Development and Validation of an Electronic Health Record–Based Algorithm for Identifying Patients With Long-Term Opioid Therapy: Cross-Sectional Study
Shu Huang, Tianze Jiao, Serena Jingchuan Guo, Jill A Star, Jie Xu, Jiang Bian, Debbie L Wilson, Amie J Goodin, Guy Tsafnat, Kenan Zamore, Nidhi Soley · 2025
Read Development and Validation of an Electronic Health Record–Based Algorithm for Identifying Patients With Long-Term Opioid Therapy: Cross-Sectional Study by Shu Huang, Tianze Jiao, Serena Jingchuan Guo, Jill A Star, Jie Xu, Jiang Bian, Debbie L Wilson, Amie J Goodin, Guy Tsafnat, Kenan Zamore, Nidhi Soley (2025) 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-12739455.txt.
Description
Huang and colleagues built in 2025 an algorithm that spots patients on long-term opioid therapy inside electronic health records. Claims data can identify such patients, but point-of-care tools need record-based methods. The team trained an elastic net model on Florida health record data linked to Medicaid claims. The claims definition of ninety continuous days of use served as the reference.
The model picked a compact set of features from over a hundred candidates. It matched the reference standard well in both internal and later external validation. Most true cases landed in the highest risk groups. The authors say the tool can support risk checks during clinical visits.
Catalog record
| CREATOR | Shu Huang, Tianze Jiao, Serena Jingchuan Guo, Jill A Star, Jie Xu, Jiang Bian, Debbie L Wilson, Amie J Goodin, Guy Tsafnat, Kenan Zamore, Nidhi Soley |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 41 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |