The role of morbidity and mortality rounds in medical education: a scoping review
Bibliographic record
Abstract
CONTEXT: There is increasing focus on how health care professionals can be trained effectively in quality improvement and patient safety principles. The morbidity and mortality round (MMR) has often been used as a tool with which to examine and teach care quality, yet little is known of its implementation and educational outcomes. OBJECTIVES: The objectives of this scoping review are to examine and summarise the literature on how the MMR is designed and delivered, and to identify how it is evaluated for effectiveness in addressing medical education outcomes. METHODS: A literature search of the PubMed, MEDLINE, PsycInfo and Cochrane Library databases was conducted for articles published from 1980 to 1 June 2016. Publications in English describing the design, implementation and evaluation of MMRs were included. A total of 67 studies were identified, including eight survey-based studies, four literature reviews, one ethnographic study, three opinion papers, two qualitative observation studies and 49 case studies of education programmes with or without formal evaluation. Study outcomes were categorised using Donald Moore's framework for the evaluation of continuing medical education (CME). RESULTS: There is much heterogeneity within the literature regarding the implementation, delivery and goals of the MMR. Common design components included explicit programme goals and objectives, the case selection process, case presentation models and some form of case analysis. Evaluation of CME outcomes for MMR were mainly limited to learner participation, satisfaction and self-assessed changes in knowledge. CONCLUSIONS: The MMR is widely utilised as an educational tool to promote medical education, patient safety and quality improvement. Although evidence to guide the design and implementation of the MMR to achieve measurable CME outcomes remains limited, there are components associated with positive improvements to learning and performance outcomes.
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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 | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".