Identification of Long-Term Care Facility Residence From Admission Notes Using Large Language Models
Authors: Katherine E. Goodman, Matthew L. Robinson, Seyed M. Shams, Pilar Beccar-Varela, Suiyini Fiawoo, Nathan Kwon, Jae Hyoung Lee, Abigail H. Vorsteg, Monica Taneja, Laurence S. Magder, Mark Sutherland, Scott Sorongon, Pranita D. Tamma, Daniel J. Morgan, Philip Resnik, Anthony D. Harris, Eili Y. Klein
Journal: JAMA Network Open (2025)

Importance An estimated half of all long-term care facility (LTCF) residents are colonized with antimicrobial-resistant organisms, and early identification of these patients on admission to acute care hospitals is a core strategy for preventing intrahospital spread. However, because LTCF exposure is not reliably captured in structured electronic health record data, LTCF-exposed patients routinely go undetected. Large language models (LLMs) offer a promising, but untested, opportunity for extracting this information from patient admission histories. Objective To evaluate the performance of an LLM against human review for identifying recent LTCF exposure from identifiable patient admission histories. Design, Setting, and Participants This cross-sectional, multicenter study used the history and physical (H&amp;amp;amp;P) notes from unique, randomly sampled adult admissions occurring between January 1, 2016, and December 31, 2021, at 13 hospitals in the University of Maryland Medical System (UMMS) and the John Hopkins (Hopkins) health care system to compare the performance of an LLM (GPT-4-Turbo) using zero-shot learning and prompting against humans in identifying patients with recent LTCF exposure. LLM analyses were conducted from August to September 2024. Exposure Recent (≤12 months) LTCF exposure documented in the H&amp;amp;amp;P note, as adjudicated by (1) humans and (2) an LLM. Main Outcomes and Measures LLM sensitivity and specificity with Clopper-Pearson 95% CIs. Secondary outcomes were note review time and cost. The LLM was also prompted to provide a rationale and supporting note-text for each classification. Results The study included 359 601 eligible adult admissions, of which 2087 randomly sampled H&amp;amp;amp;P notes were manually reviewed at UMMS (1020 individuals; median [IQR] age, 58 [41-71] years; 493 [48%] male) and Hopkins (1067 individuals; median [IQR] age, 58 [48-67] years; 561 [53%] male) for LTCF residence. Compared with human review, the LLM achieved a sensitivity of 97% (95% CI, 91%-100%) and a specificity of 98% (95% CI, 97%-99%) at UMMS, and 96% (95% CI, 86%-100%) and 93% (95% CI, 92%-95%) sensitivity and specificity, respectively, at Hopkins; specificity at Hopkins improved with prompt revision (96% [95% CI, 95%-97%]). Of 117 manually reviewed LLM rationales, all were factually correct and quoted note-text accurately, and some demonstrated inferential logic and external knowledge. The LLM identified 37 (1.8%) human errors. Human review time had a mean of 2.5 minutes and cost $0.63 to $0.83 per note vs a mean of 4 to 6 seconds and $0.03 per note for LLM review. Conclusions and Relevance In this 13-hospital study of 2087 adult admissions, an LLM accurately identified LTCF residence from H&amp;amp;amp;P notes and was more than 25 times faster and 20 times less expensive than human review.