By Marta Schaaf, member of the Health Systems Global thematic working group on social science approaches for research and engagement in health policy & systems (SHaPeS)
It is now widely acknowledged within the HSR community that health systems are complex adaptive systems. Indeed, the concept of “people-centered health systems” highlights that as institutions with people at the center, health systems are inevitably shaped by the self-organization of individual and collective behavior. Sessions at the Third Global Symposium on Health Systems Research raised thought- provoking and timely points about what complexity means for teaching, researching, and addressing health systems challenges.
Given that complexity science and systems thinking have been relatively recently applied to health systems questions, these disciplines are not yet embedded in many public health school curricula. Future public health practitioners, researchers, and policy makers need to be equipped with systems thinking skills that they can apply to interrogate complex health systems, understand the dynamic interactions amongst a variety of actors throughout the system, and identify key leverage points in the health system to maximize health improvements.
As explained by multiple session panelists, such teaching should be based on a sound theoretical understanding of complexity science and systems thinking, and then extended by examining actual applications of systems thinking to health system design, implementation, and evaluation.
Complexity can be integrated into a variety of approaches and methods, including program science, social network analysis, realist evaluation, and discourse analysis. Each approach brings its own advantages, disadvantages, and prerequisites; any one method on its own is not suited to answer all questions. These methods can be helpful in answering questions regarding the “how” and “why” of health systems interventions, and exploring the broader social, political, and economic context. They thus differ from traditional impact evaluation. These methods may include deductive features, but they always have some inductive components.
Moreover, complexity thinking facilitates examination of unspoken norms and implicit knowledge. In other words, hegemonic paradigms, donor policies, and other factors that are often excluded from research should be part of our complexity-informed analyses. This will allow us to “think bigger” and to go beyond the current approaches to policy. Indeed, a rigorous complexity-informed analysis is not the application of new methods to limited questions, but rather the application of new methods to new questions.
Panelists were resolute that complexity approaches should be true to their conceptual underpinnings; researchers should not use these approaches simply because a donor has inappropriately asked, or because they assume it increases the likelihood of getting results published in a peer-reviewed journal. Appropriate and rigorous use of complexity science requires adequate time, funding, and data.
Complexity-informed approaches are well-suited to answer important program questions in real time. For example, program science can inform course corrections in interventions, improving service delivery. In addition to its utility for program implementation questions, complexity is particularly appropriate for questions related to scale-up. Scale-up implies much more than doing the same thing in a bigger space with more people; a whole host of political, governance, social and other factors arise.
However, as complexity is an emerging field in health systems research, policy-makers may be less sophisticated consumers than they are of other types of research. Researchers can work with policy-makers to bridge this gap.
The wide-ranging discussions in Cape Town lay out an ambitious research and policy action agenda. In order to reap the full benefit complexity thinking offers, health systems researchers need to work to ensure that this approach – which is unique in its nuanced understanding of systems and context – informs how we attempt to intervene in systems and context. In brief, our practice-informed research needs to be in turn leveraged to affect practice.