Abstract
The last in a series of four papers on how learning health systems can use routinely collected electronic health data (EHD) to advance knowledge and support continuous learning, this review describes how delivery system science provides a systematic means to answer questions that arise in translating complex interventions to other practice settings. When the focus is on translation and spread of innovations, the questions are different than in evaluative research. Causal inference is not the main issue, but rather one must ask: How and why does the intervention work? What works for whom and in what contexts? How can a model be amended to work in new settings? In these settings, organizational factors and design, infrastructure, policies, and payment mechanisms all influence an intervention's success, so a theory-driven formative evaluation approach that considers the full path of the intervention from activities to engage participants and change how they act to the expected changes in clinical processes and outcomes is needed. This requires a scientific approach to quality improvement that is characterized by a basis in theory; iterative testing; clear, measurable process and outcomes goals; appropriate analytic methods; and documented results. To better answer the questions that arise in delivery system science, this paper introduces a number of standard qualitative research approaches that can be applied in a learning health system: Pawson and Tilley's "realist evaluation," theory-based evaluation approaches, mixed-methods and case study research approaches, and the "positive deviance" approach.