Quality gaps identified through mortality review
Bibliographic record
Abstract
BACKGROUND: Hospital mortality rate is a common measure of healthcare quality. Morbidity and mortality meetings are common but there are few reports of hospital-wide mortality-review processes to provide understanding of quality-of-care problems associated with patient deaths. OBJECTIVE: To describe the implementation and results from an institution-wide mortality-review process. DESIGN: A nurse and a physician independently reviewed every death that occurred at our multisite teaching institution over a 3-month period. Deaths judged by either reviewer to be unanticipated or to have any opportunity for improvement were reviewed by a multidisciplinary committee. We report characteristics of patients with unanticipated death or opportunity for improved care and summarise the opportunities for improved care. RESULTS: Over a 3-month period, we reviewed all 427 deaths in our hospital in detail; 33 deaths (7.7%) were deemed unanticipated and 100 (23.4%) were deemed to be associated with an opportunity for improvement. We identified 97 opportunities to improve care. The most common gap in care was: 'goals of care not discussed or the discussion was inadequate' (n=25 (25.8%)) and 'delay or failure to achieve a timely diagnosis' (n=8 (8.3%)). Patients who had opportunities for improvement had longer length of stay and a lower baseline predicted risk of death in hospital. Nurse and physician reviewers spent approximately 142 h reviewing cases outside of committee meetings. CONCLUSIONS: Our institution-wide mortality review found many quality gaps among decedents, in particular inadequate discussion of goals of care.
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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 | 0.072 | 0.258 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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 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".