Developing and Evaluating Large Language Model-Generated Emergency Medicine Handoff Notes.

Importance: An emergency medicine (EM) handoff note generated by a large language model (LLM) has the potential to reduce physician documentation burden without compromising the safety of EM-to-inpatient (IP) handoffs.

Objective: To develop LLM-generated EM-to-IP handoff notes and evaluate their accuracy and safety compared with physician-written notes.

Design, Setting, And Participants: This cohort study used EM patient medical records with acute hospital admissions that occurred in 2023 at NewYork-Presbyterian/Weill Cornell Medical Center.

A method to automate the discharge summary hospital course for neurology patients.

Objective: Generation of automated clinical notes has been posited as a strategy to mitigate physician burnout. In particular, an automated narrative summary of a patient's hospital stay could supplement the hospital course section of the discharge summary that inpatient physicians document in electronic health record (EHR) systems. In the current study, we developed and evaluated an automated method for summarizing the hospital course section using encoder-decoder sequence-to-sequence transformer models.

A Day-to-Day Approach for Automating the Hospital Course Section of the Discharge Summary.

Optimal solutions for abstractive summarization of electronic health record content have yet to be discovered. Although studies have applied state-of-the-art transformers in the clinical domain to radiology reports and information extraction, little is known of transformers' performance with the hospital course section of the discharge summary. This paper compares two summarization approaches for automating the hospital course section within the discharge summary: (1) a truncation approach that uses all clinical notes and (2) a day-to-day approach that segments the notes per clinical day.