Enserink, 2003). Importations of hemorrhagic fever into non-affected countries already happen (J.A. Bryan, 1977). Infectious diseases such as Crimean-Congo hemorrhagic fever (CCHF) spread over neighboring countries and geographically distant locations like any other invasive species (J.R. Spengler, 2019). Climate change and habitat destruction will also facilitate the spreading of infectious diseases to new regions (C.A. Phillips, 2020). SARS, MERS and SARS-CoV-2 are examples of coronaviridae causing a severe epidemic (V.C.C. Cheng, 2007; Y. Fan, 2019). "Nightmare" bacteria or "superbugs" (VRSA, CRE, ...), which are difficult or impossible to treat because they are resistant to even the last-resort antibiotics, are also on the rise (A. Morris 1998; B. M. Kuehn, 2013). We are also confronted with re-emerging diseases on a global scale (J. Lederberg, 1996; M.A. Winker, 1996)
(See also Making Peace with Nature is the Defining Task of the 21st century (UN Secretary-General, 2 Dec. 2020) and Re-emerging diseases: gone today, here tomorrow? and antimicrobial resistance (AMR)).

Dealing with black swans and zero-day events

When a health and healthcare crisis emerges we usually react as if we encounter a black swan, something which could not happen. What happens resembles a zero-day event, which catches us by surprise and exposes the design flaws of our health and healthcare systems. Our health and healthcare system is unprepared and unable to deal with an external shock, as already in regular mode it runs at the limits of its capacity and capability. Our epistemological and ontological limitations and our paradigmatic "imprisonment" does not allow for conceiving and developing a system capable of dealing with sudden challenges which overwhelm our sociopolitical system. It is as if we have been institutionalized by the conceptual space in which we have constructed the model of our living environment. Sh*t happens, prepare for it.

A well designed, integrated, robust and resilient healthcare system strengthens our defence in depth.

Our "non-communicable" or "communicable" enemies take no hostages or prisoners of war (asymmetric warfare). A gated community is no permanent solution for a global health threat. Although the "οἱ πολλοί" may be more vulnerable, Nature's bacteria and viruses, in the end, will not spare the "οἱ ὀλίγοι". You cannot resist a global bacterial or viral blitzkrieg by digging trenches, and trying a "Munich Agreement" with a natural threat and emergency won't do against a "furor infectiosa", "furor calamitas", or when "Hannibal ante portas". A natural disaster or epidemic does not negotiate about the Geneva Conventions or any other human right of its victims. Nobody likes a Cassandra or the scientists and epidemiologists acting as "party-poopers" disturbing the "globalized party" (P.D. Marghella, 2005; M.T. Osterholm, 2005; J.G. Bartlett, 2006; J.F. Brundage, 2006; H.V. Fineberg, 2014). The way we tend to look at fast-spreading disasters and health threats is as if looking in a broken (healthcare) mirror, which provides us only with a fragmented and distorted image. Both non-communicable and communicable "pandemics" are a political, social, economic and public health issue, and we should pay more attention to prevention and resilience, instead of only dealing with them in the usual disaster-response mode: "Si vis pacem, para bellum" or "expectatio armata" and "you get the politics and politicians you deserve"
(See also Pandemic preparedness (WHO), A world at risk (GPMB, WHO, 2019) and Pandemic preparedness and health systems strengthening (World Bank) and Welcome to the Age of Pandemics and Asia-Pacific Alliance for the Control of Influenza (APACI) and Influenza pandemic preparedness (ECDC) and R&D Blueprint (WHO) and Emergency Preparedness and Response (CDC) and Pandemic (CDC) and One Health Initiative and Resilient Health Care).

Analyzing and fighting diseases and pandemics or dealing with natural disasters or man-made disasters in a certain way resembles analyzing and fighting global criminal organizations and terrorism, as there is no single and clear front as in a traditional war. Containment of a natural or man-made disaster has to be part of the measures taken to limit the damage to society and to protect our people. Infectious diseases have self-propagating capabilities, which have to be dealt with in order to contain and mitigate an outbreak. Analysis, strategy and dealing with diseases requires an integrated and dynamic system approach, not a (static) Maginot Line. The response to natural or man-made disasters requires rapid response teams (RRTs), advanced logistics, and resources resembling military actions and capabilities. The impact of an epidemic and pandemic is related to the combination of its case fatality rate (CFR) and infectivity or average number of people infected by each sick person ( basic reproduction number, G.N. Milligan, 2015, p. 310, pandemic severity index). Depending on the combination of case fatality rate and infectivity, healthcare systems, economies and entire civilizations collapse (e.g. Spanish flu, native American disease and epidemics). Seasonal influenza-associated respiratory hospitalizations already impose a heavy burden on health systems in the Americas (R.S. Palekar, 2019). The 1918 H1N1 pandemic virus (Spanish flu) spread across Europe, North America, and Asia over a 12-month period resulting in an estimated 500 million infections and 50-100 million deaths worldwide, of which ~50% of these occurred within the fall of 1918 (N.P.A.S. Johnson, 2002). The Spanish flu killed more people in 25 weeks than AIDS did in 25 years ( The Five Deadliest Outbreaks and Pandemics in History). Governments and health care systems remain inadequately prepared for the impact of a 1918-like severe influenza pandemic (medical surge) (P.D. Marghella, 2005; J.G. Bartlett, 2006; B. Jester, 2018; T. Kain, 2019; M. E. Nickol, 2019). An influenza pandemic or any pandemic with a high basic reproduction number, would cause the global healthcare system and the economy to collapse. Most hospitals, healthcare workers (HCWs) and intensive care units (ICU) during flu season are already at capacity and a more severe epidemic (high R₀ and ICU admission rate) will push them over capacity (P.D. Marghella, 2005; M.T. Osterholm, 2005; J.G. Bartlett, 2006; C. Terwiesch, 2011; L.T. Tierney, 2014; S. Einav, 2014).

The development and production of a vaccine for a new virus takes about 12 to 18 months, so we have to rely on other measures in the early stages of an epidemic with a new virus. We have to get through the first 12 to 24 months of a pandemic with containment and mitigation measures, before we develop, produce and distribute a vaccine or any drug which deals with the infection (W. Ebstein, 1869, p. 41; M.T. Osterholm, 2005; S. Riley, 2007). Otherwise we will have to look back in horror like the Angelus Novus staring to the catastrophe which keeps piling wreckage upon wreckage and hurls it in front of his feet. As the basic reproduction number (R₀=βγ) depends upon contacts per unit time producing new infections (β) and the mean infectious period (γ); reducing the number of contacts per unit time (social distancing), reducing the proportion of contacts that produces infection (hygiene, International Patient Safety Goals (IPSG), Personal Protective Equipment (PPE)), and isolation (quarantine), can be used to reduce the progression of infections. These measures reduce R₀ and slow down spreading of an infectious disease (basic model). A combination of social distancing, personal, domestic and community hygiene, travel restrictions, surveillance, exhaustive contact tracing, quarantine, postponing non-essential operations, and supportive therapy have to deal with a new virus, and remain relevant even for a 21st century and global problem. Once a healthcare system runs out of resources and/or surpasses its surge capacity, (critical care) triage criteria will have to decide who will be treated (M.D. Christian, 2006; L. Blanch, 2016; J.W. Choi, 2017). The lead time, production capacity and vaccine supply chains and logistics do not allow for a rapid response in case of a global emergency. Vaccine production requires complex production methods, meticulous quality control, and reliable distribution channels (J. Smith, 2011). Slowing down the spreading of an infection and decreasing the epidemic peak or 'flattening the epidemic curve', matches the outbreak to the surge capacity of the healthcare system, our industry (production), supply chains and logistics (supplies, medication, (human) resources, transportation) (H.L. Mills, 2014). The entire population should act responsibly in order to 'flatten the epidemic curve' (civic responsibility). The same problem of inadequate surge capacity goes for terrorism and war, but in that case there is also an active destruction of our healthcare infrastructure and resources (e.g. Global Action Plan for Influenza Vaccines (GAP)).
(See also Emergency cycle (WHO) and Public health emergency (USA)).

Do we have to deal with health and healthcare policy or politics? The principles of health and healthcare policy and politics are closely related to a "philosophy of human affairs" (Nicomachean Ethics X.9, 1181b15). While politics is about government and its activities, a policy is about a plan and a set of rules or principles that guide political decisions. Policy deals with the underlying ontological (political objects) and metaphysical (rules) assumptions, while politics deals with the ontic level. Public health policy is about achieving a (political) goal and the identification, understanding and providing the means of reaching this goal. The Canadian politicologist Vincent Lemieux wrote that the term public policy is understood differently by government actors and by university researchers, (sometimes) leading to a Babylonian confusion (V. Lemieux, 2002, p. 1-2). Government actors tend to delimit the actions they consider to be public health policies. According to Thomas Dye, "public policy is whatever governments choose to do or not to do" (T.R. Dye, 1987, p.3). Government actors see public policies as branching off into programs, projects and activities ("initiativitis"). According to William Jenkins, public policy is "a set of interrelated decisions taken by a political actor or group of actors concerning the selection of goals and the means of achieving them within a specified situation where those decisions should, in principle, be within the power of those actors to achieve" (W.L. Jenkins, 1978, p. 15).

In liberal democracies, there is always a tension between democratic government (liberal constitutionalism) and democratic society (public democratic life), the so-called democratic paradox (ᾰ̓πορῐ́η). It refers to the Platonic distinction between a republic (power of written law) versus a democracy as a form of individual and social life (δᾶμος) (Plato, Πολιτεία, book VIII; Νόμοι, book III) (M. Crozier, 1975). There is also the tension between personal relations (kinship) and impersonal (abstract) relations, which requires the squaring of the circle in the political construction. The conjunction or disjunction between state and nation is a source of tension. As a result politics is always a more or less irrational, chaotic and even catastrophic process, depending on the relation between spatio-temporal pressure (frequency and amplitude of change) and the paradigmatic, cognitive, analytical, strategic and operational capability and capacity of policy makers, committees, commissions and government agencies. The emotional and cognitive limitations of the human species put limitations on what can be achieved. For the executive branch, we could even take into account the Shannon-Hartley theorem with regard to the capacity for reality and information processing by a political entity, as the (operational) political process collapses in times of high frequency and amplitude of change (political bandwidth overflow). There is also the risk to resolve issues by throwing money at problems or special interest groups without much thought (pecuniarism), as opposed to actually solving problems or working on the fundamental causes of problems (D.A. Stockman, 1975; K.A. Shepsle, 1981; D.P. Baron, 1991). Another pitfall is uncritical technological optimism, by believing that throwing technology at any problem will solve it (L. R. Cohen, 2002, pp. 1-3). Western "άκοσμισμός" (unworldliness) also prefers theoretical models, and political ideologies, over everyday clinical reality
(See also Public policy and catastrophe theory and pork barrel).

