The time has come: Embedded implementation research for health care improvement.

RATIONALE, AIMS, AND
OBJECTIVES: The field of implementation science has developed in response to slow and inconsistent translation of evidence into practice. Despite utilizing increasingly sophisticated approaches to implementation, including applying a complexity science lens and conducting realist evaluations, challenges remain to getting the kinds of outcomes hoped for by implementation efforts. These include gaining access and buy-in from those implementing the change and accounting for the influence of local context. One emerging approach to address these challenges is embedded implementation research-a collaborative, adaptive approach to improvement. It involves researchers and implementers working together in situ from the outset of, and throughout, an implementation project. Both groups can benefit from the collaboration: it increases the rigor of evaluation, provides opportunities to improve the intervention through direct feedback, and promotes better on-the-ground understanding of the change process. We aimed to examine the potential benefits, and some of the challenges, of increased embeddedness.
METHOD: We performed a multi-case analysis of implementation research projects that varied by degree of embeddedness.
RESULTS: Embedded implementation research may offer a range of advantages over dichotomized research-practice designs, including better understanding of local context and direct feedback to improve the implementation along the way. We present a model that spans four approaches: dichotomized research-practice, collaborative linking-up, partially-embedded, and deep immersion.
CONCLUSION: Embedded implementation research approaches hold promise in comparison to traditional dichotomized-research practice designs, where the research is external to the implementation and conducts a summative evaluation. We are only beginning to understand how such partnerships operate in practice and what makes them successful. Our analysis suggests the time has come to consider such approaches.