W(h)ither complexity? The emperor's new toolkit? Or elucidating the evolution of health systems knowledge?

Improving quality in a complex primary care system-An example of refugee care and literature review

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.

Optimizing indices of AF susceptibility and burden to evaluate AF severity, risk and outcomes

Atrial fibrillation (AF) has heterogeneous patterns of presentation concerning symptoms, duration of episodes, AF burden, and the tendency to progress towards the terminal step of permanent AF. AF is associated with a risk of stroke/thromboembolism traditionally considered dependent on patient-level risk factors rather than AF type, AF burden, or other characterizations. However, the time spent in AF appears related to an incremental risk of stroke, as suggested by the higher risk of stroke in patients with clinical AF vs. subclinical episodes and in patients with non-paroxysmal AF vs. paroxysmal AF. In patients with device-detected atrial tachyarrhythmias, AF burden is a dynamic process with potential transitions from a lower to a higher maximum daily arrhythmia burden, thus justifying monitoring its temporal evolution. In clinical terms, the appearance of the first episode of AF, the characterization of the arrhythmia in a specific AF type, the progression of AF, and the response to rhythm control therapies, as well as the clinical outcomes, are all conditioned by underlying heart disease, risk factors, and comorbidities. Improved understanding is needed on how to monitor and modulate the effect of factors that condition AF susceptibility and modulate AF-associated outcomes. The increasing use of wearables and apps in practice and clinical research may be useful to predict and quantify AF burden and assess AF susceptibility at the individual patient level. This may help us reveal why AF stops and starts again, or why AF episodes, or burden, cluster. Additionally, whether the distribution of burden is associated with variations in the propensity to thrombosis or other clinical adverse events. Combining the improved methods for data analysis, clinical and translational science could be the basis for the early identification of the subset of patients at risk of progressing to a longer duration/higher burden of AF and the associated adverse outcomes.

Applying systems thinking to knowledge mobilisation in public health

CONTEXT

Knowledge mobilisation (KM) is a vital strategy in efforts to improve public health policy and practice. Linear models describing knowledge transfer and translation have moved towards multi-directional and complexity-attuned approaches where knowledge is produced and becomes meaningful through social processes. There are calls for systems approaches to KM but little guidance on how this can be operationalised. This paper describes the contribution that systems thinking can make to KM and provides guidance about how to put it into action.

METHODS

We apply a model of systems thinking (which focuses on leveraging change in complex systems) to eight KM practices empirically identified by others. We describe how these models interact and draw out some key learnings for applying systems thinking practically to KM in public health policy and practice. Examples of empirical studies, tools and targeted strategies are provided.

FINDINGS

Systems thinking can enhance and fundamentally transform KM. It upholds a pluralistic view of knowledge as informed by multiple parts of the system and reconstituted through use. Mobilisation is conceived as a situated, non-prescriptive and potentially destabilising practice, no longer conceptualised as a discrete piece of work within wider efforts to strengthen public health but as integral to and in continual dialogue with those efforts. A systems approach to KM relies on contextual understanding, collaborative practices, addressing power imbalances and adaptive learning that responds to changing interactions between mobilisation activities and context.

CONCLUSION

Systems thinking offers valuable perspectives, tools and strategies to better understand complex problems in their settings and for strengthening KM practice. We make four suggestions for further developing empirical evidence and debate about how systems thinking can enhance our capacity to mobilise knowledge for solving complex problems - (1) be specific about what is meant by 'systems thinking', (2) describe counterfactual KM scenarios so the added value of systems thinking is clearer, (3) widen conceptualisations of impact when evaluating KM, and (4) use methods that can track how and where knowledge is mobilised in complex systems.

The application of systems thinking concepts, methods, and tools to global health practices: An analysis of case studies

OBJECTIVES

This review of systems thinking (ST) case studies seeks to compile and analyse cases from ST literature and provide practitioners with a reference for ST in health practice. Particular attention was given to (1) reviewing the frequency and use of key ST terms, methods, and tools in the context of health, and (2) extracting and analysing longitudinal themes across cases.

METHODS

A systematic search of databases was conducted, and a total of 36 case studies were identified. A combination of integrative and inductive qualitative approaches to analysis was used.

RESULTS

Most cases identified took place in high-income countries and applied ST retrospectively. The most commonly used ST terms were agent/stakeholder/actor (n = 29), interdependent/interconnected (n = 28), emergence (n = 26), and adaptability/adaptation (n = 26). Common ST methods and tools were largely underutilized. Social network analysis was the most commonly used method (n = 4), and innovation or change management history was the most frequently used tool (n = 11). Four overarching themes were identified; the importance of the interdependent and interconnected nature of a health system, characteristics of leaders in a complex adaptive system, the benefits of using ST, and barriers to implementing ST.

CONCLUSIONS

This review revealed that while much has been written about the potential benefits of applying ST to health, it has yet to completely transition from theory to practice. There is however evidence of the practical use of an ST lens as well as specific methods and tools. With clear examples of ST applications, the global health community will be better equipped to understand and address key health challenges.

Complexity Sciences

Complexity sciences, in plain English, are the sciences of interconnectedness.

