Tool to find opioid use disorder risk from doctors' notes and social factors
Developing a Clinical Decision Support Tool that Assesses Risk of Opioid Use Disorder Using Natural Language Processing, Machine Learning, and Social Determinants of Health from Clinical Notes
This project uses computer programs to read doctors' notes and social-risk information to find people at higher risk of opioid use disorder so clinicians can act earlier.
Quick facts
| Grant type | Career grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project ID | NIH-11138634 on NIH RePORTER |
What this research studies
Researchers will use natural language processing to pull social determinants of health from clinical notes, like housing or employment issues. They will combine those social factors with medical information using machine learning to identify patterns linked to opioid use disorder risk. The team will build a clinician-facing decision tool that provides clear risk signals and suggested resources. Finally, they will test the tool in clinics to see if it is easy to use, acceptable to staff, and practical in real care settings.
Who could benefit from this research
Good fit: Adults receiving care in participating clinics—especially those prescribed opioids or with social risk factors such as unstable housing, unemployment, or mental health concerns—are the focus for this work.
Not a fit: Patients who do not receive care within the participating health systems, lack electronic clinical notes, or have no exposure to opioids may not directly benefit from this project.
Why it matters
Potential benefit: Could help clinicians spot opioid risk earlier and connect patients to support and resources, potentially preventing overdoses and improving care.
How similar studies have performed: NLP and machine-learning tools have shown promise at identifying social risks and predicting health outcomes, but applying them specifically to flag opioid use disorder risk and embed that into clinician tools is still relatively new.
Where this research is happening
San Francisco, United States
- University of California, San Francisco — San Francisco, United States (Active)
Researchers
- Principal investigator: Brown Iii, William — University of California, San Francisco
- Study coordinator: Brown Iii, William
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 below.