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Primary care physicians are likely both excited and apprehensive at the prospects for artificial intelligence (AI) and machine learning (ML). Complexity science may provide insight into which AI/ML applications will most likely affect primary care in the future. AI/ML has successfully diagnosed some diseases from digital images, helped with administrative tasks such as writing notes in the electronic record by converting voice to text, and organized information from multiple sources within a health care system. AI/ML has less successfully recommended treatments for patients with complicated single diseases such as cancer; or improved diagnosing, patient shared decision making, and treating patients with multiple comorbidities and social determinant challenges. AI/ML has magnified disparities in health equity, and almost nothing is known of the effect of AI/ML on primary care physician-patient relationships. An intervention in Victoria, Australia showed promise where an AI/ML tool was used only as an adjunct to complex medical decision making. Putting these findings in a complex adaptive system framework, AI/ML tools will likely work when its tasks are limited in scope, have clean data that are mostly linear and deterministic, and fit well into existing workflows. AI/ML has rarely improved comprehensive care, especially in primary care settings, where data have a significant number of errors and inconsistencies. Primary care should be intimately involved in AI/ML development, and its tools carefully tested before implementation; and unlike electronic health records, not just assumed that AI/ML tools will improve primary care work life, quality, safety, and person-centered clinical decision making.

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Universal health care (UHC) is primarily a financing concern, whereas primary health care (PHC) is primarily concerned with providing the right care at the right time to achieve the best possible health outcomes for individuals and communities. A recent call for contributions by the WHO emphasized that UHC can only be achieved through PHC, and that to achieve this goal will require the strengthening of the three pillars of PHC - (a) enabling primary care and public health to integrate health services, (b) empowering people and communities to create healthy living conditions, and (c) integrating multisectoral policy decisions to ensure UHC that achieves the goal of "health for all." "Pillars" - as a static metaphor - sends the wrong signal to the research and policy-making community. It, in fact, contradicts the WHO's own view, namely that there is "the need to strengthen comprehensive primary health care systems based on local priorities, needs and contexts … [that are] co-developed by people who are engaged in their own health." What we really need to develop PHC as the basis to achieve the goal of UHC is a dynamic agency to drive a "system-as-a-whole framework" that simultaneously takes into account finance, individual, and local needs. Health systems are socially constructed organizational systems that are "functionally layered" in a hierarchical fashion - governments and/or funders at the top-level not only promote the goals of the system (policies) but also constrain the system (rules, regulations, resources) in its ability to deliver. Hence, there is a need to focus on two key system features - political leadership and dynamic bottom-up agency that maintains everyone's focus on the goal to be achieved, and a limitation of system constraints so that communities can shape best adapted primary care services that truly meet the needs of their individuals, families, and community.

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RATIONALE, AIMS AND OBJECTIVES: Applying traditional industrial quality improvement (QI) methodologies to primary care is often inappropriate because primary care and its relationship to the healthcare macrosystem has many features of a complex adaptive system (CAS) that is particularly responsive to bottom-up rather than top-down management approaches. We report on a demonstration case study of improvements made in the Family Health Center (FHC) of the JPS Health Network in a refugee patient population that illustrate features of QI in a CAS framework as opposed to a traditional QI approach. METHODS: We report on changes in health system utilization by new refugee patients of the FHC from 2016 to 2017. We review the literature and summarize relevant theoretical understandings of quality management in complex adaptive systems as it applies to this case example. RESULTS: Applying CAS principles in the FHC, utilization of the Emergency Department and Urgent Care Center by newly arrived refugee patients before their first clinic visit was reduced by more than half (total visits decreased from 31%-14% of the refugee patients). Our review of the literature demonstrates that traditional algorithmic top-down QI processes are most often unsuccessful in improving even a few single-disease metrics, and increases clinician burnout and penalizes clinicians who care for vulnerable patients. Improvement in a CAS occurs when front-line clinicians identify care gaps and are given the flexibility to learn and self-organize to enable new care processes to emerge, which are created from bottom-up leadership that utilize existing interdependencies and interact with the top levels of the organization through intelligent top-down causation. We give examples of early adapters who are better applying the principles of CAS change to their QI efforts. CONCLUSIONS: Meaningful improvement in primary care is more likely achieved when the impetus to implement change shifts from top-down to bottom-up.

