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Lessons From the Community-Engaged, Data-Driven Selection of Evidence-Based Practice Strategies in the HEALing
Peter Balvanz1, Daniel Harris, Ramona Olvera
1Author Affiliations: General Internal Medicine, Boston Medical Center, Boston, MA (Mr Balvanz, Ms Bridden, Ms Damato-MacPherson, Ms Kosakowski, Dr Larochelle); Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky (Dr Harris); Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research (CATALYST), College of Medicine, The Ohio State University, Columbus, Ohio (Dr Olvera); School of Social Work, Columbia University, New York, New York (Dr Sabounchi, Mr David, Dr Hunt, Dr Lounsbury, Dr Wu); Families First Parenting Programs, Watertown, Massachusetts (Ms Carpenter); Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio (Dr Fareed); College of Medicine, The Ohio State University, HEALing Communities Study, Columbus, Ohio (Dr Olvera, Dr Fareed, Dr Huerta, Ms Plagens, Dr Chase); Tufts Clinical and Translational Science Institute, Boston, Massachusetts (Ms Gibson); Boston University Chobanian & Avedisian School of Medicine (Dr Larochelle); Berkshire Regional Planning Commission, Pittsfield, Massachusetts (Ms Lewis); Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California (Ms Smeltzer) and Division of Program Coordination, Planning, and Strategic Initiatives, National Institute on Drug Abuse, Bethesda, Maryland (Dr Villani).
Abstract:
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.
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