Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts

Sifting through vast textual data and summarizing key information imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy across diverse clinical summarization tasks has not yet been rigorously examined. In this work, we employ domain adaptation methods on eight LLMs, spanning six datasets and four distinct summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not lead to improved results. Further, in a clinical reader study with six physicians, we depict that summaries from the best adapted LLM are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis delineates mutual challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and other irreplaceable human aspects of medicine.Comment: 23 pages, 22 figure

Timing and Predictors of Subspecialty Career Choice Among Internal Medicine Residents: A Retrospective Cohort Study.

BACKGROUND: Internal medicine residents face numerous career options after residency training. Little is known about when residents make their final career choice. OBJECTIVE: We assessed the timing and predictive factors of final career choices among internal medicine residents at graduation, including demographics, pre-residency career preferences, and rotation scheduling. METHODS: We conducted a retrospective study of graduates of an academic internal medicine residency program from 2014 to 2017. Main measures included demographics, rotation schedules, and self-reported career choices for residents at 5 time points: recruitment day, immediately after Match Day, end of postgraduate year 1 (PGY-1), end of PGY-2, and at graduation. RESULTS: Of the 138 residents eligible for the study, 5 were excluded based on participation in a fast-track program for an Accreditation Council for Graduate Medical Education subspecialty fellowship. Among the remaining 133 residents, 48 (36%) pursued general internal medicine fields and 78 (59%) pursued fellowship training. Career choices from recruitment day, Match Day, and PGY-1 were only weakly predictive of the career choice. Many choices demonstrated low concordance throughout training, and general medicine fields (primary care, hospital medicine) were frequently not decided until after PGY-2. Early clinical exposure to subspecialty rotations did not predict final career choice. CONCLUSIONS: Early career choices before and during residency training may have low predictability toward final career choices upon graduation in internal medicine. These choices may continue to have low predictability beyond PGY-2 for many specialties. Early clinical exposure may not predict final career choice for subspecialties

Association between Obesity and Length of COVID-19 Hospitalization: Unexpected Insights from the American Heart Association National COVID-19 Registry

BackgroundThe mechanism for possible association between obesity and poor clinical outcomes from Coronavirus Disease 2019 (COVID-19) remains unclear.MethodsWe analyzed 22,915 adult COVID-19 patients hospitalized from March 2020 to April 2021 to non-intensive care using the American Heart Association National COVID Registry. A multivariable Poisson model adjusted for age, sex, medical history, admission respiratory status, hospitalization characteristics, and laboratory findings was used to calculate length of stay (LOS) as a function of body mass index (BMI). We similarly analyzed 5,327 patients admitted to intensive care for comparison.ResultsRelative to normal BMI subjects, overweight, class I obese, and class II obese patients had approximately half-day reductions in LOS (-0.469 days, P<0.01; -0.480 days, P<0.01; -0.578 days, P<0.01, respectively).ConclusionThe model identified a dose-dependent, inverse relationship between BMI category and LOS for COVID-19, which was not seen when the model was applied to critically ill patients

Assessing the relationship between American Heart Association atherosclerotic cardiovascular disease risk score and coronary artery imaging findings

Objective The aim of this study was to characterize the relationship between computed tomography angiography imaging characteristics of coronary artery and atherosclerotic cardiovascular disease (ASCVD) score. Methods We retrospectively identified all patients who underwent a coronary computed tomography angiography at our institution from December 2013 to July 2016, then we calculated the 10-year ASCVD score. We characterized the relationship between coronary artery imaging findings and ASCVD risk score. Results One hundred fifty-one patients met our inclusion criteria. Patients with a 10-year ASCVD score of 7.5% or greater had significantly more arterial segments showing stenosis (46.4%, P = 0.008) and significantly higher maximal plaque thickness (1.25 vs 0.53, P = 0.001). However, among 56 patients with a 10-year ASCVD score of 7.5% or greater, 30 (53.6%) had no arterial stenosis. Furthermore, among the patients with a 10-year ASCVD score of less than 7.5%, 24 (25.3%) had some arterial stenosis. Conclusions There is some concordance but not a perfect overlap between 10-year ASCVD risk scores and coronary artery imaging findings

Baricitinib Treatment of Coronavirus Disease 2019 Is Associated With a Reduction in Secondary Infections

We performed a secondary analysis of the National Institutes of Health-sponsored Adaptive COVID-19 Treatment Trial (ACTT-2) randomized controlled trial and found that baricitinib was associated with a 50% reduction in secondary infections after controlling for baseline and postrandomization patient characteristics. This finding provides a novel mechanism of benefit for baricitinib and supports the safety profile of this immunomodulator for the treatment of coronavirus disease 2019