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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".