The aim of complexity sciences is to understand the many different facets of phenomena. Complexity sciences employs a variety of different methodological approaches to describe and to analyse multifaceted phenomena like health, the economy, or environmental systems.

Basically, a system consists of a number of parts that are connected to each other. Systems differ depending on the nature of their connectedness. Simple systems have one-to-one relationships and their behaviour is precisely predictable. Complicated systems have one-to-many relationships with mostly predictable behaviours.

This book deals with complex adaptive systems with many-to-many relationships. Their many-to-many relationships make their behaviour emergent, hence their outcomes are unpredictable. Complex adaptive systems have a special characteristic, the members of the system can learn from feedback and experiences. The relationships in complex adaptive systems change constantly allowing the system to evolve over time in light of changing demands. However, a system’s overall behaviour, despite its adaptation to changing circumstances, remains relatively stable within boundaries, but occasionally, its behaviour may change abruptly and dramatically for no apparent reason.

One can compare the behaviour of complex adaptive systems to that of a family; most of the time a family stays together despite ups and downs, but occasionally a family can abruptly break apart to the surprise of its members and its surroundings.

Another important characteristic of complex adaptive systems is its nonlinear behaviour to change, i.e. the magnitude of change in one member of the system shows a disproportional change in that of others. As experience shows, small changes in the behaviour of a system member often show dramatic changes in the behaviour of the whole system, whereas a major change in the behaviour of that member typically results in little or no change.

Studying complex adaptive systems aims to understand the relationships and the dynamics between the members of the systems. This understanding allows for better responses when the system as a whole is challenged by constraints and/or unfamiliar challenges.

A special characteristic of social systems is their “goal-delivering” nature. In organisational terms these are codified by their purpose, goals, and values statements.

Measuring interdependence in ambulatory care

RATIONALE, AIMS AND OBJECTIVES

Complex systems differ from complicated systems in that they are nonlinear, unpredictable and lacking clear cause-and-effect relationships, largely due to the interdependence of their components (effects of interconnectedness on system behaviour and consequences). The purpose of this study was to demonstrate the potential for network density to serve as a measure of interdependence, assess its concurrent validity and test whether the use of valued or binary ties yields better results.

METHOD

This secondary analysis used the 2010 National Ambulatory Care Medical Survey to assess interdependence of 'top 20' diagnoses seen and medications prescribed for 14 specialties. The degree of interdependence was measured as the level of association between diagnoses and drug interactions among medications. Both valued and binary network densities were computed for each specialty. To assess concurrent validity, these measures were correlated with previously-derived valid measures of complexity of care using the same database, adjusting for diagnosis and medication diversity.

RESULTS

Partial correlations between diagnosis density, and both diagnosis and total input complexity, were significant, as were those between medication density and both medication and total output complexity; for both diagnosis and medication densities, adjusted correlations were higher for binary rather than valued densities.

CONCLUSION

This study demonstrated the feasibility and validity of using network density as a measure of interdependence. When adjusted for measure diversity, density-complexity correlations were significant and higher for binary than valued density. This approach complements other methods of estimating complexity of care and may be applicable to unique settings.

The complex patient: A concept clarification

Over the last decade, the concept of the "complex patient" has not only been more widely used in multidisciplinary healthcare teams and across various healthcare disciplines, but it has also become more vacuous in meaning. The uptake of the concept of the "complex patient" spans across disciplines, such as medicine, nursing, and social work, with no consistent definition. We review the chronological evolution of this concept and its surrogate terms, namely "comorbidity," "multimorbidity," "polypathology," "dual diagnosis," and "multiple chronic conditions." Drawing on key principles of concept clarification, we highlight disciplinary usage in the literature published between 2005 and 2015 in health sciences, attending to overlaps and revealing nuances of the complex patient concept. Finally, we discuss the implications of this concept for practice, research, and theory.

Do complexity-informed health interventions work? A scoping review

BACKGROUND

The lens of complexity theory is widely advocated to improve health care delivery. However, empirical evidence that this lens has been useful in designing health care remains elusive. This review assesses whether it is possible to reliably capture evidence for efficacy in results or process within interventions that were informed by complexity science and closely related conceptual frameworks.

METHODS

Systematic searches of scientific and grey literature were undertaken in late 2015/early 2016. Titles and abstracts were screened for interventions (A) delivered by the health services, (B) that explicitly stated that complexity science provided theoretical underpinning, and (C) also reported specific outcomes. Outcomes had to relate to changes in actual practice, service delivery or patient clinical indicators. Data extraction and detailed analysis was undertaken for studies in three developed countries: Canada, UK and USA. Data were extracted for intervention format, barriers encountered and quality aspects (thoroughness or possible biases) of evaluation and reporting.

RESULTS

From 5067 initial finds in scientific literature and 171 items in grey literature, 22 interventions described in 29 articles were selected. Most interventions relied on facilitating collaboration to find solutions to specific or general problems. Many outcomes were very positive. However, some outcomes were measured only subjectively, one intervention was designed with complexity theory in mind but did not reiterate this in subsequent evaluation and other interventions were credited as compatible with complexity science but reported no relevant theoretical underpinning. Articles often omitted discussion on implementation barriers or unintended consequences, which suggests that complexity theory was not widely used in evaluation.

