Evaluating Hospital Course Summarization by an Electronic Health Record-Based Large Language Model.

Importance: 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: 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.

Clinical Decision Support Leveraging Health Information Exchange Improves Concordance with Patients' Resuscitation Orders and End-of-Life Wishes.

This study aims to improve concordance between patient end-of-life preferences and code status orders by incorporating data from a state registry with clinical decision support (CDS) within the electronic health record (EHR) to preserve patient autonomy and ensure that patients receive care that aligns with their wishes.Leveraging a health information exchange (HIE) interface between the New York State Medical Orders for Life-Sustaining Treatment (eMOLST) registry and the EHR of our academic health system, we developed a bundled CDS intervention that displays eMOLST information at the time of code status ordering and provides an in-line alert when providers enter a resuscitation order discordant with wishes documented in the eMOLST registry. To evaluate this intervention, we performed a segmented regression analysis of an interrupted time series to compare the percentage of discordant orders before and after implementation among all hospitalizations for which an eMOLST was available.