What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

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.

Fit-for-purpose-The bottom-up redesign of the nursing home system: The Australian Aged Care System.

Nursing homes struggle to meet the needs of their residents as they become older and frailer, live with more complex co-morbidity, and are impacted by memory impairment and dementia. Moreover, the nursing home system is overwhelmed with significantly constraining organisational and regulatory demands that stand in the way of achieving resident-focused outcomes. These issues are compounded by the perceptions of poor working environments, poor remuneration, and poor satisfaction amongst staff. The system is beyond the state of 'reform' and requires a fundamental redesign based on first organisational systems understandings: a clearly defined purpose and goal, shared values, and system-wide agreed "simple (or operating) rules". A 'fit-for-purpose' future requires a complex adaptive nursing home system characterised by seamless 'bottom-up and top-down' information flows to ensure that the necessary 'work that needs to be done' is done, and a governance structure that focuses on quality improvement and holds the system accountable for the quality of care that is provided.

Who is "anti-science"?

Objectives: "Anti-science" accusations are common in medicine and public health, sometimes to discredit scientists who hold opposing views. However, there is no such thing as "one science". Epistemology recognizes that any "science" is sociologically embedded, and therefore contextual and intersubjective. In this paper, we reflect on how "science" needs to adopt various perspectives to give a comprehensive and nuanced understanding of a phenomenon. Study design: Opinion paper. Methods: Based on a targeted literature survey, we first clarify the known limits of traditional scientific methods and then reflect on how the scientific reporting about Covid-19 mRNA vaccines has evolved. Results: The first reports of the Covid-19 mRNA vaccines randomised controlled trial results showed impressive efficacy. Nevertheless, an abundant literature has since depicted a far more nuanced picture of the effectiveness and safety of those vaccines over the medium-term. We organise them around five themes: (i) differentiating between relative and absolute reduction; (ii) taking account of time in reporting effectiveness; (iii) taking account of all outcomes, including adverse effects; (iv) stratifying effectiveness and considering other decision criteria (efficiency, equity, and acceptance); (v) changing the outcome of concern and assessing vaccines' effectiveness on mortality. Conclusions: Science offers a wide range of perspectives on a given study object. Only the process of deliberation amongst scientists and other stakeholders can result in accepted new knowledge useful to support decision-making. Unfortunately, by trying to reduce "science" to simple messages set in stone, scientists can become the worse enemies of science.

Complex adaptive organisations: How three-dimensional visualisations can help to understand their structures and behaviours.

Many organisations struggle to achieve their true potential. In part it is a problem of organisational design, which is an outcome of a particularly common-command and control-leadership philosophy. The traditional linear hierarchical structure of organisations suggests that all knowledge and power concentrates at the top organisational layer, and that people in the lower layers need to be told what to do and when. These arrangements disempower people at the lower level, preventing them from providing the necessary feedback to pre-emptively address emerging concerns. Systems and complexity thinking provide different ways of understanding organisations and their complex adaptive behaviours. In this paper, we first describe the key characteristics of complex adaptive organisations-their structure and dynamic behaviours-and then propose a three-dimensional visualisation of a complex adaptive organisation that allows its members to clearly see and appreciate its interdependencies.

Systemic failures in nursing home care-A scoping study.

Nursing homes (also referred to as residential aged care facilities, or long-term care facilities) cater for older people on a respite or long-term basis for those who are no longer able to live independently at home. Globally the sector struggles to meet societal expectations since it is torn between three competing agendas-meeting the needs of residents, meeting the demands of regulators, and meeting the financial imperatives of nursing home proprietors. Competing demands indicate that the system lacks a clear understanding of its purpose-without a clearly understood purpose any system will become dysfunctional overall and across all its levels of organisation. This scoping study aims to summarise and synthesise what is already known about the systemic function and failures in the nursing home system, and the impact this has on the wider health and aged care system. METHOD: MEDLINE, EMBASE, PSYCHINFO, CINAHL and SCOPUS were searched using the terms: (nursing home care OR residential aged care OR nursing home) AND (organisational failure OR institutional failure OR systemic failure), limited to English language articles, including all years up to the end of February 2021. In addition, we used snowballing of article references and Google searches of the grey literature. System-focused articles were defined as those that explored how an issue at one system level impacted other system levels, or how an issue impacted at least two different agents at the same system level. RESULT: Thirty-eight articles addressed systemic issues as defined in four different contexts: United States (14), Canada (2), Australia (11) and European countries (11). Only four studies reported whole-of-system findings, whereas the remaining 34 more narrowly addressed systemic features of specific nursing home issues. The thematic analysis identified 29 key systemic issues across five system layers which consistently appear across every country/health system context. The negative outcomes of these systemic failings include: high rates of regulatory reprimands for unacceptable or unsafe practices; dissatisfaction in care experiences on the part of residents, families, and care staff-including a fear of being sent to a nursing home; and the perception amongst staff that nursing homes are not preferred places to work. CONCLUSIONS: The key issues affecting nursing home residents, and the care home sector more generally, are systemic in nature arising from two key issues: first, the lack of shared agreement on the care home system's purpose; and second, the lack of clear governance and accountability frameworks for system regulation and performance at a national level. Addressing these two key issues must be the starting point for any 'real' nursing home system redesign that can achieve a seamlessly integrated system that delivers the outcomes nursing home residents and their families expect. 'Systems thinking' is required to simultaneously improve care quality and outcomes for residents, strengthen regulation and accountability, and enable financial viability.

