Large Language Model Summarization of Physician-to-Physician Calls for Interhospital Transfer of Patients With ST-Elevation Myocardial Infarction: Observational Study.

Background: Interhospital transfer of patients with suspected ST-elevation myocardial infarction (STEMI) requires timely and robust communication. Clinical uptake of potentially useful information from physician-to-physician phone calls authorizing transfer is low at many institutions, at least in part due to relative inaccessibility of call audio and lack of transcripts or summaries. Large language models (LLMs) can transform text into brief, consistently formatted summaries that could be made available in the electronic health record, thus facilitating the timely availability of clinically relevant data to physicians downstream of the transfer call. Objective: We sought to assess the feasibility of using transcription and LLM summarization to provide written information summarizing transfer calls by adapting the Physician Documentation Quality Instrument (PDQI) to score generated call summaries and evaluate whether LLMs could effectively summarize a curated set of transfer calls. Methods: STEMI transfer calls for which our institution was the receiving facility were transcribed and summarized by Whisper and ChatGPT (OpenAI), respectively. Each summary was reviewed by 2 of 7 independent physician raters. Summaries were rated using a Likert scale applied to an 8-domain framework adapted from the PDQI. We calculated summary statistics, including means, SDs, and raw and weighted agreement, and produced visual radar plots to demonstrate ratings for each call. We also performed thematic analysis of reviewers' comments. Results: We identified 32 calls, of which 1 (3.1%) was excluded for incompleteness. Raw agreement between raters was 62% (153/248), and the mean of the pairwise weighted κ coefficients was 0.19 (SD 0.30; slight agreement). The mean rating of all summaries across all domains was 4.6 of 5 (SD 0.7). The "useful" (mean 4.8/5, SD 0.5) and "consistent" (mean 4.9/5, SD 0.6) domains were the highest rated, and the "thorough" (mean 4.4/5, SD 1.0) and "hallucination free" (mean 4.4/5, SD 0.9) domains were the lowest rated. The mean score for accuracy was 4.6/5 (SD 0.7). Qualitative analysis found that raters penalized the LLM for inferential hallucinations, although these were often clinically accurate, and discrepancies related to calculation of timing of events. Conclusions: Despite the limitations inherent in a small pilot cohort, this feasibility study suggests that LLMs can generate accurate and pertinent summaries of interhospital transfer calls for patients with STEMI. Interrater agreement was slight, which may suggest inadequate training of raters, unclear definitions, or a limitation of using the PDQI for this task. We identified several important areas for consideration prior to implementation, including thorough assessment of transcription accuracy, prompt engineering to minimize unwanted LLM behavior, and assessment of the impact of incorporating these summaries into clinical care on clinical outcomes.