Morbidity and Mortality Conference: Its Purpose Reclaimed and Grounded in Theory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ISSUE: The morbidity and mortality conference (MMC) remains a central activity within the departments of our academic healthcare institutions. It is deeply rooted in the premise that we can learn from our mistakes, thereby improving the care we provide. Recent advances in our understanding of medical error and quality improvement have challenged the value of traditional models of MMC. As a result the purpose of MMC has become clouded and ill-defined: Is it an educational conference that promotes mastery of clinical acumen, or is it a venue to drive quality improvement by addressing systems-based issues in delivering care? Or can it serve both purposes? EVIDENCE: Review of the history of MMC, the literature, and critical application of education theory demonstrates the source of the confusion and the challenges in viewing it through the exclusive lens of either education or quality improvement. Application of experiential learning theory helps resolve this discord showing how the conference facilitates the development of clinical mastery while informing quality improvement programs about important and relevant systems-based issues. IMPLICATION: Building on this, we present a model for MMC involving five essential elements: case-based involving an adverse patient event, anonymity for participants, expert guided critical analysis, reframing understanding of the case presentation and related systems-based factors, and projection to practice change. This model builds on previously described models, is grounded in the literature, and helps clarify its role from both the educational and the quality improvement perspectives.
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.
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 | 0.008 | 0.006 |
| 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 | 0.002 |
| 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 it