There are some fundamental issues with the political process as such. We are cognitively limited in our conceptualization capabilities and capacities (ontological and epistemological limitations). Incapable of approaching reality as it is, we cannot grasp or comprehend the full complexity of its structure (statics) and processes (dynamics). Both the etic (universal) and emic (culture-bound) dimension plays a role in the development of policies. Healthcare policies nowadays differ fundamentally between a positive and post-positivist approach (e.g. κατάληψις versus ἀκαταληψία). A positivist approach embraces scientific rationality and sees policy analysis as part of the quest to uncover objective (quantifiable) knowledge (evidence-based, mathematically interpreted) versus a 'post-positivist' approach. The technical or quantitative dimension seeks to identify the optimal relationship between goals and tools, since some tools are better suited to address particular problems than others. Post-positivism or methodological pluralism, rejects any claim of an established truth valid for all (objective reality) and takes a more normative approach (S.E. Krauss, 2005). In a certain way, a post-positivist approach returns to a pre-Galilean qualitative and Aristotelian approach to reality. Qualitative and anti-quantitative reasoning avoids the burden of quantitative proof or quantifiable link to physical reality outside the philosophical or ideological framework. The first principles of any ideology are the basic assumptions that cannot be deduced any further and have to be taken for granted ("the first basis from which a thing is known" according to Aristotle, The Metaphysics, Aristotle, 1013a14-15). Arbitrary and contingent descriptive ideological or political first principles (principia neutra), without intrinsic truth value, are connected to prescriptive ones. Once you have established a set of first principles, you can play around with the way you will deal with reality within the constructed framework. Ideologies can and will ignore the aspects of life and reality which do not match the ideology
(See also is-ought problem and open-question argument).

Politics has to go beyond the frontiers of reason and science and past the 'Pillars of Hercules' into uncharted territory: 'hic sunt dracones' (ignoramus et ignorabimus). Beyond the 'Pillars of Hercules', only ideologies or political "δόξα" act as a navigation instrument. In a certain way, the demarcation line between what is negotiable or not or the 'Pillars of Hercules' location is somewhat contingent and arbitrary, depending on the political ideology and cognitive capacity and capability of the political process. Dealing with a certain degree of uncertainty and indeterminacy is part of the political process, but political ideologies also tend to ignore (scientific) facts and what is known about reality. Although a scientific hypothesis may be acepted or rejected, (unfalsifiable) ideologies are not rejected when the facts don't fit the ideology (J.P. Friesen, 2015). Unfalsifiable belief systems lower the signal-to-noise ratio (S/N ratio) when evaluating claims in belief systems versus scientific standards of evidence (J. McPhetres, 2017; D.E. Sherkat, 2017).

'Politics is nothing but medicine at a larger scale.' (J.P. Mackenbach, 2014). Due to ideological differences, political actors typically disagree on what constitutes a health or healthcare policy problem or an appropriate solution (C. Bambra, 2005; E. De Leeuw, 2014). Besides our state of knowledge about the causes and consequences of death, disease, and disability, policy actors' ideas, norms, and principles profoundly impact healthcare policies (T.R. Oliver, 2006; S.L. Greer, 2017). We not only have to deal with quantitative techniques, such as cost-benefit analysis or risk assessment and management, but also with the causes and presuppositions and the processes that led to their adoption. For instance, liberalism (Locke, Mill) quarrels with communitarianism (Aristotle, Hegel) and conservatism (Burke) quarrels with progressivism (Locke, Kant, Mill). Ideological argumentation quite often is founded on tacit, arbitrary, and even irrational principles. By accepting ideological a priori principles, axioms, or dogmas (random set of assumptions), which ideologies adhere to in order to create an illusion of understanding the world, people should agree with a political point of view. By mixing arbitrary assumptions with facts, ideologies create an illusion of objectivity but hide their ideological partiality and self-interests. Reality is filtered and convolved with our personal and political paradigms, models, knowledge, beliefs, myths and illusions, which act as a low-pass filter (LPF) and distorted mirror. Mental convolution is using a 'kernel' (mental model) to extract certain 'features' from an input (reality).

Health and healthcare policy are always tainted by our ideologies, prejudices, self-interests, and cognitive limitations. In many situations, health and healthcare policy is a mere 'flatus vocis' founded on arbitrary and contingent first principles but limited in its understanding of reality (Weltanschauung, absolute propositions, axioms, ideology, political gnosis). Discussions between political (socioeconomic) opponents in many cases are mere sophistry and power play and not a matter of pure deliberative rationality, as 'contra principia negantem non est disputandum'. It leads to a sort of Westphalian sovereignty (territoriality) between political and socioeconomic territories (analog or digital). We are in a permanent state of political flux as the confrontation with reality is a never-ending stumbling block for the ideological and political illusions of our anthropocentric perspective on reality. The exchange of political "δόξα" does not necessarily lead to "ἐπιστήμη", but only a conditional and contingent 'modus vivendi', or even merely an 'armed peace'. In modern, liberal (Western) democracy, there is not one "true" political answer for all the problems of society, only a plurality of "δοκεῖ μοι" (F. Dolan, 2000, pp. 261-276). Due to the nature of health and healthcare challenges, there is only a narrow margin for health and healthcare policy between the frontiers of science and the abyss of political soteriology
(See also A Message to the 21st Century by Isaiah Berlin on 25 November 1994).

Political decisions impact all those people and organizations living within reach of the political decision process. Health and healthcare policy development happens at multiple levels, and healthcare providers, patients, and suppliers have to operate in a fragmented political and legal environment. Besides (vertical) political fragmentation (local, regional, national, supranational), there is also the growing impact of non-majoritarian institutions (NMIs) such as central banks, constitutional courts, and international organizations (IOs), which leads to a fragmentation of health and healthcare policies. Politics's "quantum unit" is territorial (Westphalian principle), not functional or operational. We try to solve trans-territorial (functional) problems with territorial solutions. A fragmented and contingent man-made political sphere stands against the unified biotic and abiotic forces of Nature. Global (World Health Organization, WHO), regional (European Union, EU), national (USA, China, ..), and local political bodies each have their own, often disjunct, political and legal agenda. Multiple political and legal levels, each operating in their own 'first principle' universe are a cause of disjunct political and legal development. Health and healthcare policy requires political consistency by implementing Health in All Policies (HiAP). The major or principal problem is not a fragmented strategic, tactical and operational pyramid as such, but a fragmented and incongruent pyramid of principles and policies at all levels of policy-making, according to which the various strategic, tactical and operational levels have to operate (back and forth from the (inter)national to the individual level). We have a fragmented and inconsistent inter- and supra-national pile of empirical evidence and normative disciplines and arguments to work with (J.P. Koplan, 2009; G. Ooms, 2014; G. Ooms, 2015). Detachment and inconsistency of political and legal frameworks from real world people, processes and outcomes, and vice versa, leads to process imperfections. Designing and developing a complex integrated analog-digital healthcare system requires political and legal vision, consistency during change processes and high quality political development. Countries with different needs and varying stages of healthcare service development should be embedded in a global but diversified development process (coherent and consistent milestones and deliverables). Diagnostic and therapeutic process and outcome measures should inspire the political process. Left to its own, there is only the healthcare market which develops along market and profit principles. Economic (market) opportunities are increasingly driving healthcare development (profit motive), but healthcare value is not only a matter of profit and loss or (instant) consumer satisfaction. Healthcare is both a matter of 'care' (process and outcome, efficiency and effectiveness) and 'caring' (humaneness). A patient not only is a healthcare consumer, but also a person (citizen, human being). A healthcare provider, not only is a vendor of healthcare products and services, but also a person interacting with the patient as a person. Inspired by Immanuel Kant, we could say that: "Caring without care is blind, but care without caring is mere intellectual play". Healthcare policy requires a vision and a long-range view beyond 'winning the day' or 'kicking the can'
(See also eHealth at WHO and Global Observatory for eHealth (WHO) and The Helsinki statement on Health in All Policies, The 8th Global Conference on Health Promotion Helskinki, Finland 10-14 June 2013 and Transforming Health Care Takes Continuity and Consistency and Balancing Consistency and Innovation in Healthcare and World Summit on the Information Society (WSIS)).

The European problem

I apologize for my "Eurocentrism", but living in Europe myself makes me nervous about how we deal with health and healthcare.

Although I dearly admire the European ideal, we are not up to the health and healthcare challenges we are facing. What are the roots and causes of this situation? The European Union's (EU) roots go back to the fears and goals of post-war Germany and France. As Zbigniew Brzezinski once put it: "France seeks reincarnation as Europe: Germany hopes for redemption through Europe." (Z. Brzezinski, 1998, p. 61; M. Lefebvre, 2004). The European Union (EU) can be regarded a successor organization to the European Recovery Program (ERP, Marshall Plan) as it encouraged economic integration in (Western) Europe. On 4 April 1949 the North Atlantic Treaty Organization (NATO) was founded, and America's hegemonic position in NATO and (Western) Europe would stabilize the European structure (impossibility of an independent military command structure). The foundation of NATO and (Western) European economic integration at the time was a matter of Realpolitik. The European Union began as the European Coal and Steel Community (ECSC) in 1950. The plan for a European Defence Community (EDC), which would have allowed the creation of a joint European army and the foundation of a European political community, failed. Although the treaty was signed on 27 May 1952, it failed to obtain ratification in the French National Assembly on 30 August 1954. Later attempts to create a political union, such as Amsterdam (1997), Nice (2001), and Laeken (2001) would not reach a similar result.

In 1956, the Suez Crisis marked a turning point in the geopolitical position of Europe (K. Scott, 1996). The political consequences of the Suez Crisis made it possible to establish the European Economic Community (H. Kissinger, 1994, p. 547; M. Feldstein, 1997; A Szász, 1999). The ECSC became the European Economic Community (EEC) in 1957 under the Treaty of Rome and, subsequently, became the European Community (EC) in 1993. The Treaties of Rome would pave the way for the creation of a liberal order in Europe (European Economic Community (EEC), European Atomic Energy Community (EURATOM)). The Treaty on European Union (TEU, Maastricht Treaty) took effect on 1 November 1993, and the European Union (EU) replaced the EC in 2009. The TEU established the principle of subsidiarity, and a liberal monetarist economic policy (Broad Economic Policy Guidelines, BEPGs) (M. Feldstein, 1997; P. Arestis, 2001; E. Hein, 2005). The Maastricht Treaty was amended by the Treaty of Amsterdam (signed 1997), which established the European Central Bank (ECB). The Treaty of Nice was signed in 2001, and the Treaty of Lisbon was signed in 2007. In the Treaty of Lisbon, common safety concerns in public health matters are considered shared competence, while protection and improvement of human health fall under Article 6 as supporting competence
(See also Consolidated version of the Treaty on the Functioning of the European Union, part one - principles, title I - categories and areas of union competence, Article 6).

