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

Peter Balvanz; Daniel Harris; Ramona Olvera; Nasim Sabounchi; Carly Bridden; Jane Carpenter; Carolyn Damato-MacPherson; James David; Naleef Fareed; Erin Gibson; Tim Huerta; Tim Hunt; Sarah Kosakowski; Marc Larochelle; Nikki Lewis; David Lounsbury; Courtney Plagens; Rebecca Smeltzer; Jennifer Villani; Elwin Wu; Rachel Chase

doi:10.1097/PHH.0000000000002252

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

Peter Balvanz
, Daniel Harris
, Ramona Olvera
, Nasim Sabounchi
, Carly Bridden
, Jane Carpenter
, Carolyn Damato-MacPherson
, James David
, Naleef Fareed
, Erin Gibson
, Tim Huerta
, Tim Hunt
, Sarah Kosakowski
, Marc Larochelle
, Nikki Lewis
, David Lounsbury
, Courtney Plagens
, Rebecca Smeltzer
, Jennifer Villani
, Elwin Wu

Rachel Chase

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English
Pages (from-to)	25-33
Number of pages	9
Journal	Journal of Public Health Management and Practice
Volume	32
Issue number	1
DOIs	https://doi.org/10.1097/PHH.0000000000002252
State	Published - Jan 1 2026

Bibliographical note

Publisher Copyright:
Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.

Funding

This research was supported by the National Institutes of Health (NIH) and the Substance Abuse and Mental Health Services Administration through the NIH HEAL (Helping to End Addiction Long-term) Initiative under award numbers UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415 and UM1DA049417 (ClinicalTrials.gov Identifier: NCT04111939). Dr Vilani was substantially involved in UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415, and UM1DA049417, consistent with her role as Scientific Officer. This study protocol (Pro00038088) was approved by Advarra Inc., the HEALing Communities Study single Institutional Review Board. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the Substance Abuse and Mental Health Services Administration or the NIH HEAL Initiative.

Funders	Funder number
National Institutes of Health (NIH)
Substance Abuse and Mental Health Services Administration through the NIH HEAL (Helping to End Addiction Long-term) Initiative	UM1DA049394, UM1DA049406, UM1DA049412, UM1DA049415, UM1DA049417

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

clinical trial
community engagement
data-driven decision making
evidence-based practices
opioids

ASJC Scopus subject areas

Health Policy
Public Health, Environmental and Occupational Health

Access to Document

10.1097/PHH.0000000000002252

Cite this

Balvanz, P., Harris, D., Olvera, R., Sabounchi, N., Bridden, C., Carpenter, J., Damato-MacPherson, C., David, J., Fareed, N., Gibson, E., Huerta, T., Hunt, T., Kosakowski, S., Larochelle, M., Lewis, N., Lounsbury, D., Plagens, C., Smeltzer, R., Villani, J., ... Chase, R. (2026). Lessons From the Community-Engaged, Data-Driven Selection of Evidence-Based Practice Strategies in the HEALing Communities Study. Journal of Public Health Management and Practice, 32(1), 25-33. https://doi.org/10.1097/PHH.0000000000002252

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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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Balvanz, P, Harris, D, Olvera, R, Sabounchi, N, Bridden, C, Carpenter, J, Damato-MacPherson, C, David, J, Fareed, N, Gibson, E, Huerta, T, Hunt, T, Kosakowski, S, Larochelle, M, Lewis, N, Lounsbury, D, Plagens, C, Smeltzer, R, Villani, J, Wu, E & Chase, R 2026, 'Lessons From the Community-Engaged, Data-Driven Selection of Evidence-Based Practice Strategies in the HEALing Communities Study', Journal of Public Health Management and Practice, vol. 32, no. 1, pp. 25-33. https://doi.org/10.1097/PHH.0000000000002252

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AU - Bridden, Carly

AU - Carpenter, Jane

AU - Damato-MacPherson, Carolyn

AU - David, James

AU - Fareed, Naleef

AU - Gibson, Erin

AU - Huerta, Tim

AU - Hunt, Tim

AU - Kosakowski, Sarah

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AU - Lewis, Nikki

AU - Lounsbury, David

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AB - 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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KW - community engagement

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KW - opioids

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

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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