Use of a large language model integrated within the electronic medical record for the evaluation of surgical site infections – Northern California, 2025
Our study evaluated a large language model (gpt-4o-mini) for surgical site infection (SSI) adjudication, achieving 100% sensitivity but 69.4% specificity. While reducing the manual screening workload by 66%, the agent generated many false positives, underscoring the need for refined models to improve specificity without compromising accuracy.
Authors
- Mindy Marie Sampson (ORCID: https://orcid.org/0000-0001-5724-5875)
- Eugenia Miranti (ORCID: https://orcid.org/0000-0002-0654-8937)
- Wajeeha Tariq (ORCID: https://orcid.org/0000-0001-8387-4309)
- Timothy Keyes (ORCID: https://orcid.org/0000-0003-0423-9679)
- Nerissa Ambers (ORCID: https://orcid.org/0009-0008-9878-1824)
- Alvaro Ayala
- Erika Paola Viana-Cardenas
- Elmer de Leon
- Jorge L. Salinas (ORCID: https://orcid.org/0000-0002-9193-820X)
- Gina Newman (ORCID: https://orcid.org/0009-0008-8456-954X)
Institutions
- Stanford Health Care (US)
- Stanford University (US)
Publication Details
- Journal
- Infection Control and Hospital Epidemiology
- Published
- 2026-04-13
- DOI
- https://doi.org/10.1017/ice.2026.10432
- Primary Topic
- Surgical site infection prevention
- Type
- article
- Field-Weighted Citation Impact
- 0.00