Review of the utility of routine mortality reviews among deaths on General Internal Medicine wards in a Canadian tertiary care hospital
Bibliographic record
Abstract
BACKGROUND: Hospital morbidity and mortality reviews are common quality assurance activities, intended to uncover latent or unrecognised systemic issues that contribute to preventable adverse events and patient harm. Mortality reviews may be routinely mandated by hospital policy or for accreditation purposes. However, patients under the care of certain specialties, such as general internal medicine (GIM), are affected by a substantial burden of chronic disease, advanced age, frailty or limited life expectancy. Many of their deaths could be viewed as reasonably foreseeable, and unrelated to poor-quality care. METHODS: We sought to determine how frequently postmortem chart reviews for hospitalised GIM patients at our tertiary care centre in Canada would uncover patient safety or quality of care issues that directly led to these patients' deaths. We reviewed the charts of all patients who died while admitted to the GIM admitting service over a 12-month time period between 1 July 2020 and 30 June 2021. RESULTS: We found that in only 2% of cases was a clinical adverse event detected that directly contributed to a poor or unexpected outcome for the patient, and of those cases, more than half were related to unfortunate nosocomial transmission of COVID-19 infection. CONCLUSION: Due to an overall low yield, we discourage routine mortality chart reviews for general medical patients, and instead suggest that organisations focus on strategies to recognise and capture safety incidents that may not necessarily result in death.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".