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Health is an adaptive state unique to each person. This subjective state must be distinguished from the objective state of disease. The experience of health and illness (or poor health) can occur both in the absence and presence of objective disease. Given that the subjective experience of health, as well as the finding of objective disease in the community, follow a Pareto distribution, the following questions arise: What are the processes that allow the emergence of four observable states-(1) subjective health in the absence of objective disease, (2) subjective health in the presence of objective disease, (3) illness in the absence of objective disease, and (4) illness in the presence of objective disease? If we consider each individual as a unique biological system, these four health states must emerge from physiological network structures and personal behaviors. The underlying physiological mechanisms primarily arise from the dynamics of external environmental and internal patho/physiological stimuli, which activate regulatory systems including the hypothalamic-pituitary-adrenal axis and autonomic nervous system. Together with other systems, they enable feedback interactions between all of the person's system domains and impact on his system's entropy. These interactions affect individual behaviors, emotional, and cognitive responses, as well as molecular, cellular, and organ system level functions. This paper explores the hypothesis that health is an emergent state that arises from hierarchical network interactions between a person's external environment and internal physiology. As a result, the concept of health synthesizes available qualitative and quantitative evidence of interdependencies and constraints that indicate its top-down and bottom-up causative mechanisms. Thus, to provide effective care, we must use strategies that combine person-centeredness with the scientific approaches that address the molecular network physiology, which together underpin health and disease. Moreover, we propose that good health can also be promoted by strengthening resilience and self-efficacy at the personal and social level, and via cohesion at the population level. Understanding health as a state that is both individualized and that emerges from multi-scale interdependencies between microlevel physiological mechanisms of health and disease and macrolevel societal domains may provide the basis for a new public discourse for health service and health system redesign.

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Purpose: Potentially preventable hospitalizations (PPH) are minimized when adults (usually with multiple morbidities ± frailty) benefit from alternatives to emergency hospital use. A complex systems and anticipatory journey approach to PPH, the Patient Journey Record System (PaJR) is proposed. Application: PaJR is a web-based service supporting ≥weekly telephone calls by trained lay Care Guides (CG) to individuals at risk of PPH. The Victorian HealthLinks Chronic Care algorithm provides case finding from hospital big data. Prediction algorithms on call data helps optimize emergency hospital use through adaptive and anticipatory care. MonashWatch deployment incorporating PaJR is conducted by Monash Health in its Dandenong urban catchment area, Victoria, Australia. Theory: A Complex Adaptive Systems (CAS) framework underpins PaJR, and recognizes unique individual journeys, their dependence on historical and biopsychosocial influences, and difficult to predict tipping points. Rosen's modeling relationship and anticipation theory additionally informed the CAS framework with data sense-making and care delivery. PaJR uses perceptions of current and future health (interoception) through ongoing conversations to anticipate possible tipping points. This allows for possible timely intervention in trajectories in the biopsychosocial dimensions of patients as "particulars" in their unique trajectories. Evaluation: Monash Watch is actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing). Trajectories of poor health (SRH) and anticipation of worse/uncertain health (AH), and CG concerns statistically shifted at a tipping point, 3 days before admission in the subset who experienced ≥1 acute admission. The -3 day point was generally consistent across age and gender. Three randomly selected case studies demonstrate the processes of anticipatory and reactive care. PaJR-supported services achieved higher than pre-set targets-consistent reduction in acute bed days (20-25%) vs. target 10% and high levels of patient satisfaction. Discussion: Anticipatory care is an emerging trajectory data analytic approach that uses human sense-making as its core metric demonstrates improvements in processes and outcomes. Multiple sources can provide big data to inform trajectory care, however simple tailored data collections may prove effective if they embrace human interoception and anticipation. Admission risk may be addressed with a simple data collections including SRH, AH, and CG perceptions, where practical. Conclusion: Anticipatory care, as operationalized through PaJR approaches applied in MonashWatch, demonstrates processes and outcomes that successfully ameliorate PPH.

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We argue that 'multimorbidity' is the manifestation of interconnected physiological network processes within an individual in his or her socio-cultural environment. Networks include genomic, metabolomic, proteomic, neuroendocrine, immune and mitochondrial bioenergetic elements, as well as social, environmental and health care networks. Stress systems and other physiological mechanisms create feedback loops that integrate and regulate internal networks within the individual. Minor (e.g. daily hassles) and major (e.g. trauma) stressful life experiences perturb internal and social networks resulting in physiological instability with changes ranging from improved resilience to unhealthy adaptation and 'clinical disease'. Understanding 'multimorbidity' as a complex adaptive systems response to biobehavioural and socio-environmental networks is essential. Thus, designing integrative care delivery approaches that more adequately address the underlying disease processes as the manifestation of a state of physiological dysregulation is essential. This framework can shape care delivery approaches to meet the individual's care needs in the context of his or her underlying illness experience. It recognizes 'multimorbidity' and its symptoms as the end product of complex physiological processes, namely, stress activation and mitochondrial energetics, and suggests new opportunities for treatment and prevention. The future of 'multimorbidity' management might become much more discerning by combining the balancing of physiological dysregulation with targeted personalized biotechnology interventions such as small molecule therapeutics targeting specific cellular components of the stress response, with community-embedded interventions that involve addressing psycho-socio-cultural impediments that would aim to strengthen personal/social resilience and enhance social capital.

