Use of a large language model integrated within the electronic medical record for the evaluation of surgical site infections – Northern California, 2025

2026/04/13 by Eugenia Miranti, Timothy Keyes, Alvaro Ayala +7 · 1 voice
Biochemistry, Genetics and Molecular Biology · Medicine · #Bacterial Identification and Susceptibility Testing #Sepsis Diagnosis and Treatment #Surgical site infection prevention

paper · pdf · doi:10.1017/ice.2026.10432

openalex publication_date 2026/04/13 · openalex created_date 2026/04/14 · openalex updated_date 2026/06/16

Abstract

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.

Citations

• Performance of a large language model for identifying central line-associated bloodstream infections (CLABSI) using real clinical notes
• Unleashing the potential of prompt engineering for large language models

Discussions

• New in ICHE from @jlsalinas.bsky.social and authors: ➡️ A large language model can adjudicate surgical site infections with 100% sensitivity ➡️ Infection Preventionists share their thoughts on use o [bsky, 4 points, 1 comments]

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