Faculty Bibliography

IMPORTANCE/UNASSIGNED:Hospital course (HC) summarization represents an increasingly onerous discharge summary component for physicians. Literature supports large language models (LLMs) for HC summarization, but whether physicians can effectively partner with electronic health record-embedded LLMs to draft HCs is unknown. OBJECTIVES/UNASSIGNED:To compare the editing effort required by time-constrained resident physicians to improve LLM- vs physician-generated HCs toward a novel 4Cs (complete, concise, cohesive, and confabulation-free) HC. DESIGN, SETTING, AND PARTICIPANTS/UNASSIGNED:Quality improvement study using a convenience sample of 10 internal medicine resident editors, 8 hospitalist evaluators, and randomly selected general medicine admissions in December 2023 lasting 4 to 8 days at New York University Langone Health. EXPOSURES/UNASSIGNED:Residents and hospitalists reviewed randomly assigned patient medical records for 10 minutes. Residents blinded to author type who edited each HC pair (physician and LLM) for quality in 3 minutes, followed by comparative ratings by attending hospitalists. MAIN OUTCOMES AND MEASURES/UNASSIGNED:Editing effort was quantified by analyzing the edits that occurred on the HC pairs after controlling for length (percentage edited) and the degree to which the original HCs' meaning was altered (semantic change). Hospitalists compared edited HC pairs with A/B testing on the 4Cs (5-point Likert scales converted to 10-point bidirectional scales). RESULTS/UNASSIGNED:Among 100 admissions, compared with physician HCs, residents edited a smaller percentage of LLM HCs (LLM mean [SD], 31.5% [16.6%] vs physicians, 44.8% [20.0%]; P < .001). Additionally, LLM HCs required less semantic change (LLM mean [SD], 2.4% [1.6%] vs physicians, 4.9% [3.5%]; P < .001). Attending physicians deemed LLM HCs to be more complete (mean [SD] difference LLM vs physicians on 10-point bidirectional scale, 3.00 [5.28]; P < .001), similarly concise (mean [SD], -1.02 [6.08]; P = .20), and cohesive (mean [SD], 0.70 [6.14]; P = .60), but with more confabulations (mean [SD], -0.98 [3.53]; P = .002). The composite scores were similar (mean [SD] difference LLM vs physician on 40-point bidirectional scale, 1.70 [14.24]; P = .46). CONCLUSIONS AND RELEVANCE/UNASSIGNED:Electronic health record-embedded LLM HCs required less editing than physician-generated HCs to approach a quality standard, resulting in HCs that were comparably or more complete, concise, and cohesive, but contained more confabulations. Despite the potential influence of artificial time constraints, this study supports the feasibility of a physician-LLM partnership for writing HCs and provides a basis for monitoring LLM HCs in clinical practice.

Smoking is common in patients with HIV and is associated with increased morbidity and mortality. With the goal of targeting future cessation interventions, we sought to identify factors associated with smoking status, readiness and confidence in cessation, and success in quitting. As part of a larger study in New York City assessing predictors of chronic obstructive pulmonary disease (COPD), we enrolled HIV-infected subjects at least 35 years of age without known asthma or COPD. Current smokers received detailed tobacco history, and smoking status was assessed by chart review at 3 and 6 months post-enrollment. Two hundred subjects were enrolled (29% current smokers, 31.5% never smokers, 39.5% former smokers, mean age of 49, 84% male, 64% had AIDS, and 97% were receiving antiretroviral therapy). Current smokers had higher unemployment and increased rates of other substance use than former smokers or never smokers. In multivariate analysis, being unemployed and having used inhalant drugs were associated with current smoking. Substance abuse history was not correlated with readiness to quit or patient estimated cessation. Lower education was associated with decreased readiness to quit. Follow-up smoking status for baseline current smokers was available for 47/58 enrollees at 6 months; 4 (9%) stopped smoking completely, and 17 (36%) decreased the number of packs-per-day. Smoking and concomitant substance abuse is common in HIV, and special attention should be given to this issue, in addition to a patient's readiness to quit, when implementing tobacco cessation protocols, especially in busy urban HIV care centers.