Data2Action Oregon Project: Supporting Data-driven Decision-Making for Substance Use Services, Policy, and Overdose Prevention
D2A Oregon
Supporting Data-driven Decision-Making to Support Substance Use Service Expansion Policies and to Prevent Overdoses
1 other identifier
interventional
341
1 country
1
Brief Summary
Oregon's decision makers (e.g., community service providers, public health, justice, advocacy groups, payers) are calling for comprehensive, current, and trusted data to inform how they allocate resources to improve substance use services and mitigate the growing opioid and methamphetamine epidemics in their state. Consistent with the HEAL Data2Action call for Innovation projects that drive action with data in real-world settings, this study will refine and test the impact of a novel implementation strategy to engage cross- sector decision makers and make data that they identify as relevant to their decisions available to them in easy- to-use products. The proposed study aims to not only address critical knowledge gaps regarding how and when data can inform impactful, transparent decision-making, but to provide decision makers with the data that they need to achieve community-wide substance use prevention and treatment goals, including the increased delivery of high-quality, evidence-informed, services and the prevention of overdoses.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for not_applicable
Started Feb 2025
Longer than P75 for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
January 31, 2025
CompletedStudy Start
First participant enrolled
February 6, 2025
CompletedFirst Posted
Study publicly available on registry
February 28, 2025
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 1, 2028
ExpectedStudy Completion
Last participant's last visit for all outcomes
August 31, 2028
March 16, 2026
March 1, 2026
3.3 years
January 31, 2025
March 12, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Community Engagement Survey
A measure of community engagement (Oetzel et al, 2018) which includes 2 subscales; collaboration, which includes 4 items answered on a Likert Scale ranging from Strongly Disagree to Strongly Agree, and synergy, which includes 7 items answered on a Likert Scale ranging from Not at All to To a Great Extent.
Measurement will occur at five timepoints: baseline, around 15 months post-baseline, around 18 months post-baseline, around 30 months post-baseline, around 42 months post-baseline
Secondary Outcomes (3)
Data Product Usability
Annually for up to two years after each round of data product release in the given county (varies by condition)
Social Network of Collaboration
Measurement will occur at five timepoints: baseline, around 15 months post-baseline, around 18 months post-baseline, around 30 months post-baseline, around 42 months post-baseline
Trust in data
Annually for up to two years after each round of data product release in the given county (varies by condition)
Other Outcomes (3)
Data Product: Missing Data
Annually for up to two years after each round of data product release in the given county (varies by condition)
Focus Groups
Annually, post-baseline, for up to 3 years
Evidence Use Behavior
Measurement will occur at five timepoints: baseline, around 15 months post-baseline, around 18 months post-baseline, around 30 months post-baseline, around 42 months post-baseline
Study Arms (2)
CDS
EXPERIMENTALThis group will participate in 4 CDS to co-design and tailor data products with the study team. They will receive fully tailored Data Products at T3 or T4, depending on wedge assignment.
No CDS
EXPERIMENTALThis group will not participate in CDS. They will receive standardized data products at T3 or T4, depending on wedge assignment.
Interventions
CDS uses principles and activities from Liberating Structures (LS) and Group Model Building (GMB). Each method uses semi-structured processes for engaging partners to collaborate with one another and address complex problems. Example activities and discussions include: identifying a shared vision for how data can inform decisions related to substance use service delivery and overdose prevention; identifying relevant data that should be disseminated; identifying decisions to be supported with data. Methods from human-centered design - an approach for developing products that are useful and easy to use - will be used to refine data products developed by the study team so that the data products are acceptable and useful to end users. Together, these three methods (Liberating Structures, group model building, human-centered design) will be used to engage partners to iteratively co-design products for disseminating data back to partners to inform their daily substance use service delivery.
Data products disseminate localized data from local, state, or regional-level data sources to local (i.e., county) decision makers to inform their daily decision-making. Data products in counties assigned to CDS will receive fully tailored data products, while no-CDS counties will receive standardized data products. A suite of data products will be made available to inform diverse decisions by a variety of end users. Data products will be identified and prioritized during CDS, but may include reports, policy briefs, journey maps, and technical assistance for data interpretation.
Eligibility Criteria
You may qualify if:
- Sample 1: Local or Regional Decision Makers
- At least 18 years old
- Has decision-making authority within their professional role related to substance use service delivery, including leadership responsible for developing policy (e.g. executive directors) OR middle-managers (e.g., case managers, supervisor) and front-line workers responsible for service delivery decisions OR Responsible for developing local or state policy related to substance use/behavioral health and/or the criminal justice system OR Advises these decision makers (e.g., legislative staff, data analysts)
- These individuals will be drawn from organizations with the following perspectives: behavioral health, public health, health payer, first responders, health advocacy.
- Sample 2: State or Local Decision Makers
- At least 18 years old
- Has decision-making authority within their professional role related to substance use service delivery, including leadership responsible for developing policy (e.g. executive directors) OR middle-managers (e.g., case managers, supervisor) and front-line workers responsible for service delivery decisions OR Responsible for developing local or state policy related to substance use/behavioral health and/or the criminal justice system OR Advises these decision makers (e.g., legislative staff, data analysts)
You may not qualify if:
- None.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Chestnut Health Systemslead
- National Institute on Drug Abuse (NIDA)collaborator
- University of California, San Diegocollaborator
Study Sites (1)
Chestnut Health Systems
Eugene, Oregon, 97401, United States
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Central Study Contacts
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Purpose
- HEALTH SERVICES RESEARCH
- Intervention Model
- SEQUENTIAL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Research Scientist II
Study Record Dates
First Submitted
January 31, 2025
First Posted
February 28, 2025
Study Start
February 6, 2025
Primary Completion (Estimated)
June 1, 2028
Study Completion (Estimated)
August 31, 2028
Last Updated
March 16, 2026
Record last verified: 2026-03
Data Sharing
- IPD Sharing
- Will not share
De-identified IPD collected by the study will be shared per procedures outlined in a NIH-approved Data Sharing and Management Plan as part of the NIH and NIH HEAL-initiative data sharing requirements. Some qualitative data will lose meaning if de-identified and will thus not be shared.