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Terminology matters - as Lakoff emphasised, words and phrases evoke powerful images and frames of understanding. It is for that reason that we need to discern and use appropriately the term complex/complexity in the health science/professional/policy domain. Complex is the fashionable term used when in reality one means 'complicated', 'difficult to understand' or 'multiple simultaneous actions'. However, this is not what complex means. The Latin term means 'entwined/interwoven' - a structural characteristic describing systems. Complexity arises from the interactions between structurally connected entities - a functional characteristic of a system. The basis of scientific rigor is a clear understanding of a discipline's epistemology. Complexity refers to the emergence of outcomes from the interactions of a system's constituent components (and thus has nothing in common with the colloquial meaning of complicatedness).

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RATIONALE, AIMS AND OBJECTIVES: The focus on the diagnosis is a pivotal aspect of medical practice since antiquity. Diagnostic taxonomy helped to categorize ailments to improve medical care, and in its social sense resulted in validation of the sick role for some, but marginalization or stigmatization for others. In the medical industrial complex, diagnostic taxonomy structured health care financing, management and practitioner remuneration. However, with increasing demands from multiple agencies, there are increasing unintended and unwarranted consequences of our current taxonomies and diagnostic processes resulting from the conglomeration of underpinning concepts, theories, information and motivations. RESULTS: We argue that the increasing focus on the diagnosis resulted in excessive compartmentalization - 'partialism' - of medical practice, diminishing medical care and being naively simplistic in light of the emerging understanding of the interconnected nature of the diseasome. The human is a complex organic system of interconnecting dynamics and feedback loops responding to internal and external forces including genetic, epigenetic and environmental attractors, rather than the sum of multiple discrete organs which can develop isolated diseases or multiple morbidities. Solutions to these unintended consequences of many contemporary health system processes involve revisiting the nature of diagnostic taxonomies and the processes of their construction. A dynamic taxonomic framework would shift to more relevant attractors at personal, clinical and health system levels recognizing the non-linear nature of health and disease. Human health at an individual, group and population level is the ability to adapt to internal and external stressors with resilience throughout the life course, yet diagnostic taxonomies are increasingly constructed around fixed anchors. CONCLUSIONS: Understanding diagnosis as dissecting, pigeonholing or bean counting (learning by dividing) is no longer useful, the challenge for the future is to understand the big picture (learning by connecting). Diagnostic categorization needs to embrace a meta-learning approach open to human variability.

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RATIONALE, AIMS AND OBJECTIVES: Person-centred health care is prominent in international health care reforms. A shift to understanding and improving personal care at the point of delivery has generated debates about the nature of the person-centred research agenda. This paper purviews research paradigms that influence current person-centred research approaches and traditions that influence knowledge foundations in the field. It presents a synthesis of the emergent approaches and methodologies and highlights gaps between static academic research and the increasing accessibility of evaluation, informatics and big data from health information systems. FINDINGS: Paradigms in health services research range from theoretical to atheoretical, including positivist, interpretive, postmodern and pragmatic. Interpretivist (subjective) and positivist (objectivist) paradigms have been historically polarized. Yet, integrative and pragmatic approaches have emerged. Nevertheless, there is a tendency to reductionism, and to reduce personal experiences to metrics in the positivist paradigm. Integrating personalized information into clinical systems is increasingly driven by the pervasive health information technology, which raises many issues about the asymmetry and uncertainty in the flow of information to support personal health journeys. The flux and uncertainty of knowledge between and within paradigmatic or pragmatic approaches highlights the uncertainty and the 'unorder and disorder' in what is known and what it means. Transdisciplinary, complex adaptive systems theory with multi-ontology sense making provides an overarching framework for making sense of the complex dynamics in research progress. CONCLUSION: A major challenge to current research paradigms is focus on the individualizing of care and enhancing experiences of persons in health settings. There is an urgent need for person-centred research to address this complex process. A transdisciplinary and complex systems approach provides a sense-making framework.

