Machine Learning Prediction and Reducing Overdoses With Electronic Health Record Nudges (mPROVEN) in the Primary Care Setting: Protocol for a Cluster Randomized Controlled Trial
Walid F Gellad, Yi-Fan Chen, Tae Woo Park, Qingnan Yang, Jonathan D Arnold, Courtney C Kuza, Stephanie N Fedro-Byrom, Julie Diiulio, Laura G Militello, Michelle Whitlock, Eugene M Sadhu, Shyam Visweswaran, Michael J Fine, Kaleab Z Abebe, Katie J Suda, Wei-Hsuan Lo-Ciganic · 2026
Read Machine Learning Prediction and Reducing Overdoses With Electronic Health Record Nudges (mPROVEN) in the Primary Care Setting: Protocol for a Cluster Randomized Controlled Trial by Walid F Gellad, Yi-Fan Chen, Tae Woo Park, Qingnan Yang, Jonathan D Arnold, Courtney C Kuza, Stephanie N Fedro-Byrom, Julie Diiulio, Laura G Militello, Michelle Whitlock, Eugene M Sadhu, Shyam Visweswaran, Michael J Fine, Kaleab Z Abebe, Katie J Suda, Wei-Hsuan Lo-Ciganic (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13184600.txt.
Description
Walid Gellad and co-authors set out a trial protocol that pairs machine learning risk prediction with record nudges to cut opioid overdoses, in a protocol paper from 2026. Overdose is a leading preventable cause of death in the United States. Rule-based risk flags misclassify many patients, so prescribing chances to lower risk get missed.
The pragmatic trial randomizes primary care practices in a large health system. A validated learning model flags adults at elevated risk. Nudges in the health record, shaped by behavioral science, then prompt safer prescribing for those patients. Outcomes track prescribing behavior and, in time, overdose itself. The design tests prediction plus action together.
Catalog record
| CREATOR | Walid F Gellad, Yi-Fan Chen, Tae Woo Park, Qingnan Yang, Jonathan D Arnold, Courtney C Kuza, Stephanie N Fedro-Byrom, Julie Diiulio, Laura G Militello, Michelle Whitlock, Eugene M Sadhu, Shyam Visweswaran, Michael J Fine, Kaleab Z Abebe, Katie J Suda, Wei-Hsuan Lo-Ciganic |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 34 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |