Leveraging a large language model to support expansion of surveillance activities to include cardiovascular implantable device infections in a large, integrated national healthcare system
LLMs can help streamline the review process for healthcare-associated infection surveillance, but manual adjudication of output is needed to ensure the correct timeline of events and attribution.
Authors
- Julie H. Levison (ORCID: https://orcid.org/0000-0002-5042-9791)
- Kaeli Yuen
- Samuel W. Golenbock (ORCID: https://orcid.org/0000-0001-7270-3643)
- Westyn Branch‐Elliman (ORCID: https://orcid.org/0000-0002-9658-5124)
- Hillary J. Mull (ORCID: https://orcid.org/0000-0002-4484-0445)
- Mei‐Hwei Chang (ORCID: https://orcid.org/0000-0002-3648-9261)
- Russell N. Olmsted
- Dipandita Basnet (ORCID: https://orcid.org/0009-0002-9222-2710)
- Rebecca P Lamkin
- Daniel J. Morgan
- Dimitri Drekonja
- Kimberly Harvey
Institutions
- Boston University (US)
- University of Maryland, Baltimore (US)
- Michael E. DeBakey VA Medical Center (US)
- University of Wisconsin–Madison (US)
- Harvard University (US)
- Baylor College of Medicine (US)
- VA Boston Healthcare System (US)
- Office of the Chief Scientist (IL)
- VA West Los Angeles Medical Center (US)
- Minneapolis VA Health Care System (US)
- Olive View-UCLA Medical Center (US)
- William S. Middleton Memorial Veterans Hospital (US)
- VA Greater Los Angeles Healthcare System (US)
- VA Maryland Health Care System (US)
Publication Details
- Journal
- Infection Control and Hospital Epidemiology
- Published
- 2026-01-23
- DOI
- https://doi.org/10.1017/ice.2025.10384
- Primary Topic
- Electronic Health Records Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00