Studying complexity in health services research: desperately seeking an overdue paradigm shift

Abstract

Complexity is much talked about but sub-optimally studied in health services research. Although the significance of the complex system as an analytic lens is increasingly recognised, many researchers are still using methods that assume a closed system in which predictive studies in general, and controlled experiments in particular, are possible and preferred. We argue that in open systems characterised by dynamically changing inter-relationships and tensions, conventional research designs predicated on linearity and predictability must be augmented by the study of how we can best deal with uncertainty, unpredictability and emergent causality. Accordingly, the study of complexity in health services and systems requires new standards of research quality, namely (for example) rich theorising, generative learning, and pragmatic adaptation to changing contexts. This framing of complexity-informed health services research provides a backdrop for a new collection of empirical studies. Each of the initial five papers in this collection illustrates, in different ways, the value of theoretically grounded, methodologically pluralistic, flexible and adaptive study designs. We propose an agenda for future research and invite researchers to contribute to this on-going series.

Being “nice”: A complex activity among health professionals following a critical incident

“…This study supports Greenhalgh and Papoutsi () challenge for a paradigm shift amongst health care researchers. Through allowing for emergent causality, the interconnections that create niceness are better understood.…”

“…Aggregate complexity, a form of social complexity, shapes the thinking in this study, as it “attempts to access the holism and synergy resulting from the interaction of system components” (Manson, , p. 409). Greenhalgh and Papoutsi () argue that for the complex issues in health care, research needs to focus on the multiple influences that contribute to the emergent nature of outcomes. No matter the type of system, complexity theory provides an explanation for the emerging order (Holland, ; Lichtenstein, ; Nieuwenhuijze et al, ; Paley & Eva, ).…”

How patient and community involvement in diabetes research influences health outcomes: A realist review

“…It has been argued that research funders currently prioritize research designs that favour controlled studies at the expense of ignoring complexity in health interventions . Further, in the case of diabetes, research projects are generally expected to generate evidence of clinical impact over a relatively short time period of 6‐18 months.…”

Complexity made simple

“…9 Recently, Greenhalgh and Papoutsi have also offered an updated and accessible account of how to apply complexity in the context of health services research. 10 What all these works try to do is to address the question: in a world where prediction can never be certain, are there nevertheless some general rules that can reduce uncertainty, so that our actions stand a better chance of achieving their intended results? In essence, these models are attempts to find a reasonable mid-point between the naivety of conventional 'straight-line' thinking on the one hand ('If I do X, then it will inevitably result in Y') and fatalism on the other hand ('If something can go wrong, it will.')…”