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TRUVACE RECORD VERSION
record: TRV-2026-1666
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-17T06:54:18.842352Z
status: published
lens: g_space
sector: health
headline: (AI)lephant in the Room: Utility of Generative AI to Develop Discussion Guides for Cardiac Surgery Morbidity and Mortality Conferences
dek: 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 tas…
gain_title: A private GPT-5 instance produced case outlines, deviation tables and discussion questions for cardiac surgery M&Ms from EMR data, matching trainee outline length and earning high perceived clinical applicability.
problem_title: (none)
trace_subject: (none)
gain_reading: A private GPT-5 instance produced case outlines, deviation tables and discussion questions for cardiac surgery M&Ms from EMR data, matching trainee outline length and earning high perceived clinical applicability.
gain_evidence: LG and trainee-generated (TG) case outlines did not differ significantly in median word count | LG discussion tables and questions had high perceived clinical applicability.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers in a high-volume cardiothoracic surgery department fed electronic medical record data from fourteen cardiac surgery cases into a private instance of GPT-5. With an iterated prompt, the model produced a case outline, a table flagging deviations from evidence-based recommendations, and discussion questions for morbidity and mortality conferences.

The work suggests language models could reduce the preparation burden of M&M conferences while keeping the focus on systems-based discussion of adverse events. The evidence is small in scale, though, and rests on a handful of trainee preferences rather than on any measured change in patient outcomes or conference quality.
limitation: The evaluation covered only fourteen cases and a survey of nine trainees at a single department, and preferences for operative and perioperative content were evenly split, so the findings are preliminary.
tag: Evidence-backed gain
key_points: Inputs included notes, bloodwork, vital sign monitoring and the medication administration record from fourteen cardiac surgery cases. | Median outline length was similar: 453.5 words for LLM versus 487.5 for trainees (p = 0.68). | LLM outlines had a higher Flesch-Kincaid Grade Level, 13.9 versus 11.6 (p = 0.003). | Nine trainees completed a forced-choice survey; baseline information in LLM outlines was preferred 16 of 27 times.
rundown: The study tasked the model with three outputs: a case outline, a discussion table to identify deviations from evidence-based recommendations, and discussion questions. The setting was a department with both residency and fellowship training programs.

Readability was assessed with the Flesch-Kincaid Grade Level, and the LLM-generated outlines scored significantly higher, meaning they were written at a more difficult reading level than trainee-generated outlines.

In the two-alternative forced-choice survey, trainees favored the LLM outline for baseline information at 59.3 percent versus 40.7 percent, while preferences for operative and perioperative information were more evenly split. The authors concluded the model effectively synthesized EMR information to outline cases.
sources:
- peer_reviewed | Journal of Surgical Education | https://doi.org/10.1016/j.jsurg.2026.104185 | 2026-10-13
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