ArticleInfection control and hospital epidemiology2026
Leveraging a large language model to support expansion of surveillance activities to include cardiovascular implantable device infections in a large, integrated national healthcare system.
Article in Infection control and hospital epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05020418 (Promoting De-Implementation of Inappropriate Antimicrobial Use in Cardiac Device Procedures By Expanding Audit and Feedback), which is not on this map. Not yet cited in PubMed.
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The trial behind it
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Promoting De-Implementation of Inappropriate Antimicrobial Use in Cardiac Device Procedures By Expanding Audit and Feedback
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Authors and funding
12 authors.
Funding
Abstract
backgroundSurveillance activities are emerging as exemplar use cases for large language models (LLMs) in health care. The aim of this study was to evaluate the potential for LLMs to support the expansion of surveillance activities to include cardiovascular implantable electronic device (CIED) procedures.
methodsA validated machine learning-based infection flagging tool was applied to a cohort of VA CIED procedures from 7/1/2021 to 9/30/2023; cases with ≥10% probability of CIED infection underwent manual review. Then, a weighted random sample of 50 infected and 50 uninfected cases was reviewed with generative artificial intelligence (GenAI) assistance. GenAI prompts were iteratively refined to extract and classify all components of infection-related variables from clinical notes. Data extracted by GenAI were compared with manual chart reviews to assess infection status and extraction consistency.
resultsAmong 12,927 CIED procedures, 334 (2.58%) had ≥10% probability of CIED infection. Among 100 sampled cases, 50 of 50 uninfected cases were correctly categorized. Among 50 infection cases, GenAI identified all CIED infections, but the timing of events and the attribution to a preceding procedure were incorrect in 7 of 50 cases. The overall specificity of the GenAI-assisted process was 100% and the sensitivity for accurately classifying timing and attribution of CIED infection events was 82%. Errors in timing improved with iterative prompt updates. Manual chart reviews averaged 25 minutes per chart; the GenAI-assisted process averaged 5-7 minutes per chart.
conclusionsLLMs 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.
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Registered trials
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