The fears and ambitions of France and Germany create a delicate political balance (M. Lefebvre, 2004). Back in 1950, the European project was born (incorporated) out of a coal mine and a steel mill, and it still carries this "birth defect" up to the present day (sectoral approach, constitutional asymmetry/deficiencies). Although at that moment political theorists argued that the emerging European polity had either to become a federal state or to establish itself as a confederal organization of states, it became stuck in the middle as an organization 'sui generis'. The European problem with liberal democracy and its preference for republicanism (and pastoral government) is an ongoing problem in the development of the European political process (Benny Lévy, 2002; Jean-Claude Milner, 2003). The European construction worked for a while, as long as most political problems could still be solved at a national level. As Europe had caused havoc on the world twice in the twentieth century, it was placed under US (and Soviet) guardianship. Its geopolitical wings were clipped, and gradually Europe became a geopolitical nonentity (M. Mann, 1993). ”L'Europe est un géant économique, un nain politique et, pire encore, un ver de terre lorsqu'il s'agit d'élablorer une capacité de défenses” (S. Leibfried, 2009). As a result, the European project is over-determined concerning liberal market policy but under-determined and deficient in other political and strategic domains. The European Union cannot integrate its economy, safety, security, health, and healthcare into one consistent political narrative and operational framework due to constitutional asymmetry and deficiencies. Health and healthcare policy has to be part of a broader and consistent constitutional, political, and socioeconomic framework and cannot be dealt with in isolation.
(See also Reflections on the Marshall Plan and Schuman Declaration (9 May 1950) and The Suez crisis: An affair to remember and World Competitiveness Ranking (IMD) and The European Project, from Riches to Rags).

Health and healthcare policy have to be part of a wider sociopolitical ecosystem, achieving tangible results at the "Point of Life" (PoL), and "Point of Care" (PoC) of all citizens. Regardless of the political model it will develop Europe has no strategic vision of the future of its health and healthcare policy capable of reaching both the "Point of Life" (PoL), and "Point of Care" (PoC). An ever closer union or even a Federalisation of the European Union without the capacity and capability to conceive, design, develop and implement a health and healthcare policy adapted to reality at the PoC will lead the EU nowhere. Structure without substance remains an empty vessel. The quality of the European political and socioeconomic process is not sufficient for the challenges Europe is facing. As a result, the European Union cannot live up to its self-imposed ambitions, as we witnessed with the failure of the stability and growth pact, the European debt crisis (2009), the 2015 European migrant crisis (2015), Dieselgate (2015), and Brexit (2020) (J. De Haan, 2004; L. Bovens, 2016). The (economic) crises of the 21st century have challenged the unity of the European Union (G. Szapáry, 2021). Due to the conflict of interest between European integration and European treaties, the enforcement of European law is not well developed (R.D. Kelemen, 2021). A fundamental problem of the European Union is the content and substance of the European project, not only its political structure. Cultural, ideological, and policy substrates of the European Union do not allow for a coherent and consistent political development. The ethico-onto-epistem-ological conundrum of European policy foundations resembles a "spiderweb" or "swamp" in which any attempt of political development comes to a grinding halt (cfr. K. Barad, 2007, p. 90). The methodological, internal, and ontological inconsistencies of EU policy immobilize the ontics (physical, real, or factual) of European politics. European Union foundational narrative resembling a ("Westphalian") Swiss cheese with holes and its disunity in diversity causes gridlock on any attempt to develop a comprehensive and consistent foundation for a truly Europeanized ("post-Westphalian") policy (E.L. Dabova, 2014; S. Patton, 2019). The official motto of the European Union (EU), "In varietate concordia", is both an understatement with regard to diversity and an overstatement with regard to unity. There is no comprehensive European "Platonic" or "Aristotelian" "επιστήμη πολιτική" which provides a foundation and inspiration for the political system of the European Union. As a consequence various political and socio-economic interests cannot be suppressed or incorporated into the broader European structure.

Sociopolitical and cultural differences in the European Union make it almost impossible to achieve internal and external unity: North versus South, East versus West, solidity versus solidarity, neo-nationalism versus federalism, globalization winners ("anywheres") versus losers ("somewheres"), federal state versus confederal organization of states, national 'majoritarian systems of democracy' versus 'non-majoritarian' and 'counter-majoritarian European institutions', principles versus praxis. European expansion, an 'ever deeper' and 'ever wider' union, does not achieve an 'ever closer' union as a socio-liberal democracy when countries only have to adhere to the Copenhagen criteria or Maastricht criteria on paper, not in reality. Technocratic and intergovernmental dominance diminishing both 'input legitimacy' and 'output legitimacy' (twin legitimacy deficit) has also created a populist backlash against the European Union (R. Bellamy, 2010; C. Pinelli, 2020). As in John Kenneth Galbraith's The Affluent Society, the European Union is increasingly witnessing private enterprise wealth within spreading public poverty and problems for its middle class (J.K. Galbraith, 1998). As a result, we keep waiting for a European "Philadelphia moment" "ad calendas Graecas", which would replace the old inward and outward fragmented European order, and bring to life a new social and liberal-democratic European state and nation (R.L. Riley, 2006)
(See also "Liberale Elite" mitverantwortlich für Populismus (Andreas Voßkuhle) and BVerfG, Judgment of the Second Senate of 5 May 2020).

The principles of the 'Peace of Westphalia' (nation states, nationalism) block any attempt to impose substantial, consistent and coherent supranational authority on European states (S.D. Krasner, 2010). Intergovernmental principles, not supranational are at the core of European Union political evolution. Bureaucratic and administrative integration at the European level, political fragmentation at the intergovernmental level (e.g. Visegrád Group, Frugal Four, New Hanseatic League, EU Med Group, etc. ...). The importance of the Westphalian principles (nation-states) were already evident in the early days of the European project as Georges Bidault (1899-1983 CE) declared back in 1953: "Notre but est de faire l'Europe sans défaire la France." (Le Monde, 9 March 1953). Lionel Jospin repeated these words in 2001 (Le Monde, 29 May 2001). On 15 May 1962 Charles de Gaulle (1890-1970 CE) declared: "Il ne peut pas y avoir d’autre Europe que celle des États, en dehors des mythes, des fictions, des parades." (Le Monde, 17 May 1962). Although in France, the Matter doctrine (1931, Paul Matter, First President of the Court of Cassation), according to which French law takes precedence over international treaties, was abandoned in 1975 by the Court of Cassation (Jacques Vabre judgment) and in 1989 by the Council of State (Nicolo judgment), nationalism has not disappeared from the European political theatre (P. Manin, 1991). Governments and courts questioning and delimiting each other's jurisdiction entangles Europe in a tit for that game of political and judicial turf wars. It resembles the "Glasperlenspiel" of Hermann Hesse's novel Das Glasperlenspiel (1943 CE).

The balance of power between France and Germany would play an essential role in European politics and make a substantial transfer of political power impossible at the European level. Europe is to remain a bureaucracy, not a 'Westphalian' polity (centralized or federal European State). The federalist reference of the 'Schuman Declaration' on Tuesday 9 May 1950 will remain wishful thinking (M. Burgess, 2011; M. Holland 2019). Only the occasional crisis allows for a partial and temporal disentanglement of the political stalemate as it disrupts the "balance of powerlessness" of the European Union. Both inward and outward, the European Union does not live up to the political ambitions required to deal with the challenges we are facing (E. Jones, 2021). Internally, its democratic deficit make it lose support from the European citizens. Maybe Europeans should remember the words of Willy Brandt: "Wir wollen mehr Demokratie wagen" (1969). As there is no suitable political level left that can deal with the challenges of European society and its citizens, the European Union is fading away, both internally and externally (F.W. Scharpf, 2003). As a result, the European Union is also increasingly geopolitical irrelevant, and the new global hegemons are even colonizing it with their services, products and industries (reverse colonization by the USA, China, Russia, etc.). Under the aegis of the United States of America, Europe has developed into a relationship with the USA, resembling the relation of ancient Greece with the mighty Roman Empire. It became a tourist destination for its beautiful ruins and museums, but with little geopolitical relevance. The wealthy middle class of emerging countries, such as India, sees Europe mainly as a holiday destination, and their business communities see European companies as potential corporate acquisitions (D.M. Malone, 2011, p.16)
(See also Déclaration Schuman (9 May 1950) and Fouchet Plan (1961) and Eurosclerosis (1970s and 1980s) and Great Recession (2007-2009) and European migrant crisis (2015) and Brexit (2020) and The 17+1 initiative (China and Central and Eastern European Countries)).

What is Europe capable of, in order to deal with the growing challenges of noncommunicable diseases and infectious diseases? What kind of health policy is Europe capable of for its citizens? What are we as Europeans capable of for ourselves and our fellow citizens? A fragmented European health and healthcare policy and system is no match for the health and healthcare challenges we are facing (W. Lamping, 2005, p. 18; H. Vollaard, 2017). Although the Brussels effect results in unilateral regulatory globalization, it does not create European champions. European Union competition law may annoy multinationals, but it does not create European champions. Europe is a legal and regulatory giant, but a strategic dwarf. The creation of a European hybrid analog-digital health and healthcare system has the potential to create patient value for both 'care' (process and outcome, efficiency and effectiveness) and 'caring' (humaneness). Both secondary (value chains, logistics, ...) and primary (Point-Of-Care) processes and activities could improve. Strategic, tactical and operational decisions and execution could be improved. Operational and logistical overhead could be reduced by careful disintermediation and analog-digital process hybridization, but not by offloading technology provider profits on healthcare worker and patient losses. Whether the value created by disintermediation in the end will benefit patients, healthcare providers, healthcare systems or will be siphoned to the NYSE, NASDAQ, or the 上海证券交易所 (SSE), is still an open question. Most likely, it won't be a European government, company, or stock market, as Europe is lagging behind in standardization and interoperability of its historically fragmented healthcare systems due to European Kleinstaaterei and feudalism.

