Use of the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) framework to guide iterative adaptations: Applications, lessons learned, and future directions.

Introduction: Implementation science frameworks have been used widely for planning and evaluation, but seldom to guide adaptations during program implementation. There is great potential for these frameworks to be used to inform conceptual and data-driven decisions about adaptations.

Methods: We summarize recent applications using Iterative RE-AIM to capture and guide adaptations. Iterative RE-AIM can be repeated at multiple time points customized to each project and involves the following activities: identification of key implementation partners; rating importance of and progress on each RE-AIM dimension (reach, effectiveness, adoption, implementation, and maintenance); use of summary data on ratings to identify one or two RE-AIM dimensions for adaptations and implementation strategies; and evaluation of progress and impact of adaptations. We summarize recent and ongoing Iterative RE-AIM applications across multiple care coordination and pain management projects within the Veterans Health Administration, a hypertension control trial in Guatemala, a hospital-based lung ultrasound implementation pilot, and a colorectal cancer screening program in underserved communities.

Results: Iterative RE-AIM appears feasible, helpful, and broadly applicable across diverse health care issues, interventions, contexts, and populations. In general, the RE-AIM dimension showing the largest gap between importance and progress has been Reach. The dimensions most frequently selected for improvement have been Reach and Implementation. We discuss commonalities, differences and lessons learned across these various applications of Iterative RE-AIM. Challenges include having objective real time data on which to make decisions, having key implementation staff available for all assessments, and rapidly scoring and providing actionable feedback. We discuss print and online resources and materials to support Iterative RE-AIM.

Conclusions: The use of Iterative RE-AIM to guide and support understanding of adaptations has proven feasible across diverse projects and in multiple case studies, but there are still questions about its strengths, limitations, essential components, efficiency, comparative effectiveness, and delivery details. Future directions include investigating the optimal frequency and timing for iterative applications; adding contextual assessments; developing more continuous and rapid data on which to make adaptation decisions; identifying opportunities to enhance health equity; and determining the level of facilitation that is most cost-effective.