CONCLUSIONS

It is hard to establish cause and effect when attempting to leverage complex adaptive systems and perhaps even harder to reliably find evidence that confirms whether complexity-informed interventions are usually effective. While it is possible to show that interventions that are compatible with complexity science seem efficacious, it remains difficult to show that explicit planning with complexity in mind was particularly valuable. Recommendations are made to improve future evaluation reports, to establish a better evidence base about whether this conceptual framework is useful in intervention design and implementation.

Medical education: a particularly complex intervention to research

Previous debate has explored whether medical education research should become more like health services research in terms of frameworks, collaborations and methodologies. Notable recent changes in health services research include an increasing emphasis on complex interventions, defined as interventions that involve more than one component. The purpose of this study was to explore the extent of thinking about medical education as a complex intervention and to analyse medical education research to determine whether its collaborations and methodologies are becoming more like health services research. Research articles published in three journals over 2 years were analysed to determine the purpose of the research in relation to a framework for evaluating complex interventions, the degree of collaboration, and the methodology. Most studies aimed to develop theory or assess effectiveness and many categories of the complex interventions framework were not represented in the medical education research literature. Studies usually involved only one research site and were predominantly quantitative but not experimental or quasi-experimental. Whilst medical education research has not moved significantly in the direction of health services research over recent years, the complex interventions lens provided insights into why this might be so (namely the significant challenges associated with researching medical education). We recommend that medical education researchers work within a complex interventions framework and look to research fields with similar challenges (e.g. the study of chronic illness in a changing context) for ideas about theories, frameworks, methodologies and collaborations that can illuminate the field of medical education research.

Thinking through health capacity development for Fragile States

The purpose of this paper is to consider capacity development for healthcare in Fragile States and its roles, for example, in securing civil and political stability, as well as improved health, within the various contexts prevailing in fragile settings across the world. As a precursor to this, however, it is important to understand how, in rapidly changing environments, the role and contribution of different donors will have an impact in different ways. This paper sets out to interpret these issues, and what becomes apparent is the need to develop an understanding of the value base of donors, which we demonstrate through the development of a value-based framework. This highlights the separate motivations and choices made by donors, but what is apparent is that all remain within the positivist perspective perhaps for reasons of accountability and transparency. However, the emergence of new interpretations drawing on systems thinking, and followed by complexity theory more recently, in understanding contexts, suggests that the favouring of any one of these perspective can be counterproductive, without a consideration of the contexts in which they occur. In seeking an explanation of these environmental contexts, which also address the perspectives in use, we suggest the use of wider multi-ontology sense-making framework such as Cynefin. Through this approach, analytical insights can be given into the interpretation, decision and intervention processes available in these different and often changing environments, thus enabling greater coherence between donor values and recipient contexts.

Leadership and transitions: maintaining the science in complexity and complex systems

It is the 'moral compass', however subtle, that underpins leadership. Leadership, meaning showing the way, demands as much conviction as gentile diplomacy in the discourse with supporters and detractors. In particular, leadership defends the goal by safeguarding its principles from its detractors. The authors writing in the Forum on Complexity in Medicine and Healthcare since its inception are leaders in an intellectual transition to complex systems thinking in medicine and health.

Making sense of polarities in health organizations for policy and leadership

Making sense of complex adaptive clinical practice and health systems is a pressing challenge as health services continue to struggle to adapt to changing internal and external constraints. In this Forum, we begin with Dervin's Sense-Making theories and research in communications. This provides a conceptual and theoretical context for this editions research on comparative complexity of family medicine consultations in the USA, models for adaptive leadership in clinical care and social networking to make sense of health promotion challenges for young people. Finally, a Sense-Making schema is proposed.

Trends in the perceived complexity of primary health care: a secondary analysis

RATIONALE

If the complexity of the patient's medical problems increases or the complexity of the interactions between the doctor and the patient, the staff or the health care system increase, then complexity of patient care will increase. This study examined trends in patient complexity, and identified doctor, practice and improvement strategy characteristics associated with perceived complexity.

METHODS

This secondary analysis used data from three Community Tracking Surveys with 22,134 primary care doctors completing surveys about themselves, their practice setting, practice improvement strategies and complexity of care in three consecutive 2-year time periods (1996-1997, 1998-1999, 2000-2001). Data were analysed using hierarchical logistic regression.

RESULTS

The proportion of primary care doctors who perceived that complexity of care had increased over the past 2 years rose from 31.5% to 35.9%. Perceived complexity of patient care was consistently related to being in solo practice and the belief that they could not frequently obtain high-quality services and referrals for patients. As availability of services increased, complexity decreased whereas as use of practice improvement strategies increased, complexity also increased.

CONCLUSIONS

Understanding that we cannot determine whether respondents understood care as 'complicated' or 'complex', potential consequences of this increase in complexity include an increase in medical errors and referral rates along with decreased quality of patient care and career satisfaction.