S2989 Transforming the Gastroenterology Morbidity and Mortality Conference: A Multicenter, Fellow-Led Approach to Systems Thinking, Equity, and Safety Culture
Bibliographic record
Abstract
Introduction: Modern patient safety (PS) culture emphasizes shared accountability and constructive assessment of adverse outcomes to improve care. Morbidity and Mortality (M&M) reviews are a major process to support this, but limited data exists on implementation in gastroenterology (GI). This gap highlights the need for resources to train gastroenterologists to recognize and address key PS drivers. Tools like the SBAR and Ottawa M&M Model (OM3) can be adopted to guide communication and productive error analysis to advance this aim. Methods: A structured, trainee-led M&M conference was implemented at 2 GI training programs: Institution 1 (I1) in 9/2021 and Institution 2 (I2) in 10/2024, with data collected through 5/2025. I1 captured complete case data through 6/2023 before transitioning to a maintenance phase focused on scale-up at I2. Pre-intervention M&Ms at both sites were unstructured and limited to procedural complications. Post-intervention, trainees met with QI-proficient mentors prior to presenting cases using standardized SBAR- and OM3-based models. Each case included an apparent cause analysis addressing systems factors, cognitive bias (CB), and social determinants of health (SDOH). Action items, division feedback, and outcomes were tracked. ACGME fellow surveys were reviewed for trends in PS culture. Results: Detailed review of 21 cases (I1: 12; I2: 9) confirmed 100% adherence to new format. I1 completed 16 additional cases after 6/2023 excluded from analysis. Both sites identified CB in ≥89% of cases and addressed all CB categories. SDOH were discussed in ≥83% of cases. All cases identified change opportunities and proposed interventions, with an implementation rate up to 48%. Post-intervention, fellows at both programs reported an absolute increase in PS event investigation participation, strengthened perceptions of personal responsibility for PS, and universal knowledge of how to report PS events by 2025. Conclusion: This multicenter implementation of a structured, trainee-led GI M&M conference shifted focus from procedural and individual errors to systems-based learning by addressing cognitive reasoning pitfalls, SDOH, and healthcare delivery flaws. The process was scalable and reproducible across 2 academic centers, led to measurable interventions to improve care quality and safety, and enhanced fellow engagement in safety culture. A structured M&M framework should be broadly implemented to promote patient safety and accountability in GI training programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.053 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".