From cause and effect to causes and effects.

It is now-at least loosely-acknowledged that most health and clinical outcomes are influenced by different interacting causes. Surprisingly, medical research studies are nearly universally designed to study-usually in a binary way-the effect of a single cause. Recent experiences during the coronavirus disease 2019 pandemic brought to the forefront that most of our challenges in medicine and healthcare deal with systemic, that is, interdependent and interconnected problems. Understanding these problems defy simplistic dichotomous research methodologies. These insights demand a shift in our thinking from 'cause and effect' to 'causes and effects' since this transcends the classical way of Cartesian reductionist thinking. We require a shift to a 'causes and effects' frame so we can choose the research methodology that reflects the relationships between variables of interest-one-to-one, one-to-many, many-to-one or many-to-many. One-to-one (or cause and effect) relationships are amenable to the traditional randomized control trial design, while all others require systemic designs to understand 'causes and effects'. Researchers urgently need to re-evaluate their science models and embrace research designs that allow an exploration of the clinically obvious multiple 'causes and effects' on health and disease. Clinical examples highlight the application of various systemic research methodologies and demonstrate how 'causes and effects' explain the heterogeneity of clinical outcomes. This shift in scientific thinking will allow us to find the necessary personalized or precise clinical interventions that address the underlying reasons for the variability of clinical outcomes and will contribute to greater health equity.

Shared decision-making in the realm of uncertainty: The example of coronary artery disease through an EBM and complexity science lens.

Patients look to their clinicians for explanations and treatments that achieve predictable cures with certainty. Clinicians usually respond accordingly. Acknowledging uncertainty, while necessary, is difficult, anxiety-provoking and at times overwhelming for patients and clinicians alike. We here present three case studies to illustrate the uncertainties of managing patients with potentially life-threatening illnesses. Research aims to provide answers to clinical problems. But, conducting research almost inevitably entails a reduction of real-world complexities. Research ultimately can only provide 'partial or in general answers' mostly revealing new questions. Due to the complexity of clinical care, research cannot really achieve certainty and predictability for an individual within his specific living context and values. In an unavoidably uncertain environment, instead of oversimplifying, clinicians like patients-as far as possible-ought to better embrace a complexity thinking frame. This provides a deeper understanding how living bodies function as-a-whole within their living contexts. Uncertainty and unpredictability, being inherent elements of complexity thinking, cannot be overcome. However, it may be made easier to cope with uncertainty by at least adopting the thinking in probabilities for benefits and harms of patient related outcomes as introduced in Sackett's Evidence-Based Medicine framework. Through the lenses of evidence-based medicine and complexity sciences this paper critically explores the clinical management of three patients diagnosed as having coronary artery disease. They all received the same treatment even though they presented with very different clinical complaints arising from different disease manifestations. Looking at these case studies the authors reflect on the reasons behind this astonishing, but widely seen medical behaviour of 'one size fits all'. They critically reflect the importance of research and evidence in view of a person-centred solution.

Systemic Reflections on Knowledge Transfer Comment on "Sustaining Knowledge Translation Practices: A Critical Interpretive Synthesis".

The systemic failure of organisational learning should not come as a surprise - after all every system delivers exactly what it is designed for. Knowledge management/transfer is a property of the organisational system rather than a particular technique. Hence, knowledge management/transfer is about the contextual framing in which learning focused on understanding can occur. Looking through a system lens any research field can be defined as a complex adaptive organisation, and its culture determines if and how learning and knowledge transfer (or shared learning) can occur. Creating and maintain a learning culture requires leadership that perpetuates continuous dialogues to achieve tacit and explicit knowledge exchange.

COVID-19 - An Opportunity to Redesign Health Policy Thinking.

