Using Generative Artificial Intelligence to Identify Central Line–associated Bloodstream Infections

BACKGROUND: Central line-associated blood stream infection (CLABSI) surveillance is mandated and publicly reported in United States hospitals but requires manual chart review. Generative artificial intelligence (AI) may facilitate CLABSI identification. METHODS: We performed a retrospective cohort study of CLABSI surveillance using standardized prompts, clinical data, and Centers for Disease Control and Prevention definitions. In 11 hospitals, 24 infection control nurses and physicians retrospectively reviewed 220 CLABSI/non-CLABSI bacteremias. Three methods of review were compared to original facility expert review: (1) AI-assist review, (2) AI-alone review, and (3) repeat expert review. Disagreement between any method of review was adjudicated by a 2-expert physician referee panel. RESULTS: AI-assisted review had a 93.2% (205/220; 95% confidence interval [CI], 89.0-96.1) overall accuracy (agreement with facility reporting or refereeing panel) versus AI-alone review, 90.0% (198/220; 95% CI, 85.3-93.6) overall accuracy or repeat expert review, 88.2% (97/110; 95% CI, 80.1-93.6) overall accuracy. Inter-site variability was greater for repeat expert review than AI-assist. AI-assisted review required a median of 14 minutes (interquartile range 6-25.5) versus 25 minutes (interquartile range 11-43) for repeat expert (P = .0001). AI-assisted review was reported as low effort in 52% of cases versus 21% of repeat expert cases (P < .0001). AI-assisted review was reported as somewhat or very objective in 72% of cases versus 61% of repeat expert cases (P = .092). CONCLUSIONS: CLABSI detection by AI methods was at least as accurate as traditional methods, better liked by experts, faster and less variable. AI-assisted review could improve healthcare-associated infection reporting.