Teaming to Revitalize the Morbidity and Mortality Conference
Bibliographic record
Abstract
PROBLEM: Teaming is a conceptual approach to collaboration in dynamic environments. The internal medicine (IM) morbidity and mortality conference (M&M) is an environment where dynamic collaboration is essential to achieve educational and patient safety goals. APPROACH: Teaming principles were applied to revitalize the Mayo Clinic IM Residency M&M. All 104 Mayo Clinic postgraduate year (PGY) 2 residents participated in this curriculum in academic years July 2021 to June 2023. Rooted in project management and leadership principles, teaming fosters adaptation and collaboration, making it well suited for analyzing patient safety events (PSEs). A resident-led, faculty-mentored Quality Improvement and Patient Safety (QIPS) Council implemented a teaming-based M&M redesign in July 2021 that included a case selection tool, case vetting by the QIPS Council, and a structured timeline for M&M that ensured effective engagement with multidisciplinary stakeholders. These interventions culminated in a rebranded M&M in which interprofessional institutional leaders (i.e., special guests) were invited to discuss system-wide issues related to the PSE, creating a forum for discussion and identification of improvement opportunities. OUTCOMES: Evaluations on the M&M curriculum were completed by 74 of 104 PGY-2 residents (71.2%). Results showed significant improvements before versus after M&M in residents' ability to identify PSEs (57 [77%] vs 69 [93.2%], P = .002), confidence in reporting (50 [67.6%] vs 72 [97.2%], P < .001), analyzing PSEs (44 [59.5%] vs 70 [94.6%], P < .001), and belief that M&M would improve future patient care (58 [78.4%] vs 70 [94.6%], P = .004). Sixty-one residents (82.4%) agreed that participating in M&M would change their future practice. NEXT STEPS: Teaming has transformed the IM residency M&M by fostering effective collaboration among a diverse group of residents and institutional leaders. The next step is to apply the teaming framework to other areas of the residency curriculum where dynamic teamwork is needed.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".