Sifting through vast textual data and summarizing key information from electronic health records (EHR) 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 on a diverse range of clinical summarization tasks has not yet been rigorously demonstrated. In this work, we apply domain adaptation methods to eight LLMs, spanning six datasets and four distinct clinical 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 improve results. Further, in a clinical reader study with ten physicians, we show that summaries from our best-adapted LLMs are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis highlights 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 the inherently human aspects of medicine.

Objective: The aim of the study was to examine the impact of a surgical comanagement (SCM) hospitalist program on patient outcomes at an academic institution. Background: Prior studies may have underestimated the impact of SCM due to methodological shortcomings. Methods: This is a retrospective study utilizing a propensity score-weighted intervention (n = 16,930) and control group (n = 3695). Patients were admitted between January 2009 to July 2012 (pre-SCM) and September 2012 to September 2013 (post-SCM) to Orthopedic or Neurosurgery at our institution. Using propensity score methods, linear regression, and a difference-in-difference approach, we estimated changes in outcomes between pre and post periods, while adjusting for confounding patient characteristics. Results: The SCM intervention was associated with a significant differential decrease in the proportion of patients with at least 1 medical complication [odds ratio (OR) 0.86; 95% confidence interval (CI), 0.74–0.96; P = 0.008), the proportion of patients with length of stay at least 5 days (OR 0.75; 95% CI, 0.67–0.84; P < 0.001), 30-day readmission rate for medical cause (OR 0.67; 95% CI, 0.52–0.81; P < 0.001), and the proportion of patients with at least 2 medical consultants (OR 0.55; 95% CI, 0.49–0.63; P < 0.001). There was no significant change in patient satisfaction (OR 1.08; 95% CI, 0.87–1.33; P = 0.507). We estimated average savings of $2642 to $4303 per patient in the post-SCM group. The overall provider satisfaction with SCM was 88.3%. Conclusions: The SCM intervention reduces medical complications, length of stay, 30-day readmissions, number of consultants, and cost of care.

Patients with increasing age and medical complexity are undergoing colorectal surgery. Medical complications are not uncommon and may contribute to higher mortality. We implemented a surgical comanagement (SCM) model in July 2014 at our institution, where the same 2 SCM hospitalists were dedicated to colorectal surgery year round. Each patient was screened daily by an SCM hospitalist for prevention and management of medical complications. Before SCM, hospitalists were typically consulted after medical complications had occurred. We conducted a pre/post study at an academic medical center with 938 patients in the pre-SCM group (July 2012 to June 2014), and 1,062 patients in the post-SCM group (July 2014 to May 2016). We evaluated whether SCM by hospitalists improved outcomes of patients in colorectal surgery. There was no significant difference in medical complications, patient satisfaction, or 30-day readmission rate to our institution for medical cause with the SCM intervention. This intervention was associated with a significant decrease in the proportion of patients transferred to the ICU after rapid response team calls (relative risk [RR] 0.25 [95% CI 0.05 to 0.84], p = 0.039), proportion of patients with length of stay (LOS) ≥ 5 days (RR 0.73 [95% CI 0.64 to 0.83], p < 0.001), use of medical consultants (RR 0.75 [95% CI 0.63 to 0.89], p = 0.001), and the median direct cost of care by 10.3% (p = 0.0002). Surgical comanagement intervention was associated with a decrease in transfers to the ICU after rapid response team call, LOS, medical consultants, and the cost of care.

Surgical comanagement (SCM), in which surgeons and hospitalists share responsibility of care for surgical patients, has been increasingly utilized. In August 2012, we implemented SCM in Orthopedic and Neurosurgery services in which the same Internal Medicine hospitalists are dedicated year round to each of these surgical services to proactively prevent and manage medical conditions. In this article, we evaluate if SCM was associated with continued improvement in patient outcomes between 2012 and 2018 in Orthopedic and Neurosurgery services at our institution. We conducted regression analysis on 26,380 discharges to assess yearly change in our outcomes. Since 2012, the odds of patients with ≥1 medical complication decreased by 3.8% per year (P = .01), the estimated length of stay decreased by 0.3 days per year (P < .0001), and the odds of rapid response team calls decreased by 12.2% per year (P = .001). Estimated average direct cost savings were $3,424 per discharge.