Implementation of a structured hospital-wide morbidity and mortality rounds model
Bibliographic record
Abstract
IMPORTANCE: There is a paucity of literature on the quality and effectiveness of institutional morbidity & mortality (M&M) rounds processes. OBJECTIVE: We sought to implement and evaluate the effectiveness of a hospital-wide structured M&M rounds model at improving the quality of M&M rounds across multiple specialties. DESIGN, SETTING, PARTICIPANTS: We conducted a prospective interventional study involving 24 clinical groups (1584 physicians) at a tertiary care teaching hospital from January 2013 to June 2015. INTERVENTION: We implemented the published Ottowa M&M Model (OM3): appropriate case selection, cognitive/system issues analyses, interprofessional participation, dissemination of lessons and effector mechanisms. MAIN OUTCOMES AND MEASURES: We created an OM3 scoring index reflecting these elements to measure the quality of M&M rounds. Secondary outcomes include explicit discussions of cognitive/system issues and resultant action items. RESULTS: OM3 scores for all participating groups improved significantly from a median of 12.0/24 (95% CI 10 to 14) to 20.0/24 (95% CI 18 to 21). An increased frequency of in-rounds discussion around cognitive biases (pre 154/417 (37%), post 256/466 (55%); p<0.05) and system issues (pre 175/417 (42%), post 259/466 (62%); p<0.05) were reported by participants via online surveys postintervention, while in-person surveys throughout the intervention period demonstrated even higher frequencies (cognitive biases 1222/1437 (85%); system issues 1250/1437 (87%)). We found 45 action items resulting directly from M&M rounds postintervention, compared with none preintervention. CONCLUSIONS AND RELEVANCE: Implementation of a structured model enhanced the quality of M&M rounds with demonstrable policy improvements hospital wide. The OM3 can be feasibly implemented at other hospitals to effectively improve quality of M&M rounds across different specialties.
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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 | 0.030 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".