Physician- and Large Language Model-Generated Hospital Discharge Summaries: A Blinded, Comparative Quality and Safety Study

Importance: High quality discharge summaries are associated with improved patient outcomes but contribute to clinical documentation burden. Large language models (LLMs) provide an opportunity to support physicians by drafting discharge summary narratives. Objective: To determine whether LLM-generated discharge summary narratives are of comparable quality and safety to those of physicians. Design: Cross-sectional study. Setting: University of California, San Francisco. Participants: 100 randomly selected Inpatient Hospital Medicine encounters of 3-6 days duration between 2019-2022. Exposure: Blinded evaluation of physician- and LLM-generated narratives was performed in duplicate by 22 attending physician reviewers. Main Outcomes and Measures: Narratives were reviewed for overall quality, reviewer preference, comprehensiveness, concision, coherence, and three error types - inaccuracies, omissions, and hallucinations. Each error individually, and each narrative overall, were assigned potential harmfulness scores on a 0-7 adapted AHRQ scale. Results: Across 100 encounters, LLM- and physician-generated narratives were comparable in overall quality on a 1-5 Likert scale (average 3.67 SD 0.49 vs 3.77 SD 0.57, p=0.213) and reviewer preference (χ2 = 5.2, p=0.270). LLM-generated narratives were more concise (4.01 SD 0.37 vs. 3.70 SD 0.59; p<0.001) and more coherent (4.16 SD 0.39 vs. 4.01 SD 0.53, p=0.019) than their physician-generated counterparts, but less comprehensive (3.72 SD 0.58 vs. 4.13 SD 0.58; p<0.001). LLM-generated narratives contained more unique errors (average 2.91 SD 2.54 errors per summary) than physician-generated narratives (1.82 SD 1.94). Averaged across individual errors, there was no significant difference in the potential for harm between LLM- and physician-generated narratives (1.35 SD 1.07 vs 1.34 SD 1.05, p=0.986). Both LLM- and physician-generated narratives had low overall potential for harm (<1 on 0-7 scale), although LLM-generated narratives scored higher than physician narratives (0.84 SD 0.98 vs 0.36 SD 0.70, p<0.001). Conclusions and Relevance: In this cross-sectional study of 100 inpatient Hospital Medicine encounters, LLM-generated discharge summary narratives were of similar quality, and were preferred equally, to those generated by physicians. LLM-generated summaries were more likely to contain errors but had low overall harmfulness scores. Our findings suggest that LLMs could be used to draft discharge summary narratives of comparable quality and safety to those written by physicians.