Coronavirus disease 2019 (COVID-19) dramatically unveiled the fragile state of the world's health and social systems - the lack of emergency health crisis preparedness (under-resourced, weak leadership, strategic plans without clear lines of authority), siloed policy frameworks (focus on individual diseases and the lack of integration of health into the whole of societal activity and its impact on individual as well as community well-being and prosperity), and unclear communication (misguided rationale of policies, inconsistent interpretation of data). The net result is fear - about the disease, about risks and survival, and about economic security. We discuss the interdependencies among these domains and their emergent dynamics and emphasise the need for a robust distributed health system and for transparent communication as the basis for trust in the system. We conclude that systems thinking and complexity sciences should inform the redesign of strong health systems urgently to respond to the current health crisis and over time to build healthy, resilient, and productive communities.

Health and Disease Are Dynamic Complex-Adaptive States Implications for Practice and Research.

Interoception, the ability to convey one's overall physiological state, allows people to describe their health along an experiential continuum, from excellent, very good, good, fair to poor. Each health state reflects a distinct pattern of one's overall function. This assay provides a new frame of understanding health and disease as complex-adaptive system states of the person as-a-whole. It firstly describes how complex patterns can emerge from simple equations. It then discusses how clinical medicine in certain domains has started to explore the pattern characteristics resulting in the heterogeneity of disease, and how this better understanding has improved patient management. The experiential state of health can be surprising to the observer-some are in good health with disabling disease, others are in poor health without the evidence of any. The main part of the assay describes the underlying complexity principles that contribute to health, and synthesizes available evidence from various research perspectives to support the philosophic/theoretical proposition of the complex-adaptive nature of health. It shows how health states arise from complex-adaptive system dynamics amongst the variables of a hierarchically layered system comprising the domains of a person's macro-level external environment to his nano-level biological blueprint. The final part suggests that the frame of health as a dynamic complex-adaptive state defines a new paradigm, and outlines ways of translating these expanded understandings to clinical practice, future research, and health system design.

Beyond multimorbidity: What can we learn from complexity science?

Multimorbidity - the occurrence of two or more long-term conditions in an individual - is a major global concern, placing a huge burden on healthcare systems, physicians, and patients. It challenges the current biomedical paradigm, in particular conventional evidence-based medicine's dominant focus on single-conditions. Patients' heterogeneous range of clinical presentations tend to escape characterization by traditional means of classification, and optimal management cannot be deduced from clinical practice guidelines. In this article, we argue that person-focused care based in complexity science may be a transformational lens through which to view multimorbidity, to complement the specialism focus on each particular disease. The approach offers an integrated and coherent perspective on the person's living environment, relationships, somatic, emotional and cognitive experiences and physiological function. The underlying principles include non-linearity, tipping points, emergence, importance of initial conditions, contextual factors and co-evolution, and the presence of patterned outcomes. From a clinical perspective, complexity science has important implications at the theoretical, practice and policy levels. Three essential questions emerge: (1) What matters to patients? (2) How can we integrate, personalize and prioritize care for whole people, given the constraints of their socio-ecological circumstances? (3) What needs to change at the practice and policy levels to deliver what matters to patients? These questions have no simple answers, but complexity science principles suggest a way to integrate understanding of biological, biographical and contextual factors, to guide an integrated approach to the care of people with multimorbidity.

Without Systems and Complexity Thinking There Is no Progress - or Why Bureaucracy Needs to Become Curious Comment on "What Can Policy-Makers Get out of Systems Thinking? Policy Partners' Experiences of a Systems-Focused Research Collaboration in Preventive Health".

The bureaucracy's goal is to maintain uniformity and control within discrete areas of activity and relies on hierarchical processes and procedural correctness as means to suppress autonomous decision making. That worldview, however, is unsuited for problem solving of real world VUCA (Volatility, uncertainty, complexity and ambiguity) problems. Solving wicked problems in the VUCA world requires curiosity, creativity and collaboration, and a willingness to deeply engage and an ability to painstakingly work through their seemingly contradictory and chaotic pathways. In addition, it necessitates leadership. Leaders require a deep - indeed academic - understanding of the nature of the problems and the veracity of various problem-solving approaches. Leadership after all means "[facilitating] the necessary adaptive work that needs to be done by the people connected to the problem." That are the people at the coalface who understand and have to manage the complexities relating to problems unique to their local environment for which of the shelf solutions never work. Systems and complexity thinking is more than a tool, it is - in a sense - a way of being, namely deeply interested in understanding the highly interconnected and interdependent nature of the issues affecting our life and work. Hence, system and complexity thinking is, contrary to what Haynes and colleagues state in their "summation for the public reader," neither "overwhelming and hard [nor difficult] to use practically." Such a view is as much misleading as self-defeating.