When complexity science meets implementation science: a theoretical and empirical analysis of systems change

Abstract

BackgroundImplementation science has a core aim – to get evidence into practice. Early in the evidence-based medicine movement, this task was construed in linear terms, wherein the knowledge pipeline moved from evidence created in the laboratory through to clinical trials and, finally, via new tests, drugs, equipment, or procedures, into clinical practice. We now know that this straight-line thinking was naïve at best, and little more than an idealization, with multiple fractures appearing in the pipeline.DiscussionThe knowledge pipeline derives from a mechanistic and linear approach to science, which, while delivering huge advances in medicine over the last two centuries, is limited in its application to complex social systems such as healthcare. Instead, complexity science, a theoretical approach to understanding interconnections among agents and how they give rise to emergent, dynamic, systems-level behaviors, represents an increasingly useful conceptual framework for change. Herein, we discuss what implementation science can learn from complexity science, and tease out some of the properties of healthcare systems that enable or constrain the goals we have for better, more effective, more evidence-based care. Two Australian examples, one largely top-down, predicated on applying new standards across the country, and the other largely bottom-up, adopting medical emergency teams in over 200 hospitals, provide empirical support for a complexity-informed approach to implementation. The key lessons are that change can be stimulated in many ways, but a triggering mechanism is needed, such as legislation or widespread stakeholder agreement; that feedback loops are crucial to continue change momentum; that extended sweeps of time are involved, typically much longer than believed at the outset; and that taking a systems-informed, complexity approach, having regard for existing networks and socio-technical characteristics, is beneficial.ConclusionConstruing healthcare as a complex adaptive system implies that getting evidence into routine practice through a step-by-step model is not feasible. Complexity science forces us to consider the dynamic properties of systems and the varying characteristics that are deeply enmeshed in social practices, whilst indicating that multiple forces, variables, and influences must be factored into any change process, and that unpredictability and uncertainty are normal properties of multi-part, intricate systems.

Complexity in partnerships: A qualitative examination of collaborative depression care in primary care clinics and community‐based organisations in California, United States

“…Rather than solving specific challenges related to complexity, they highlight establishing an environment that can nimbly adjust to change. Furthermore, our findings are broadly consistent with emerging models in implementation science, which emphasise systems‐level and complexity‐informed approaches to implementation (Braithwaite et al, ; Butt et al, ).…”

“…Rather, they consist of individuals interdependently navigating stated and unstated rules and expectations within a larger system. These characteristics suggest cross‐organisational partnerships may be best understood through the lens of complexity (Braithwaite, Churruca, Long, Ellis, & Herkes, et al, ; Williams & Hummelbrunner ).…”

Implementation of e‐mental health for depression and anxiety: A critical scoping review

“…| 905 implementation, and maintenance (RE-AIM) framework (Glasgow, Vogt, & Boles, 1999), to name a few. These frameworks aid in assessing potential barriers and facilitators for implementation, developing context-specific implementation strategies, and evaluating implementation efforts to determine implementation success (Braithwaite, Churruca, Long, Ellis, & Herkes, 2018;Nilsen, 2015). However, the underuse, superficial use, and misuse of theories pose a substantial scientific challenge for implementation science (Birken et al, 2017).…”

Developing theories of change for social programmes: co-producing evidence-supported quality improvement

“…As Mowles (2014) has persuasively written in the context of evaluation methods but with striking applicability to implementation theory and practice, even when interventions seem 'simple' or straightforward, their operation in the real world is almost certainly very far from this. Given the state of non-equilibrium and non-linearity that characterises all social and human systems, it is probably time we threw out the closed, linear format for the purposes of implementation and improvement planning, and wrestled more actively with the complex responsive and contingent processes that characterise actual implementation in actual service settings (Braithwaite et al, 2018); see Fig. 2.…”