(AI)lephant in the Room: Utility of Generative AI to Develop Discussion Guides for Cardiac Surgery Morbidity and Mortality Conferences.

OBJECTIVE

Morbidity & Mortality Conferences (M&Ms) are forums to assess adverse outcomes and to analyze their root causes. We sought to utilize a large language model (LLM) to generate discussion materials for cardiac surgery M&Ms, based upon data available in the electronic medical record (EMR).

DESIGN

EMR data, including information from notes, bloodwork, vital sign monitoring, and the medication administration record, were input into a private instance of GPT-5. A prompt was iterated to accomplish three tasks, resulting in the generation of (1) a case outline, (2) a discussion table to identify deviations from evidence-based recommendations, and (3) discussion questions. Readability was assessed by Flesch-Kincaid Grade Level.

SETTING

Our study was conducted within a high-volume cardiothoracic surgery department with residency- and fellowship-level training programs.

PARTICIPANTS

A survey was administered to trainees in cardiothoracic surgery to assess perceptions of LLM-generated (LG) materials.

RESULTS

EMR data was compiled for a total of fourteen cardiac surgery cases. LG and trainee-generated (TG) case outlines did not differ significantly in median word count (453.5 (LG) vs. 487.5 (TG); p = 0.68), though LG outlines had a higher median Flesch-Kincaid Grade Level (13.9 (LG) vs. 11.6 (TG); p = 0.003). 9 trainees completed a two-alternative, forced-choice survey comparing TG and LG outlines. LG outlines were more frequently preferred for presentation of baseline information (59.3% (16/27) (LG) vs. 40.7% (11/27) (TG)), while preferences on the basis of operative and perioperative information were more evenly distributed. LG discussion tables and questions had high perceived clinical applicability.

CONCLUSIONS

An LLM effectively synthesized information from the EMR, in order to outline cases and prompt systems-based discussions about adverse events.