What remains? While European countries once conquered, colonized, and exploited large parts of the world for their profit, those days are gone: "The West won the world not by the superiority of its ideas or values or religion (to which few members of other civilizations were converted) but rather by its superiority in applying organized violence. Westerners often forget this fact; non-Westerners never do." (S. P. Huntington, 1997, p. 51). Western prosperity was built upon the colonization and exploitation of the rest of the world, but in itself, Europe is but a peninsula of the Eurasian continent. While European countries once ruled the world and initiated the industrial revolution, nowadays Europe is falling behind. What happens now is part of the growing provincialization of Europe, from being perceived as the 'cradle of modern civilization' to being the provincial remnant of ancient glory. While Europe indulges itself in culture wars, the rest of the world is busy creating a tectonic shift in the global balance of power (H.H. Wang, 2023). Europeans happily consume away their economic and industrial basis on imported raw materials, energy, products and services (zombie consumerism). They "consume their brains out", but forget to guard and maintain the foundations of their prosperity. "Like Cinderella, decked out temporarily in her fancy clothes, was enjoying herself too much at the ball to pay any attention to the hands of the clock" (Eric E. Williams, 1994). Europeans live happily in their amusement park. "minimeque ad eos mercatores saepe commeant atque ea quae ad effeminandos animos pertinent important" (Commentarii rerum in Gallia gestarum, Liber I, J. Caesar). Consumption of consumer goods may increase the European Gross Domestic Product (GDP), but at the same time we witness a dwindling 'gross strategic product' in Europe. Increasing offshore produced (empty) consumption, increasingly creates technical (industrial) and strategic debt. The growing focus on rent (financial profit, rentier capitalism) or financialization of the economy creates problems for the economy (C. M. Christensen, 2014; G. Mukunda, 2014, M.J.L. Arcand, 2012; J.L. Arcand, 2015). When bankers and investors (stock exchange) become more important than industrialists, engineers, scientists, and skilled labor, society has a problem (J. Tobin, 1984; S.G. Cecchetti, 2012) (e.g. undoing of the US 1933 Banking Act). When the 'ministry of finance' becomes more important than the 'ministry of economic affairs', society has a problem. Gone are the days of Ludwig Erhard, the German Wirtschaftswunder, and the Trente Glorieuses. Europe nowadays mainly piggybacks on US and Chinese technology development and increasingly develops into a technological colony (Forbes 2019 Largest Technology Companies). Between the rising political, economic and technological 'continents,' Europe nowadays resembles a flyover country or a Bermuda Triangle where large-scale innovation attempts disappear (R. Crescenzi, 2007; B. Van Ark, 2008). Health and healthcare cannot be dealt with in isolation, but are part of an overall political and socioeconomic policy and process. The capability and quality of the political and socioeconomic ecosystem determine the development potential of health and healthcare in society. The macro-environment determines the micro-capability of the individual within the boundaries created by the framework of society (e.g. the rotten and pocket boroughs of Great Brittain before 1832 CE). Only a 'Sputnik moment' experience could awaken the sleeping European bear. The (European) individual is the "prisoner" of the environment it has to live in.

Mastering the challenges of creating European entrepreneurial ecosystems remains a problem (G. Van der Panne, 2004; R. Brown, 2017; R. Brown, 2017). European healthcare will have to develop into a highly and well-integrated analog-digital process, or we will not make it, given the global challenges we face. The design and development of a hybrid analog-digital healthcare ecosystem will require an integrated design and development process with high-quality contributions by European government(s), academia, and the analog and digital industry. This kind of approach is called the triple helix model of innovation (H. Etzkowitz, 1995; H Etzkowitz, 2000). Of course, we also have the quadruple and quintuple helix models. No matter how many helices you want to ingrate into a political model, the proof of the pudding is in the eating, not in the theoretical model. The development of a hybrid analog-digital healthcare ecosystem cannot be dealt with in isolation, as it is part of modern society's digitalization and energy transition. On a global scale, the European digital healthcare systems are being developed by a cottage industry, due to the fragmented European healthcare systems (socioeconomic ecosystem fragmentation, agency deficit). Europe has not even created one (global) digital (healthcare) champion, capable of competing with Chinese or US competitors. The European political and socio-economic model cannot deal with China and the US's economic, industrial, and technological competition. Operating on a (national) legislative island, which resembles an island ecology, may provide the (digital) healthcare technology industry with a false sense of safety from global competition, but in the end they will face the fate of the Dodo. Disruptive technologies or global players which enter local healthcare systems have the same effect as an introduced species on an island ecosystem
(See also The Suez crisis: An affair to remember and EU Health Policy and Digital transformation of health and care in the Digital Single Market and Creating European digital champions).

Is there a way to create a hybrid (analog/digital) European public and personal healthcare ecosystem, serving our patients and our healthcare workers? Not within the limited framework of the Treaty on the Functioning of the European Union (TFEU, aka The Treaty of Rome) and the Treaty on European Union (The Maastricht Treaty). According to Article 168(7) of the 'Treaty on the Functioning of the European Union', Member States are responsible for the definition of their health policy and for the organisation and delivery of health services and medical care. The EU can only adopt health legislation within the limitations of the 'Treaty on the Functioning of the European Union', which is Article 168 (protection of public health), Article 114 (approximation of laws) and Article 153 (social policy). Article 35 of the EU Charter of Fundamental Rights proclaims 'Everyone has the right of access to preventive healthcare and the right to benefit from medical treatment under the conditions established by national laws and practices'. Principle 16 of the European Pillar of Social Rights says 'Everyone has the right to timely access to affordable, preventive and curative healthcare of good quality'. The EU only has a supportive competence in healthcare and exclusive and shared competences in other policy areas regulating health determinants which affect health (prevention). The principle of conferral (Articles 4 and 5 TEU) and subsidiarity (Article 5 TEU) make it impossible for the European Union to develop a consistent, effective and efficient healthcare policy and a hybrid healthcare technology framework on a European scale. Article 14 (eHealth) of Directive 2011/24/EU of the European Parliament and of the Council of 9 March 2011 on the application of patients' rights in cross-border healthcare, does not deal with the fundamental issue of European healthcare ecosystem fragmentation. European level initiatives cannot be founded on a non-existent legal European healthcare framework, but have to rely on a workaround based on free movement and the creation of an internal market. It is an example of political and legal creativity or as Alain de Lille once wrote: "Sed quia auctoritas cereum habet nasum, id est diversum potest flecti sensum, rationibus roborandum est" (De Fide Catholica: Contra Haereticos, Valdenses, Iudaeous et Paganos, Alain de Lille, Book 4, Ch. 30). The EU can only extend the right of individuals to seek healthcare services cross-borders, but cannot create a European healthcare ecosystem which solves the fragmentation, territorialism and feudalism of European healthcare policies (E. Mossialos, 2010, p. 12). The 'Decision No 1082/2013/EU' of the European Union on serious cross-border threats to health, does not solve the problem that the EU must respect member states' autonomy in operating their own health systems even in case of global or Pan-European emergencies. European territorial and functional healthcare fragmentation stifles the development of a European healthcare architecture and process innovation. A somewhat fragmented integration takes place through the backdoor of the single market and the European Court of Justice (CJEU) (D.S. Martinsen, 2005; E. Brooks, 2012; H. Vollaard, 2016; H. Vollaard, 2017). European health system reform takes place in the context of macroeconomic policy and from a public finance perspective, not as part of a European health or social policy, which is virtually nonexistent due to the EU's constitutional asymmetry (F.W. Scharpf, 2010; R. Baeten, 2011; S.L. Greer, 2014). It is the DG ECFIN wich determines the rules (public finances and financial stability). Mark Eyskens once criticized the EU's incapability of going beyond a trade area: ”L'Europe est un géant économique, un nain politique et, pire encore, un ver de terre lorsqu'il s'agit d'élablorer une capacité de défenses” (S. Leibfried, 2009). Europe can't become a healthcare giant within the deficient and defective framework of the European treaties. The European Union (EU) lacks the constitutional and political framework to untie the Gordian Knot of European (population) health policy and healthcare and therefore remains a toothless tiger
(See also epSOS & CALLIOPE and Refined eHealth European Interoperability Framework (ReEIF) and Interoperability of health information technology (ONC, USA) and Meaningful Use of Electronic Health Records (USA) and SNOMED CT United States Edition (USA) and The judgments of 1998 in Kohll and Decker (EU)).

Despite the considerable potential of digital technology and data to transform health and care products and services in the EU, uptake remains too slow and fragmentation too high (EU Report, 2018). Despite statements indicating that health policy must be based on the best scientific evidence derived from sound data information and relevant research, the European Commission (EC) and its Member States fail to set up an integrated European health information system ( Health information in Europe. Quo vadis?; S Giampaoli, 2015). A European health community was launched as a political idea in the formative years of European integration (Communauté européenne de la santé (CES)). On 24 September 1952, the French minister of health, Paul Ribeyre presented a detailed idea, with 330 articles of supranational healthcare regulation (P. Ribeyre, 1952; M. Cassan, 1989; C. Parsons, 2006; S. Pumberger, 2010, A. Davesne, 2013; H. Vollaard, 2016). However, the Community founding fathers refused the idea of a supranational European Health Community (EHC) or Europeanisation of healthcare (S. Boudia ed., 2015, pp. 93-108). Health regulation was to remain a national prerogative, and therefore there is no coherent framework for an integrated EU health strategy (B. Merkel, 2008; D.S. Martinsen, 2017). European healthcare as a result remains fragmented at its core and there are two incommensurable principles on which European countries built their healthcare systems: the Bismarck Model or Social Health Insurance (e.g. Austria, Belgium, Bulgaria, Czech Republic, Estonia, France, Germany, Hungary, Lithuania, Luxembourg, the Netherlands, Poland, Slovakia, Slovenia, Romania) and the Beveridge Model or National Health Service (e.g. Denmark, Great Britain, Greece, Finland, Ireland, Italy, Latvia, Portugal, Spain, Sweden) (van der Zee, 2007). The inability to deal with the antiquated design paradigms underlying both systems effectively blocks progress. Both sides emphasize the strengths of their own system, but are blind for their combined weaknesses. We keep arguing from outdated first principles and ideologies, instead of redesigning and rebuilding our health policy and public and private healthcare in order to strengthen it, so we can deal with the changing global epidemiological, socio-economic and demographic reality
(See also Health information in Europe. Quo vadis? and Communauté européenne de la santé (CES))

Healthcare policy is embedded in history through intertextuality, which in this context is the shaping of a policy, treaties, and law by previous treaties, laws, traditions, ..., effectively stifling the capacity for adapting to changing socio-economic and demographic reality. An Iron Curtain runs through the healthcare landscape of Europe, dividing Europe into two separate areas: Bismarckia and Beveridgia. The fragmentation of the different national legislations on health and healthcare causes a Balkanization of European healthcare policy and development. The result is the inability to develop a unified vision and policy on healthcare as the European problem is rooted in the fundamental principles of healthcare policy and organization: Contra principia negantem non est disputandum. Over time (national) healthcare systems have become intransparent "spaghetti-designs" or Rube Goldberg machines, effectively stifling modularized process innovation and resembling dependency hell (spaghetti code, design debt, process entropy). Complexity of an architecture or process does not have to equal in-transparency, when the system architecture shows conceptual clarity. Design backward from the desired and required outcome (scope, time, budget) in order to create or maintain process consistency and transparency (reversal of dependencies). In most discussions about healthcare improvement, the discussions only deal with secondary or derived principles within a specific paradigmatic context, the first principles or axioms or postulates are not being questioned. In the end it creates Janus regulations, which are self-contradictory. Implementing change from secondary principle islands causes havoc within the system as contradictions arise due to emerging operational inconsistencies effectivity stifling innovation and process improvement (Catch-22). Part of the problem is also a sunk-cost fallacy and escalation of commitment problem in a fragmented healthcare policy, which is no longer adapted to the evolving needs of our European citizens
(See also Krieger, 2013 and R. D. Duke, 2004, p. 177). The way we deal with a commitment to our outdated healthcare systems resembles the situation leading to the Space Shuttle Challenger disaster or the toothpaste fallacy
(See also Groupthink and How to put toothpaste back in the tube).

