Helping Veterans stay on medication for opioid addiction using health data
Official title: Data to Clinical Action: Using Predictive Analytics to Improve Care of Veterans with Opioid Use Disorder
Funded at Central Arkansas Veterans Hlthcare Sys · NIH-11086153 · Data updated 2026-11-12
This project uses VA health records and AI to predict which Veterans on medication for opioid use disorder might stop treatment so care teams can offer extra support.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Central Arkansas Veterans Hlthcare Sys NIH-funded |
| Lab location | 1 site (North Little Rock, United States) |
| Project start | April 1, 2022 |
| Project end | March 31, 2027 |
| Funding year | 2026 |
| NIH project number | 5IK2HX003358-04 |
| Project ID | NIH-11086153 on NIH RePORTER |
Who could benefit from this research
✓Could help
Veterans with opioid use disorder who are currently receiving or recently started medication for OUD (MOUD) through the VA are the main candidates for this work.
✕Less likely to help
People not receiving care within the VA system, not on MOUD, or without sufficient VA health record data are unlikely to be helped by this project.
What this research studies
You would be asked to allow researchers to use your VA health records and treatment history to build and test computer models that spot who is likely to discontinue medication for opioid use disorder. The team will apply big-data and machine-learning methods to monitor risk in near-real time during active treatment. When a Veteran's risk rises, the system could flag them for outreach or added services like peer support or extra follow-up. The work focuses on Veterans receiving MOUD through the VA, primarily within the Central Arkansas VA network.
Why it matters
Potential benefit: If successful, this approach could help clinicians find Veterans at risk of stopping life-saving opioid treatment so they can provide timely support and reduce overdoses.
How similar studies have gone: Predictive analytics have successfully flagged at-risk patients in other healthcare areas, but using them specifically to prevent MOUD dropout in Veterans is relatively new with limited direct evidence so far.
Where this research is happening
-
Central Arkansas Veterans Hlthcare Sys
North Little Rock, United States Active
Researchers
- Principal investigator: Hayes, Corey J — Central Arkansas Veterans Hlthcare Sys
- Study coordinator: Hayes, Corey J
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.