Building a Resilient Patient Safety Culture: A Large Healthcare Organization’s Approach to Systematically Reviewing Serious Harm Events
Bibliographic record
Abstract
Across Canada, pressures related to staffing, burnout and funding continue to affect healthcare organizations and systems. These pressures impact the quality of care Canadians receive, most notably access to care. Evidence indicates that patients are more likely to suffer from preventable harm during periods of hospital overcrowding and, indeed, very recent data from the Canadian Institute for Health Information suggest that rates of preventable harm have increased modestly in Canadian hospitals. A key lever that can have a positive impact on patient safety culture and contribute to fewer preventable adverse events at an institutional level is systematic formal case reviews. This article describes a large healthcare organization's approach to systematically reviewing serious harm events. An evaluation of both quantitative and qualitative metrics suggests that Unity Health Toronto's critical incident review process has been effective at building a resilient patient safety culture that stood up to the challenges of the COVID-19 pandemic and continues to have a positive impact on patient safety at Unity Health Toronto.
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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 | 0.315 | 0.339 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| 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".