As a result of the fragmentation of European healthcare systems, which permeates every aspect of healthcare in Europe, there is no European Terminology Service Agency (ETSA) for the development and maintenance of a European medical terminology. There is no operational European standard for Cross-Enterprise Document Sharing (XDS) or International Patient Summary (IPS). There is no operational European Electronic Health Record Exchange Format. There is no common European-wide standard operational for exchanging meaningful health data, such as SNOMED CT or LOINC. There is no common European system in use for health data classification in Diagnosis Related Groups (DRGs). Europe is lacking comprehensive data and strategic, tactical and operational information and strategic, tactical and operational processing capacity for its healthcare ecosystem. There is no European standard for operational healthcare data management, monitoring and control. A (European) healthcare ecosystem cannot develop the necessary efficiency and effectiveness to meet the challenges of modern healthcare unless there are common standards for strategic, tactical and operational processing of healthcare process data. A (European) healthcare ecosystem should be based upon international open semantic standards (SNOMED CT, LOINC, WHO ATC, ...), combined with standardized data exchange formats (syntax, IPS, ISO 13606, openEHR, HL7), disease classification (nosology, WHO ICD), and a European diagnosis grouping system (Diagnosis Related Groups, EuroDRG). A set of EU Core Data for Interoperability (EUCDI) should be defined (e.g. USCDI). In addition we need to improve financial systems for tracking revenue and managing billing submissions in relation to patient health and well-being (Value-Based Care, Value-Based Health Care (VBHC))
(See also WHO Family of International Classifications (WHO FIC) and International Statistical Classification of Diseases (WHO ICD) and International Classification of Health Interventions (WHO ICHI) and EuroDRG (EU)).

The European (healthcare) political process

European health and healthcare policy cannot be dealt with in isolation, as it requires other resources to support and sustain a health and healthcare system and process. European policy nowadays has two faces. A pro-market one (liberal single market policies), versus a more dirigiste one based on the Lisbon agenda, aiming to harmonize social policies and achieve greater social cohesion. European policy somewhat resembles a mixture of classical liberalism (economic liberalism, now called neo-liberalism) and ordoliberalism or a social market economy (Washington consensus versus Cornwall consensus)? The European (healthcare) political process within the constraints of the European treaties is fundamentally functional and technocratic, rather than political (Wallace & Smith, 1995; A. Follesdal, 2006). The European project proceeds by means of the 'conveyer belt' or 'spill over' method (French: méthode de l’engrenage), as each little achievement is supposed to lead on to the next one (la théorie fonctionnaliste de l’engrenage, functionalism) (Frédéric Mérand, 2011, p. 24). The European project also proceeds by means of 'failing forward', each time learning from what went wrong with the previous step, but setting the stage for the next set of problems to arise (P.C. Schmitter, 1970; F. Nicoli, 2019). European policy development is based upon expert consultation, committees and in the end intergovernmental bargaining (European Council), before entering the European Commission and the European Parliament (EU comitology system and Comitology). It is the Council of the European Union, which comprises the heads of state or government of the EU member states, which negotiates new EU legislation. The Council of the European Union in a certain way resembles the United States Senate or the German Bundesrat, as it represents the member states of the EU at the European level. The trilogue (tripartite meetings) between the European parliament, commission, and the council is the interinstitutional negotiation model to adopt EU legislation. The European political process resembles a bazaar or marketplace, and a lot of haggling (quid pro quo) (e.g. Germany got its unification, Europe got the euro, ...) (F.M. Bongiovanni, 2017, p. 114). Modern European policy is neither moral nor political, but merely deliberation about a plurality of national commercial interests being dealt with on an administrative and technical level. It is a slow, meandering and tedious process, which does not keep pace with geopolitical and socioeconomic evolution (the (European) dogs bark, but the (global) caravan goes on, paralysis by unanimity rule). In the end European politics is still about national and local politics. The process is still largely driven by (national) self-interest, necessity and crises, not by a shared European political vision. Deep ideological fault lines run through Europe between Northern Europe vs. Southern Europe, Western Europe vs. Eastern Europe, rigorism vs. laxism, legalism vs. democracy, liberalism vs. illiberalism, .... Centrifugal forces gain strength as centripetal forces weaken (G. Szapáry, 2021). Quite often, the different factions succeed in making the European political process into an ideological conflict (B. Simms, 2017). The European Union speaks loudly but (increasingly) carries a dwindling (geopolitical) stick (J. Gallon, 2019; C. Wißmann, 2019). For a long time, the European Union could hide itself under the "wings of the USA" ("Sub umbra alarum tuarum protege nos, America"), but as its geopolitical weight dwindles, it is increasingly left on its own. Should the Pax Americana come to an end, Europe would be left on its own, not knowing how to take care of itself.

Only EU leaders can decide to increase cooperation in health and healthcare, while the European Commission has no right to do so. The DG Health and Food Safety (DG SANTE) is not the main driving force behind the European eHealth projects. The Connecting Europe Facility (CEF) deals with eHealth as part of the initiative to fill the missing links in Europe's energy, transport and digital backbone. It is part of the Innovation and Networks Executive Agency (INEA), which is closely linked with its parent, the Directorate-General for Mobility and Transport (DG MOVE). The one-sided approach on European healthcare as a Digital Single Market (DSM) by the DG CONNECT is due to the lack of a common European social and healthcare vision and policy (e.g. Article 6 of the Lisbon Treaty). The EU Working Party on Public Health (WPPH, preparatory body) and the Employment, Social Policy, Health and Consumer Affairs Council (EPSCO, ministers), who deal with various aspects of European healthcare, can only deal with topics related to public health and medical care, within the constraints of European treaties. The European Commission only can make recommendations to member states; other than that, the European Commission does not have the legal tools to do more. As the European Common Agricultural Policy (CAP) is more important than healthcare, agriculture ministers gather monthly, while European health ministers gather in Brussels only twice yearly. The European Commission keeps launching an endless stream of initiatives and actions, but Europe as a whole is not capable to bring these projects to an end due to implementation dysfunction (ID) (EU Report, 2018, Section 4.3). Initiativitis, a tendency to announce swathes of new initiatives with little inherent substance, or "do somethingitis" is not the solution to our European problems. Europe has perfected the science of "fringology", meaning improving European healthcare at the (cross border) fringes, without touching the national healthcare systems at their heart, such as the Electronic cross-border health services and the eHealth Digital Service Infrastructure (eHDSI). It is as if designing and building cross-border controlled-access highways of which the feeder and exit roads originate and end in a local (national, regulatory, incompatible) swamp and traffic comes to a grinding halt. Cross border leverage upon national healthcare systems is limited. Europe lacks an Archimedean point (solid point, and a long enough lever) from which to impose and implement change at the heart of national healthcare systems.

The European Union (EU) depends on understanding a situation and implementing projects on a hodgepodge of quarreling stakeholders, including member states, industry, civil society (including individual citizens), researchers, and innovators. This is one reason why Europe is always one step behind: "Wenn die Philosophie ihr Grau in Grau malt, dann ist eine Gestalt des Lebens alt geworden, und mit Grau in Grau lässt sie sich nicht verjüngen, sondern nur erkennen; die Eule der Minerva beginnt erst mit der einbrechenden Dämmerung ihren Flug" (G. F. Hegel, Grundlinien der Philosophie des Rechts, 1972, p. 14). It is up to European national governments to organize healthcare and ensure that it is implemented and provided. They also constitute the WPPH and EPSCO, so the European nation states are the alpha an omega of European healthcare policy. The EU itself is only allowed to complement national policies as the European Union can only act in those areas where its member countries have authorized it to do so, via the EU treaties. The European healthcare problem is not knowledge and understanding, but the political and operational incapacity to modernize the fragmented European healthcare system. The European political level is embedded in a non-interventionist legal framework with regard to national health policy, leading to an effective gridlock due to the strategic independence of European nation-states with regard to the core principles of their national healthcare policy. The situation is based upon the European implementation of the subsidiarity principle, which states that the pan-European political level should only have a subsidiary function, performing only those tasks which cannot be performed at the national level, which in case of the European development of a modern healthcare policy has lead to a stalemate at the core of the European healthcare ecosystem, as no appropriate political level is available to perform the necessary changes on the required European and supranational scale. Subsidiarity can lead to parochialism or even pork barrel politics (S. Bloom, 2013). Subsidiarity may also lead to passing control from the public domain to special-interest groups operating with public money but pursuing non-public interests. To put it in economic terminology, the national healthcare markets cannot support the burden of development of a modern healthcare process- and data-management framework. There is not enough market, capital or resources available to develop the new healthcare system within the micro-scale of a national healthcare market. The localization cost is prohibitive, even for the larger European countries. The European framework does not allow for glocalization, or the intelligent combination of local healthcare billing and reimbursement legislation, with the need for a European healthcare process- and data-management framework, not only cross-border at the fringes of national healthcare systems, but at the heart of every national, regional and local European healthcare system.

Yesterday (European) health and healthcare policy fragmentation is also not up to future challenges with a European and even global dimension. This political fragmentation has a negative impact on the European Health Community, consisting first and foremost of our patients, physicians, nurses and other healthcare workers. Given the challenges we are facing, top-class European civil servants and politicians with profound knowledge and understanding of health and healthcare issues should be a priority. 'Science can identify solutions to pressing public health problems, but only politics can turn most of those solutions into reality.' (T.R. Oliver, 2006; S.L. Greer, 2017). However, European health policy is only a marginal European political competence, which means it is more or less a dead-end for a political career. Neither ambitious politicians nor special advisers (spads) are willing to risk their political careers in this domain. Trade and industry, etc., are more important on a European level. The European political void is filled by non-elected experts, leading to an epistemocracy or technocracy. As a result, the European political level lacks the political resources to deal with healthcare challenges on a European level. European politics also lacks (elected) scientists and engineers in parliaments and governments, in order to keep up with the development of science and technology. Health and healthcare policy is not to be developed merely by non-elected technocrats or the 'mandarins' of the civil service, so at least some politicians should have a decent understanding of the evolving environmental, socioeconomic and technological context for which they develop healthcare legislation ('dicere enim bene nemo potest, nisi qui prudenter intelligit' (Cicero)). Civil servants are not to blame for this situation; the political domain is not sufficiently staffed at the European health and healthcare level. The challenge is primarily a political one, but a high-quality political design for European health and healthcare will also require high-quality execution and much hard work by the European administrations. The outcome has to be measured by its results at ambitious but realistic milestones, not only by its political and bureaucratic activity.

