Large-system transformation in health care: a realist review

An evidence base that addresses issues of complexity and context is urgently needed for large-system transformation (LST) and health care reform. Fundamental conceptual and methodological challenges also must be addressed. The Saskatchewan Ministry of Health in Canada requested a six-month synthesis project to guide four major policy development and strategy initiatives focused on patient- and family-centered care, primary health care renewal, quality improvement, and surgical wait lists. The aims of the review were to analyze examples of successful and less successful transformation initiatives, to synthesize knowledge of the underlying mechanisms, to clarify the role of government, and to outline options for evaluation.

We used realist review, whose working assumption is that a particular intervention triggers particular mechanisms of change. Mechanisms may be more or less effective in producing their intended outcomes, depending on their interaction with various contextual factors. We explain the variations in outcome as the interplay between context and mechanisms. We nested this analytic approach in a macro framing of complex adaptive systems (CAS).

Our rapid realist review identified five "simple rules" of LST that were likely to enhance the success of the target initiatives: (1) blend designated leadership with distributed leadership; (2) establish feedback loops; (3) attend to history; (4) engage physicians; and (5) include patients and families. These principles play out differently in different contexts affecting human behavior (and thereby contributing to change) through a wide range of different mechanisms.

Realist review methodology can be applied in combination with a complex system lens on published literature to produce a knowledge synthesis that informs a prospective change effort in large-system transformation. A collaborative process engaging both research producers and research users contributes to local applications of universal principles and mid-range theories, as well as to a more robust knowledge base for applied research. We conclude with suggestions for the future development of synthesis and evaluation methods.