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PURPOSE: Over the past 7 decades, theories in the systems and complexity sciences have had a major influence on academic thinking and research. We assessed the impact of complexity science on general practice/family medicine. METHODS: We performed a historical integrative review using the following systematic search strategy: medical subject heading [humans] combined in turn with the terms complex adaptive systems, nonlinear dynamics, systems biology, and systems theory, limited to general practice/family medicine and published before December 2010. A total of 16,242 articles were retrieved, of which 49 were published in general practice/family medicine journals. Hand searches and snowballing retrieved another 35. After a full-text review, we included 56 articles dealing specifically with systems sciences and general/family practice. RESULTS: General practice/family medicine engaged with the emerging systems and complexity theories in 4 stages. Before 1995, articles tended to explore common phenomenologic general practice/family medicine experiences. Between 1995 and 2000, articles described the complex adaptive nature of this discipline. Those published between 2000 and 2005 focused on describing the system dynamics of medical practice. After 2005, articles increasingly applied the breadth of complex science theories to health care, health care reform, and the future of medicine. CONCLUSIONS: This historical review describes the development of general practice/family medicine in relation to complex adaptive systems theories, and shows how systems sciences more accurately reflect the discipline's philosophy and identity. Analysis suggests that general practice/family medicine first embraced systems theories through conscious reorganization of its boundaries and scope, before applying empirical tools. Future research should concentrate on applying nonlinear dynamics and empirical modeling to patient care, and to organizing and developing local practices, engaging in community development, and influencing health care reform.

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BACKGROUND: A growing proportion of people are living with long term conditions. The majority have more than one. Dealing with multi-morbidity is a complex problem for health systems: for those designing and implementing healthcare as well as for those providing the evidence informing practice. Yet the concept of multi-morbidity (the presence of >2 diseases) is a product of the design of health care systems which define health care need on the basis of disease status. So does the solution lie in an alternative model of healthcare? DISCUSSION: Strengthening generalist practice has been proposed as part of the solution to tackling multi-morbidity. Generalism is a professional philosophy of practice, deeply known to many practitioners, and described as expertise in whole person medicine. But generalism lacks the evidence base needed by policy makers and planners to support service redesign. The challenge is to fill this practice-research gap in order to critically explore if and when generalist care offers a robust alternative to management of this complex problem. We need practice-based evidence to fill this gap. By recognising generalist practice as a 'complex intervention' (intervening in a complex system), we outline an approach to evaluate impact using action-research principles. We highlight the implications for those who both commission and undertake research in order to tackle this problem. SUMMARY: Answers to the complex problem of multi-morbidity won't come from doing more of the same. We need to change systems of care, and so the systems for generating evidence to support that care. This paper contributes to that work through outlining a process for generating practice-based evidence of generalist solutions to the complex problem of person-centred care for people with multi-morbidity.

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BACKGROUND: The Patient Journey Record system (PaJR) is an application of a complex adaptive chronic care model in which early detection of adverse changes in patient biopsychosocial trajectories prompts tailored care, constitute the cornerstone of the model. AIMS: To evaluate the PaJR system's impact on care and the experiences of older people with chronic illness, who were at risk of repeat admissions over 12 months. DESIGN: Community-based cohort study - random assignment into intervention and usual care group, with process and outcome evaluation. STUDY POPULATION: Adult and older patients with multiple morbidity, one or more chronic diseases with one or more overnight hospitalizations, and seven or more general practice visits in the past 6 months. COMPLEX INTERVENTION: PaJR lay care guides/advocates call patients and their caregivers. The care guides summarize their semi-structured conversations about health concerns and well-being. Predictive modelling and rules-based algorithms trigger alerts in relation to online call summaries. Alerts are acted upon according to agreed guidelines. ANALYSIS: Descriptive and comparative statistics. OUTCOMES: Impact on unplanned emergency ambulatory care sensitive admissions (ACSC) with an overnight stay; sensitivity of alerts and predictions; rates of care guides-supported activities. FINDINGS: Five part-time lay care guides and a care manager monitored 153 intervention patients for 500 person months with 5050 phone calls. The 153 patients in the intervention group were comparable to the 61 controls. The intervention group reported in 50% of calls that their health limited their social activities; and one-third of calls reported immediate health concerns. Predictive analytics were highly sensitive to risk of hospitalization. ACSC admissions were reduced by 50% compared to controls across the sites. DISCUSSION: The initial implementation of a complex patient-centred adaptive chronic care model using lay care guides, supported by machine learning, appeared sensitive to risk of hospitalization and capable of stabilizing illness journeys in older patients with multi-morbidity. CONCLUSION: Actions based on alerts produced in this study appeared to significantly reduce hospitalizations. This paves the way for further testing of the model.

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