As a consequence of the current situation, Europe remains a digital (healthcare) dwarf, unable to create an ecosystem in which an advanced hybrid European digital-analog (healthcare) ecosystem can develop. As a result (European) healthcare systems overall are increasingly becoming an open-air museum of outdated healthcare architectures and processes, hurting our patients and healthcare workers. Technology, which is not embedded and integrated into an ecosystem nor on par with the people involved in the system, does not solve the problem. The comparison is not between the healthcare system of country X which is better or worse than country Y, because this is a false argument as it often compares apples and oranges. The political healthcare framework itself is based on ethical (human rights, civil rights) and political principles, not on the particularities of technology. In the end, the European political level does not have the means and resources to conceive and develop an integrated, internally consistent, and appropriately funded, equipped, and staffed European health and healthcare policy. Therefore European health and healthcare policy must forever remain in the shadows
(See also EU Health Policy and Health care systems in Europe - an impossible overview and Areas of EU action and Digital transformation of health and care in the Digital Single Market and EPF Position paper on eHealth and A Roadmap for Sustainable Healthcare and Access to Healthcare/Health Inequalities (EPF) and Electronic//Mobile health and Taking the pulse of eHealth in the EU and Coalition of the willing can 'drive' healthcare data in Europe and Redesigning health in Europe for 2020 and Health care regulation across Europe, from funding crisis to productivity imperative and ASSESS CT (EU) and LOINC (EU) and Diagnosis-Related Groups in Europe and EuroDRG (EU) and WHO-FIC and ICPC and A Tired Continent of Crises and The creeping privatization of healthcare; M. R. Cowie, 2016).

Healthcare innovation requires political will, long-term commitment and a step-wise approach. Society as a whole has to be involved in the change of the healthcare system (we get what we want and commit ourselves to). Designing and developing a new healthcare system will require a long-term vision, governance, high quality politics, medical, social and economic leadership, management, engineering, development, healthcare system configuration control, education and training and coordinated change management at all levels of society. The change process will not be easy, but maybe something like an occasional Tindallgram may ease the effort: "Well, I just got back from MIT with my weekly quota of new ulcers, which I thought might interest you." (Bill Tindall, Apollo program, 13 June 1966)
(See also Good Governance and the Quality of Government (QoG) and Healthcare governance (WHO) and National health policies (WHO)).

Putting it together

Figure 20a: Effectiveness versus harm (10% harm)

Figure 20b: Efficiency versus waste (25% waste)

Strategy is about making choices under uncertainty. Health and healthcare strategy and policy go hand-in hand. A health and healthcare strategy requires setting priorities and providing a strategic path (polar star). A roadmap has to provide guidance towards the strategic goal. Policies supporting the strategy allow for day-to-day decisions which will enable to pursue the direction set by the strategy. Without sufficient political context, strategy reduces to a list of priorities or a wish-list. We do not need wish-lists. Strategy should not be focused on the plan, which only matches the strategy to the organization's capabilities. Planning itself does not question strategic assumptions; it does not decide about why to do or not to do. Planning is about scope, time, budget, and a Work Breakdown Structure (WBS). A strategy is not a goal, and strategy is not a list of initiatives (F. Vermeulen, 2017).

We will have to reduce the overall burden of disease: non-communicable diseases (NCDs), communicable, maternal, neonatal and nutritional diseases, and injuries. Reducing anthropogenic or industriogenic health destruction will have to be part of the strategy. Reducing the burden of society on healthcare and minimizing the burden caused by healthcare on society (reduce PPC, NE), should be our goal. Monetary value created by "repairing" anthropogenic damage (monetization) does not increase overall wealth, but only converts life value into monetary value (hamster wheel economics). Privatizing (internalizing) profit and consumer satisfaction (I consume therefore I am) and socializing (externalizing) losses and destruction, is not the way to keep going. "The ultimate hidden truth of healthcare is that it is something we make and could just as easily make differently" (adapted from a quote from David Graeber)
(See also anthropogenic hazard, parable of the broken window).

Achievement, not merely activity, should be our goal. What we need to improve is the overall efficiency and effectiveness of both society (production of health and well-being) and our healthcare system (restoring health). Efficiency, effectiveness, and productivity are not anathema to humaneness and kindness. The antagonism of productivity (positive results for patients and healthcare workers) and kindness is a false dichotomy. Avoiding harm to patients is not being heartless. Adhering to International Patient Safety Goals (IPSGs), avoiding Potentially Preventable Complications (PPC), and never events (NEs) is not being unkind to patients or healthcare workers. Quality assurance (QA) and quality control (QC) are not inhumane or cruel to patients or healthcare workers. The WHO estimates show that even in high-income countries as many as 1 in 10 patients (10%) is harmed while receiving health care, causing over 46 million patient harms worldwide per year, and over 1.4 million deaths (WHO, 2017; Figure 20a). In the USA approximately 25% of health care spending may be considered waste, caused by failure of care delivery, failure of care coordination, overtreatment or low-value care, pricing failure, fraud and abuse, and administrative complexity (W. H. Shrank, 2019; Figure 20b). According to the OECD in 2010, improving the efficiency of the health care system, public spending savings would be large, approaching 2% of GDP on average in the OECD. We need to improve the return on effort (ROE) or the efficiency and effectiveness of our healthcare architecture, process and outcome. This will not require just thinking out of the (shoe-)box, but taking out the shoes and start walking. Thinking out of the (shoe-)box is not enough, we should start walking towards a new healthcare architecture and system. We will also have to throw away the box, as we need an entirely new reference frame, which is no longer a "box" (hexahedron, rectangular cuboid), but a "dodecahedron" (you have to know the Platonic solids).
(See also Health Care Systems: Getting More Value for Money (OECD, 2010) and Tackling Wasteful Spending on Health (OECD, 2017) and Improvement Science Research Network (ISRN)).

The creation of the integrated hybrid analog-digital system requires a long-term vision, an integrated, multifaceted and step-wise (timeline, milestones, clustered) approach on many levels and aspects of society and healthcare. Dealing with healthcare outcomes (scope) within the constraints of the Triple Aim or Triple Constraint, deals with the relative weight of scope, cost (resources) and time in health and healthcare policy. The system is to be patient-centered (person-centered), process-oriented, platform-independent and ontology-driven. Patient and process are two sides of an analog coin, just as ontology and platform are two sides of a digital coin. While patient and process both represent the analog aspects of the healthcare system, the ontology and platform reflect the analog world in digital space. The combination of both constitutes the hybrid analog-digital ecosystem. The patient (health) situation is reflected as good as possible in the ontology, while the platform reflects as good as possible the healthcare process. When we look at it from an hybrid perspective, the hybrid coins consists of patient and ontology on the one hand and process and platform on the other hand. No matter how you combine the parts, they have to constitute an interlinked and balanced tetralogy or quaternity (balanced functional and structural correspondence). The overall system will perform well when there exists a bi-axial symmetry, between people and ontology and process and platform, behaving like a set of coupled events (conditions) and processes (dynamical equations) (status and change are cross-coupled and -linked). Combining and integrating analog and digital forces at the PoC allows for process transformation as the overall system links symmetry with economy. Imbalance (asymmetry) or deficiencies (diseconomy) cause the system to under-perform or even collapse under pressure. Passing from the analog world into the digital world could be regarded as passing through a looking-glass as in Through the Looking-Glass, and What Alice Found There (Lewis Carroll, 1871 CE). Things which may be difficult or impossible to perform in the analog world can be performed in the digital world, and vice versa, in order to develop or evolve a global optimization of the entire hybrid ecosystem. The tetralogy or quaternity in the patient-process-platform-ontology model deals with creating a process (tetralogy) and structural (quaternity) relation between efficiency and effectiveness (quality and scope, process and goal), within a hybrid analog-digital healthcare ecosystem. It combines becoming (process medicine, ontology of becoming) with being (health status, ontology of being). It is a conceptual framework to allow for the creation of a hybrid analog-digital public and personal healthcare ecosystem. The architecture of the integrated hybrid analog-digital ecosystem creates checks and balances within a healthcare ecosystem and also determines the governance structure of the system. It also relates to a certain metaphysical and ideological framework (e.g. R.G. Collingwood, 2014; E.J. Lowe, 2006). The integrated hybrid analog-digital system creates an ontological (political) balance between contractarianism (Kantianism, liberalism, individuality, autonomy, reason) and communitarianism (Aristotelianism, Hegelianism, internal relations, organic whole, heuristics, embeddedness) (K.A. Strike, 2000). The analog-digital hybridization of healthcare allows for a hybrid approach to deliberative agency embedded in an inter-subjectivistic system. The analog ontic world (its, beings) is crosslinked bidirectionally to a digital (bits) and ontological conceptualization (being). Crosslinking the analog and the digital should avoid the pitfalls of an ontological representational deficit (M. Poveda-Villalón, 2010; D. Buscaldi, 2013; B. Aldosari, 2017). Although these principles are being applied to health systems and healthcare, they can of course be applied to other aspects of society for creating an integrated, crosslinked and balanced hybrid analog-digital system
(See also Philosophy of medicine, S.A. Schwarzenbach, 1991, OntOlogy Pitfall Scanner (OOPS)).

Figure 21: Work towards intermediate (stable) milestones (A),
but plan backwards (B) from the desired endpoint and a grand vision on a new healthcare.

Creating the (operational) system requires a grand vision, but also a step-wise approach ('Quidquid agis, prudenter agas et respice finem!') (Figure 21). First of all, the legal framework, rules and regulations and (international) guidelines and relevant semantic and syntactic standards have to be put in place (political development). Development of the components of the framework (system architecture) takes place within this environment (planning, staged development and deployment, change management, education and training). Patient-related aspects (human dignity, ethics, legislation, safety, security, confidentiality, privacy, transparency) and the ontology (semantics, relevant standards) come first and upon these foundations the open processes and the open platform are being built. Syntactic and semantic unification and interoperability (common understanding of information) allows for integrating complex activities involving many parties, distributed in location and time. A healthcare ontology is not limited to the ontology of computer science and information science. The "digital ontology" represents only part of the healthcare ontology. Care (efficiency and effectiveness) is to be balanced with caring (humaneness). Human and organizational factors, such as user-centered systems design and evaluation for safe and effective health information technology use are being dealt with (M.-C. Beuscart-Zéphir et al, 2013). Foundational (building blocks), structural (syntax), semantic (ontology) and organizational (ethical, policy, social and organizational components) interoperability are the core components of the hybrid analog-digital system. An integrated analog-digital ecosystem enables man and (intelligent) machine to cooperate as partners in an integrated process. Considering what is at stake, human dignity of the patient and healthcare workers and ethical use of technology has to be an integral part of the hybrid healthcare system and process. It is the patient as a person and not his or her data, which is at the center of the healthcare system and process. The vulnerability of the patient and his or her human dignity as a person requires careful ethical and political consideration in implementing and unleashing technology. The patient legally owns his or her own data, and safety, security and confidentiality of patient information should be a top priority. This will require 'citizen-centric data management' and 'true consent' instead of merely 'informed consent', in order to develop a trustworthy and high quality data management environment (S.M.A. Babar, 2004; M. Hori, 2005). With 'true consent' a professional informs a patient about what he or she 'needs' to know, while with 'informed consent' the patient decides on what he 'wants' to know. Without truly informed patients and their data, a healthcare ecosystem is an empty vessel. Patients and healthcare workers should be at the center of the process and system.

