Toward Zero Harm: Mackenzie Health’s Journey Toward Becoming a High Reliability Organization and Eliminating Avoidable Harm
Bibliographic record
Abstract
OBJECTIVES: In response to an organizational survey revealing low safety culture scores, we implemented a "zero harm" approach to eliminate preventable harm across a wide variety of clinical areas. We aimed to achieve this objective within 3 years. METHODS: We developed a 5-part strategy for cultural and process redesign that included (1) engaging leadership; (2) developing an organization-specific patient safety framework; (3) monitoring specific quality aims based on high-risk, high-volume, high-cost, and problem-prone areas; (4) standardizing a 3-part review process that includes a root cause analysis for moderate and critical patient safety incidents; and (5) communicating progress to staff in real time via unit-specific electronic dashboards. RESULTS: In less than 1 year, we increased patient safety incident reporting by 37% while simultaneously decreasing falls with injury by 39%, pressure injury rates by 37%, and central line-associated blood stream infections by 34%. We also improved medication reconciliation rate by 3.3% and decreased our irretrievable specimen rate to 0. Finally, we noted increased awareness around patient safety within clinical teams, with open discussions about patient safety becoming a routine part of patient care. CONCLUSIONS: This study describes an initiative that sought to introduce system-wide changes to practice and patient safety culture in a rapid time frame. Results suggest that our 5-step approach to transformation may confer substantial gains in patient safety for peer institutions. Next steps include continuing to expand and monitor quality aims as we progress through our journey to eliminating preventable patient harm in our healthcare system.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".