Creating a tool to assess the risk of opioid use disorder using patient notes and data analysis
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 study is creating a helpful tool that uses technology to look at important social factors from doctors' notes, so healthcare providers can better understand a patient's risk for opioid use disorder and give them the right support, all while making sure the tool is easy to use in real-life medical settings.
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-10890822 on NIH RePORTER |
What this research studies
This research aims to develop a clinical decision support tool that utilizes natural language processing and machine learning to evaluate social determinants of health from clinical notes. By analyzing these factors, the tool will help healthcare providers quickly assess a patient's risk for opioid use disorder (OUD) and provide actionable recommendations for intervention. The study will also evaluate the usability and feasibility of this tool in real clinical settings, ultimately aiming to improve patient care and outcomes related to OUD.
Who could benefit from this research
Good fit: Ideal candidates for this research include patients who are at risk for opioid use disorder, particularly those with social determinants of health that may contribute to their risk.
Not a fit: Patients who are not at risk for opioid use disorder or those without relevant social determinants of health may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to earlier identification and intervention for patients at risk of opioid use disorder, potentially reducing the incidence of OUD and related complications.
How similar studies have performed: Other research has shown success in using similar approaches to assess health risks through data analysis and natural language processing, indicating a promising avenue for this study.
Where this research is happening
San Francisco, United States
- University of California, San Francisco — San Francisco, United States (Active)
Researchers
- Principal investigator: Brown, William — University of California, San Francisco
- Study coordinator: Brown, 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.