Lessons From the Community-Engaged, Data-Driven Selection of Evidence-Based Practice Strategies in the HEALing Communities Study

Public health data and tools have proliferated, yet practical guidance for community-engaged data-driven decision making is limited. The HEALing Communities Study (HCS) was a randomized, wait-list controlled trial to assess the impact of an intervention to reduce fatal opioid overdoses in 67 highly affected communities across 4 sites (Kentucky, Massachusetts, New York, and Ohio). HCS researchers implemented the Communities That HEAL intervention, a phased approach which included a coalition-engaged, data-driven approach to selection of evidence-based practice strategies to reduce fatal opioid overdoses. Core steps to the data-driven approach included data selection, access, display, and engagement. Staff selected metrics that aligned with study goals, accessed data from numerous sources, created visualizations, and engaged coalition members to assess resource gaps and intervention opportunities. At the intervention conclusion, all 4 sites' staff collectively workshopped best practices and barriers encountered to data-driven decision making. This article explains the data-driven decision-making approach implemented, assessment results, alterations for subsequent implementation, and guidance for future implementations.