Factors Affecting Post-trial Sustainment or De-implementation of Study Interventions: A Narrative Review.

In contrast to traditional randomized controlled trials, embedded pragmatic clinical trials (ePCTs) are conducted within healthcare settings with real-world patient populations. ePCTs are intentionally designed to align with health system priorities leveraging existing healthcare system infrastructure and resources to ease intervention implementation and increase the likelihood that effective interventions translate into routine practice following the trial. The NIH Pragmatic Trials Collaboratory, funded by the National Institutes of Health (NIH), supports the conduct of large-scale ePCT Demonstration Projects that address major public health issues within healthcare systems. The Collaboratory has a unique opportunity to draw on the Demonstration Project experiences to generate lessons learned related to ePCTs and the dissemination and implementation of interventions tested in ePCTs. In this article, we use case studies from six completed Demonstration Projects to summarize the Collaboratory's experience with post-trial interpretation of results, and implications for sustainment (or de-implementation) of tested interventions. We highlight three key lessons learned. First, ineffective interventions (i.e., ePCT is null for the primary outcome) may be sustained if they have other measured benefits (e.g., secondary outcome or subgroup) or even perceived benefits (e.g., staff like the intervention). Second, effective interventions-even those solicited by the health system and/or designed with significant health system partner buy-in-may not be sustained if they require significant resources. Third, alignment with policy incentives is essential for achieving sustainment and scale-up of effective interventions. Our experiences point to several recommendations to aid in considering post-trial sustainment or de-implementation of interventions tested in ePCTs: (1) include secondary outcome measures that are salient to health system partners; (2) collect all appropriate data to allow for post hoc analysis of subgroups; (3) collect experience data from clinicians and staff; (4) engage policy-makers before starting the trial.

Duke Scholars

Author Devon Karnes Check Population Health Sciences

Author Hayden Barry Bosworth Population Health Sciences

Author Kevin Phillip Weinfurt Population Health Sciences