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Chatting new territory: large language models for infection surveillance from pilot to deployment

Published online by Cambridge University Press:  14 February 2025

Julie T. Wu Open the ORCID record for Julie T. Wu [Opens in a new window] ,
Bradley J. Langford Open the ORCID record for Bradley J. Langford [Opens in a new window] ,
Erica S. Shenoy Open the ORCID record for Erica S. Shenoy [Opens in a new window] ,
Evan Carey Open the ORCID record for Evan Carey [Opens in a new window]  and
Westyn Branch-Elliman Open the ORCID record for Westyn Branch-Elliman [Opens in a new window]
Julie T. Wu*
Affiliation:
Department of Medicine, VA Palo Alto Healthcare System, Palo Alto, CA, USA Department of Medicine, Division of Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
Bradley J. Langford
Affiliation:
Dalla Lana School of Public Health, University of Toronto, Canada
Erica S. Shenoy
Affiliation:
Infection Control, Mass General Brigham, Boston, MA, USA Division of Infections Diseases, Massachusetts General Hospital, Boston, MA, US Harvard Medical School, Boston, MA, USA
Evan Carey
Affiliation:
Digital Health Office, Veterans Health Administration, National Artificial Intelligence Institute, Washington, DC, USA Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
Westyn Branch-Elliman
Affiliation:
Department of Medicine, Section of Infectious Diseases, VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Digital Health Office, National Artificial Intelligence Institute, Washington, DC, USA Department of Medicine, Section of Infectious Diseases, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
*
Corresponding author: Julie T. Wu; Email: JulieWu@stanford.edu
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Abstract

Rodriguez-Nava et al. present a proof-of-concept study evaluating the use of a secure large language model (LLM) approved for healthcare data for retrospective identification of a specific healthcare-associated infection (HAI)—central line-associated bloodstream infections—from real patient data for the purposes of surveillance.1 This study illustrates a promising direction for how LLMs can, at a minimum, semi-automate or streamline HAI surveillance activities.

Information

Type
Commentary
Information
Infection Control & Hospital Epidemiology , Volume 46 , Issue 3 , March 2025 , pp. 224 - 226
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

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References

Rodriguez-Nava, G, Egoryan, G, Goodman, KE, Morgan, DJ, Salinas, JL. Performance of a large language model for identifying central line-associated bloodstream infections (CLABSI) using real clinical notes. Infect Control Hosp Epidemiol 2024 Oct 30:1–4.CrossRefGoogle Scholar
Cool Stuff Now: Epic and Generative AI | Epic. https://www.epic.com/epic/post/cool-stuff-now-epic-and-generative-ai/.Google Scholar
VA AI Use Case Inventory. https://department.va.gov/ai/ai-use-case-inventory/.Google Scholar
Branch-Elliman, W, Sundermann, AJ, Wiens, J, Shenoy, ES. Leveraging electronic data to expand infection detection beyond traditional settings and definitions (Part II/III). Antimicrob Steward Healthc Epidemiol 2023;3:e27.CrossRefGoogle ScholarPubMed
Yan, LKQ, Niu, Q, Li, M, et al. Large language model benchmarks in medical tasks. Published online December 9, 2024.CrossRefGoogle Scholar