Process hybridization and integration

Interoperability and efficient an effective processing of healthcare data requires both semantic (vocabulary), syntactic (grammar), and technical interoperability. Interoperability specifications require terminology bindings to concepts, code systems, and reusable value sets. The entire system is built around integrated communication, decision taking and execution as an integrated system and healthcare process (interoperability within a hybrid ecosystem), thereby creating a hybrid analog-digital healthcare system and process. It is based upon a hierarchical standardized situation and context representation with personal (who, identity), spatiotemporal (where) and semantic (what) information, with an underlying ontology for representation of meaning. The episodicity of information is represented in a structured representation of medical history (past, AS IS), medical encounter (problem classification, intervention, current episode) and follow-up (outcome, TO BE). Converging the exchange and representation of information (meaning) towards ICPC-2 for primary care, ICNP for nursing and SNOMED CT for secondary, tertiary, and quaternary care, with SNOMED CT as an underlying transmural and transdisciplinary lingua franca, would facilitate transmural, transdisciplinary and complex long-term care. Avoid as much as possible semantic mapping and conversion in order to avoid loss or misrepresentation of meaning due to translation of information. We all understand the problem of translating between languages (dialects) spoken by humans, e.g. appendicitis and 闌尾炎, but we tend to ignore the problems caused by translating meaning in machine communication. The goal should be semantic unification in order to avoid miscommunication and improve interoperability between man and machine. It would also allow for improving and facilitating clinical process support with intelligent machines (AI) due to unification of transmural and transdisciplinary process monitoring and control (PMC). The entire ecosystem is ontology-driven instead of merely data-driven, as meaning is at the heart of the system in order to provide both man and machine with meaningful information in a hybrid analog-digital ecosystem enabling distributed cognition. The communication process integrates analog and digital participants by means of a common lingua franca, based on international open semantic standards such as SNOMED CT, WHO ICD, WHO ICHI, WHO ICF, LOINC, WHO ATC, and UCUM (e.g. USA SOLOR (SnOmed LOinc, Rxnorm)). These standards are combined with data exchange formats, such as the International Patient Summary (IPS), openEHR, HL7 FHIR, DICOM, JSON, XML, REST, and SOAP, etc.. An interrelated set of semantic and syntactic standards makes possible syntactic and semantic unification of the (inter)national healthcare ecosystem. Implement the IHE-standards, such as Cross-enterprise Document Sharing (XDS), and XDS for Imaging (XDS-i), etc. . Taking into account the WHO Family of International Classifications (WHO-FIC) would also be a good starting point. None of these standards is perfect, but the endless bickering about local standards and incompatible and closed systems causes more harm to patients, healthcare workers and healthcare systems. Standards developing organizations (SDOs) also keep popping up and keep developing incompatible standards, which only make things worse to create an ecosystem of standards which serves healthcare workers and patients. Closed and incompatible standards are as bad for interoperability as closed systems. We should keep in mind that (data) standards and interoperability are no substitute for system change and they are not an end in itself. The 'standards industry' is not the master of change and standards are no substitute for system architecture, human and organizational factors. Rigid and context-insensitive (data) standardization may even have a negative impact on process performance and innovation (M.-C. Beuscart-Zéphir et al, 2013, pp. 13-15). It's with data standards as with toothbrushes: "Everybody agrees we need them, but nobody wants to use anyone else's". It is also important to deal with the silo syndrome, (standard) dependency hell and stovepipe systems when necessary
(See also WHO Family of International Classifications (WHO-FIC) and The Language of Nursing: NANDA, NIC, NOC, and Other Standardized Nursing Languages, OMAHA, General Data Protection Regulation (GDPR) and Ethics guidelines for trustworthy AI and Unified Medical Language System (UMLS) and UMLS Metathesaurus Vocabulary Documentation and Healthcare Standards Development: The Value of Nurturing Collaboration and Joint Initiative Council for Global Health Informatics Standardization (JIC) and United States Core Data for Interoperability (USCDI) and European Electronic Health Record Exchange Format).

Process monitoring and control systems, into which the active components are being plugged, act as (intelligent) traffic control agents (data and process). The ExR becomes a platform or hub, no longer a (passive) and closed repository. One could see this as an "uberisation" of applications which provide healthcare services depending on demand and operate beside and in collaboration with healthcare workers (open platform, open communication standards, open data structures, safe and secure). The recruitment and integration of digital competences could be on a more or less structural basis, depending on the need and volume of requests for a certain components and services. Clinical Communication Systems (CCS), Clinical Decision Systems (CDS), and Clinical Execution Systems (CES) are hybrid teams consisting of health care workers and their digital companions. The digital systems evolve from support systems to active participants in the healthcare process. Digital systems evolve to allow for extended and even expanded cognition as they provide additional cognitive capacity and even cognition beyond what is possible with human participants alone. Semantic knowledge and understanding should be available for both man and machine to share and act accordingly. An integrated hybrid analog-digital system allows for distributed, extended and expanded cognition. A Unified Resource Management (URM) system coordinates both health care workers, their digital companions and healthcare logistics (data and hardware). Systems are capable of interconnection across organizations and borders and no vendor lock-in, platform lock-in, or entity lock-in is allowed. Billing and the revenue cycle takes its input from clinical information, but runs separately in order to avoid billing system lock-in of medical activities. How national systems deal with healthcare billing and revenue cycles should not interfere with the secure and safe flow of clinical information.
(See also What is Interoperability? and message-oriented middleware (MOM) and Healthcare Information and Management Systems Society (HIMSS) and Integrating the Healthcare Enterprise (IHE)).

Creating an integrated hybrid analog-digital healthcare system is not about creating yet another grandiose (money making) scheme, but about creating evolutionary pressure which enables a humane hybrid analog-digital healthcare system to unfold. We do not need another grandiose underestimated plan or megaproject running over budget, over time, over and over again (Bent Flyvbjerg, 2014; Bent Flyvbjerg, 2017). The usual bad estimates, optimism bias, escalation of commitment, scope creep and strategic misrepresentation of scope and budget are the destructive elements in all grandiose plans (white elephant). Healthcare, healthcare workers and patients deserve better. It is first of all about putting a political and structural framework in place, which exerts evolutionary pressure towards the intended goal (demographics, etc. will force us anyhow, so reduce the mismatch between the outdated healthcare framework and where society is going to). Change the first principles (axioms, constraints, culture) in order to cause the required Copernican turnaround and the system will (have to) evolve towards the intended goal of an integrated hybrid analog-digital healthcare system. It is not about 'thinking outside the box', as this still confirms 'the box' to be the reference frame, it is about throwing the box away altogether. It is as if changing the reference frame from a Cartesian coordinate system (attached to institutions) to a Gauge reference frame attached to the patient (a gauge is a "continuous" set of local measuring apparatus associated with a single observer). Our axioms and postulates serve as a basis for deducing other truths. Only when man himself is our reference frame, will we be capable to understand and see the true value and impact of our actions: "Μέτρο για όλα τα πράγματα είναι ο άνθρωπος, για όσα υπάρχουν ότι υπάρχουν, και για όσα δεν υπάρχουν ότι δεν υπάρχουν." (Πρωταγόρας). It is as if moving from an Euclidean geometry towards a non-Euclidean geometry, by changing axioms and postulates (e.g. like changing Euclid's fifth postulate)
(See also R.G. Collingwood, 2014 and 7 Tips for Preventing Cost Overrun on Projects and The Iron Law of Megaprojects, Over budget, over time, under benefits, over and over again).

Figure 22: Change process models from AS IS to TO BE (% change per year).
Changing healthcare processes on average takes a generation and the change process is never a smooth transition,
nor a well managed stepwise process, maybe a disruptive change takes place (mutation, catastrophe), but most likely,
due to its complex and multi factorial nature, a multitude of stakeholders and behavioral variability,
it resembles more of an evolutionary process by means of (un)natural selection, proceeding like the dancing procession of Echternach.

The challenges we face will require a cross-disciplinary approach and international collaboration, for which we should not be afraid. We should find courage in the words of Max Weber (1864-1920 CE), who once was criticized for writing outside his narrow discipline: "I am not a donkey, and I don't have a field." (M. Krygier, 2012, p. 282).

How do you eat the elephant which is healthcare? One bite at a time, but keep an eye on the entire elephant (Figure 21) (Blind men and an elephant). Aspirations have to be aligned with capabilities. First, get the foundational principles right and aligned with reality before beginning a journey into nowhere and nothing (avoid a white elephant). A health and healthcare strategy, policy, and roadmap with milestones to be reached should be put into law. Procedural safeguards for protecting patients from abuse and exploitation should be put into law and rigorously enforced (e.g. GDPR, patient rights, ethics). Privacy, safety and security of a healthcare system should be put into law and rigorously enforced (e.g. ISO/IEC 27000, access control, audit controls, integrity controls, transmission security, encryption, zero trust security model, ...). Vendor lock-in, and information blocking should be forbidden. Create a federated system (FDBS) for safety and security reasons (hub and spoke model). Highly centralized systems are sitting ducks for hackers. There is no such thing as 100% safety and security.

For the registration, and exchange of information we have to deal with grammar, semantics, and syntax. Another way of looking at interoperability is foundational, structural (syntax), and semantic. Semantic interoperability allows two or more systems to communicate and exchange data, but the data are also understood by each system. International open standards up to the semantic level should be put in place. Standards such as SNOMED CT, LOINC, WHO-FIC, UCUM (Unified Code for Units of Measure), and OHDSI OMOP Common Data Model deal with semantic unification, semantic modeling and semantic interoperability. Syntactic interoperability allows two or more systems to communicate and exchange data. Formats such as the International Patient Summary (IPS), ISO 13606, openEHR, HL7 FHIR, JSON, REST, XML, and SOAP deal with syntactic interoperability. Grammatical interoperability refers to the exchange of data in a sentence. IPS, ISO 13606, openEHR, and HL7 FHIR provide digital models (containers) for semantic interoperability (solving health data exchange problems, the grammar of data exchange)
(See also e-Estonia and interoperability in healthcare (HIMSS) and IHE).

