Using Generative Artificial Intelligence for Healthcare-Associated Infection Surveillance
Generative artificial intelligence (GenAI) is becoming widespread in society but has had limited impact on medicine. GenAI can rapidly digest large documents, making it particularly well suited for healthcare-associated infection (HAI) surveillance that is largely based on clinical notes. In this article, we review evidence on the application of GenAI for HAI surveillance and highlight the necessary steps for changes to practice. Small studies of simulated patients and retrospective studies of patient records have found GenAI can isolate individual criteria for HAI and make HAI determinations with accuracy similar to that of human experts. GenAI tools are being developed to assist in HAI detection. Use of GenAI for HAI review could reduce variability between facilities, improve efficiency of HAI review. and lead to more time for direct prevention work. For widespread adoption, GenAI HAI detection must be safe and effective in prospective clinical studies.
Authors
- Jorge Salinas (ORCID: https://orcid.org/0000-0002-9193-820X)
- Lisa Pineles (ORCID: https://orcid.org/0000-0002-2377-5971)
- Eili Klein (ORCID: https://orcid.org/0000-0002-1304-5289)
- Shatha Alshanqeeti (ORCID: https://orcid.org/0000-0003-4691-5743)
- Gregory M. Schrank (ORCID: https://orcid.org/0000-0001-9030-4224)
- Erica S. Shenoy (ORCID: https://orcid.org/0000-0001-8086-1123)
- Katherine E Goodman (ORCID: https://orcid.org/0000-0003-2851-775X)
- Makoto Jones (ORCID: https://orcid.org/0000-0001-5580-6739)
- Westyn Branch‐Elliman (ORCID: https://orcid.org/0000-0002-9658-5124)
- S. Singh
- Anthony D. Harris (ORCID: https://orcid.org/0000-0003-2193-1560)
- Daniel J. Morgan (ORCID: https://orcid.org/0000-0002-2933-6208)
Institutions
- University of Maryland, Baltimore (US)
- United States Department of Veterans Affairs (US)
- Harvard University (US)
- Johns Hopkins University (US)
- University of California, Los Angeles (US)
- Palo Alto University (US)
- University of Utah (US)
- Johns Hopkins Medicine (US)
- VA Office of Research and Development (US)
- Massachusetts General Hospital (US)
- VA Salt Lake City Healthcare System (US)
- Institute of Computing Technology (CN)
- Institute of Human Virology (NG)
- Center for the Study of Healthcare Provider Behavior (US)
- Johns Hopkins Hospital (US)
- VA Long Beach Healthcare System (US)
- VA Greater Los Angeles Healthcare System (US)
- VA Maryland Health Care System (US)
- Stanford University (US)
Publication Details
- Journal
- Clinical Infectious Diseases
- Published
- 2025-11-11
- DOI
- https://doi.org/10.1093/cid/ciaf619
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48