Health, and healthcare are closely related. Healthcare consists of large, complex operational systems comprised of large numbers of organizations, people, data and machines. Healthcare processes involve large numbers of interactions within these organizations, among organizations and people, and across processes. Dealing with change in healthcare, requires dealing with political, legal, economic, socio-cultural, demographic, environmental, technological, and other external influences on healthcare (e.g. PESTELI Analysis). In order to improve healthcare for our patients, the people working in the healthcare process, the analog and digital system-components, processes, healthcare architecture and infrastructure, have to be on par in order to succeed (Mandl, 2002). Lobbying and political negotiating also have an important impact on healthcare (E. Davies, 2013). Healthcare strategic development and planning requires a multilevel and multidimensional strategic analysis in order to diagnose the key healthcare issues which a given individual, organization, society, or geopolitical entity needs to address (Matryoshka analysis of nested layers of interaction, recursion, unveiling design paradigms).

Redesigning and developing healthcare requires dealing with macro-, meso- and microsystems and nation- and enterprise-level re-engineering of productivity, efficiency, and effectiveness. Redesigning both clinical and administrative processes requires a stepwise and well organized process which keeps the system going on a political, strategic, tactical and operational level during each step of the transition, but it is most often a chaotic process (Figure 22). Changing healthcare from an analog to an integrated and hybrid analog-digital system involves changing the paradigms and architecture of society, which is always hard to achieve. The theoretical model of a smooth plan-do-check-act method (PDCA) for process improvement, in reality resembles the rock of Sisyphus, due to the multi-dimensional and multi-factorial nature of the healthcare process and architecture. Reducing the degrees of freedom of the healthcare process until they fit a limited political and theoretical model, may feel comfortable at first, but may fail in the end. Reality will always force itself upon the limitations of a theoretical model. The political and theoretical healthcare model is only an approximation of reality, but not reality itself. A certain degree of residual uncertainty remains, but minimizing it to an acceptable level (risk exposure) is the goal to be achieved ( Levels of uncertainty). Modeling a system always involves reducing its complexity down to the capabilities of human beings, their instruments and society, but we have to be aware that both necessity and capacity drive design and development of the healthcare process. A process-design and implementation compromise has to be based upon our best possible understanding of the risks and benefits for the real stakeholders and not on mere lobbying or personal interests (e.g. a prioritization technique such as the MoSCoW method). Several niches of the healthcare system will be taken by digital systems, which will in itself lead to a stepwise change of the healthcare process, planned or unplanned. The legal environment is one of the environmental elements which (slowly) push the system in a certain direction by changing the "rules of the game". Redesigning and rebuilding healthcare or its spontaneous evolution lacks an ultimate foundation or in any way some secure foundation of certainty, which resembles Neurath's boat: "Es gibt kein Mittel, um endgültig gesicherte saubere Protokollsätze zum Ausgangspunkt der Wissenschaften zu machen. Es gibt keine tabula rasa. Wie Schiffer sind wir, die ihr Schiff auf offener See umbauen müssen, ohne es jemals in einem Dock zerlegen und aus besten Bestandteilen neu errichten zu können. Nur die Metaphysik kann restlos verschwinden. Die unpräzisen "Ballungen" sind immer irgendwie Bestandteil des Schiffes. Wird die Unpräzision an einer Stelle verringert, kann sie wohl gar an anderer Stelle verstärkt wieder auftreten." (Otto Neurath, 1932, p. 206). This goes against the illusion of strong foundationalism and the presumed infallibility of basic beliefs (principia neutra).

The proper balance between grand vision and programmatic detail is not easy to be specified in advance. The journey requires interdisciplinary collaboration of all sorts, multiple centers for planning, executing and monitoring actions along the way. As the transformation is unprecedented, so must be our politics, planning and execution in order to succeed. The transition from a society's old healthcare system into a new integrated analog-digital system resembles a cutover. A cutover process involves a series of steps need to be planned, executed and monitored in order to make the new integrated analog-digital system go live. The detailed process that leads towards its successful implementation is known as cutover planning. As a cutover by its nature disrupts the entire healthcare system, the main objective of a cutover plan is to minimize disruption of healthcare services. We can do a flash cut, where the change is completed in a short-span of time or we can do a parallel cut, where the legacy healthcare system is left functioning and the new system is installed around it. A parallel cut is more reliable, but also more expensive for society. Every successful project cutover activity always begins with a well-drafted cutover plan ( Project Cutover-A vital step in Project Go Live). While designing, building and implementing the new system is important, more than 80% of its success will depend on preparing the skills and minds of people who will have to work and live with the new system. In order to construct a new health care system, it is more important to understand the true needs of patients and health care workers, than to introduce a multitude of new technologies, tools and gadgets. The local economic, demographic, social and health situation has to be understood and being dealt with at the appropriate level of its capabilities (root cause and capability analysis). Clustering healthcare system development around integrated milestones, creating a step-wise and effective and efficient healthcare system development ladder is important in order to succeed and build an integrated system upon solid foundations. Rural areas in poor regions may be served by providing decent basic sanitation and well-trained barefoot doctors, dealing with basic hygiene, preventive healthcare, family planning and common illnesses. A mobile application or an expensive EHR will not solve basic sanitation problems, basic maternal health issues and high child mortality. A high-tech emergency room (ER) and operating room (OR) are useless without the skilled healthcare workers, equipment (maintenance), medication, energy, etc., to run the facility. Understanding, analyzing and designing a truly integrated system which unites man, machine and technology from a process oriented (efficient) and result driven (effective) perspective is the alpha and omega of success. As Henri Poincaré once wrote in La science et l'hypothèse (1902): "Le savant doit ordonner; on fait la science avec des faits comme une maison avec des pierres; mais une accumulation de faits n'est pas plus une science qu'un tas de pierres n'est une maison." and by W. Edwards Deming in Out of the Crisis (1982): "Long-term commitment to new learning and new philosophy is required of any management that seeks transformation. The timid and the fainthearted, and the people that expect quick results, are doomed to disappointment."

When Unified Resource Management (URM), in an integrated hybrid analog-digital healthcare process, will ever be possible is part of an ongoing development, from a P(E)HR to a complete virtual companion or ExR evolving in a Unified Resource Managed (URM) environment. The flexibility with which the digital components are capable to interact with their analog and digital environment will to a large extent determine the feasibility of such a hybrid system. Necessity will inevitably drive progress, but within the social, economic and political boundaries in which it will be allowed to develop, either for the benefit of patients and society or for the benefit of commercial enterprises. The political understanding, will and capability to create a common platform technology and standards will determine whether the new hybrid analog-digital healthcare will be an open, transparent, safe and secure environment for the benefit of patients and society.

Closing remarks

The paradigmatic revolution in healthcare is as profound and difficult as the change of viewpoint from the Ptolemaic geocentric to the Copernican heliocentric system (E. Rogers, 2003). The emerging digital technology has an equally disturbing influence on the healthcare paradigm as the telescope had on the geocentric model of the universe. The inability to grasp the impact of this new technology is comparable to the inability of the Aristotelian framework to deal with the new Copernican framework. Without a paradigm change, the healthcare system stumbles into the future, looking backward while "the pile of debris before him grows skyward", like the Angelus Novus. We have to be patient, as it took from the publication of the De revolutionibus orbium coelestium in 1543 to the publication of the Philosophiae Naturalis Principia Mathematica in 1687 or 144 years (about 5 generations) for the entire paradigmatic change to happen. Fear for the (personal) consequences of technology on the healthcare workforce may even give rise to Neo-Luddism, when the transition and accompanying uncertainty is badly managed (speed of change, precautionary principle). Ivan Illich (1926-2002 CE) with his book Medical Nemesis (1975), and Neil Postman (1931-2003 CE) with Technopoly: The Surrender of Culture to Technology (1992), warn against the emerging overmedicalisation and technocracy in modern society. Jacques Ellul in his La technique, ou, L'enjeu du siècle (1954 CE) criticized the domination of modern society by technique: "Le phénomène technique (peut se définir comme) la préoccupation de l'immense majorité des hommes de notre temps, de rechercher en toutes choses la méthode absolument la plus efficace." (Jacques Ellul, 2008, p. 18).

The new healthcare paradigm has to penetrate the entire system and the participants' minds to create an internally consistent and well-balanced system. An integrated analog-digital healthcare system will only be possible when the younger generation of digital natives comes of age to be the new healthcare consumers (patients), healthcare providers (physicians, nurses, ...), managers and politicians. Only then the Overton window will shift towards the acceptance of e-healthcare due to an emerging critical mass of patients and healthcare workers who grew up as digital natives. As Max Planck (1858-1947 CE) once said "Eine neue wissenschaftliche Wahrheit pflegt sich nicht in der Weise durchzusetzen, daß ihre Gegner überzeugt werden und sich als belehrt erklären, sondern vielmehr dadurch, daß ihre Gegner allmählich aussterben und daß die heranwachsende Generation von vornherein mit der Wahrheit vertraut gemacht ist." (Wissenschaftliche Selbstbiographie. Mit einem Bildnis und der von Max von Laue gehaltenen Traueransprache, Max Planck, Johann Ambrosius Barth Verlag, Leipzig 1948, p. 22).

There is a fundamental divide between the Anglo-American analytic 'Ontology of Facts' versus the continental 'Ontology of Events'. The analytic and data-driven approach to medicine is part of a positive metaphysics, founded upon facts and able to correct and rectify itself indefinitely in a metaphysical cycle by means of logical analysis (PDCA cycles, big data, evidence based medicine). A negative metaphysics applied to medicine consists in the claim that positive metaphysical claims are untrue and therefore the certainty derived from an analytic approach being capable to improve the healthcare process is rejected. Analytic ontology is the philosophical framework underlying the data-driven approach to healthcare. The data-driven approach to healthcare data is rooted in the analytical tradition of Anglo-American philosophy, which makes it more difficult to introduce it on the European continent, where continental philosophy is the dominant philosophy, which leads to a more narrative, qualitative and anti-quantitative approach to medicine. As Peter Drucker (1909-2005 CE) once said "culture-no matter how defined-is singularly persistent." (P.F. Drucker, 1991). This makes it virtually impossible to create a European hybrid analog-digital healthcare ecosystem, without dealing with cultural differences.

As a last remark, I would like to refer to Hannah Arendt's "Amor Mundi" and the concept of "natality" in The Human Condition (1958), as a conceptual moment when one is born into the political (vita activa) as the sphere where acting together can create the truly unexpected (J. Champlin, 2013). Besides labor (consumption), let us pay attention to work (worldliness), and action (plurality) (Hannah Arendt, The Human Condition (1958))
(See also L. Clarke, 2004 and A. Bruce, 2014 and Bridging the Analytical Continental Divide: A Companion to Contemporary Western Philosophy, Tiziana Andina, BRILL, 2014 and The Domain Shared by Computational and Digital Ontology: A Phenomenological Exploration and Analysis, Bradley Wendell Compton, PhD Thesis, Florida State University, 2009 and Sáez Rueda L., Ontology of Events vs. Ontology of Facts: About the Current Fissures between the Continental and Analytic Traditions, Journal of the British Society for Phenomenology, 2006, 37, pp. 120-137 and Boguslaw Wolniewicz on the Formal Ontology of Situations).

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