22 Leadership in action: an organization’s journey to elevating patient safety through safety rounds
Bibliographic record
Abstract
Background Patient safety rounds are a critical tool used by healthcare organizations to promote a culture of safety by providing an opportunity for staff to engage with organizational leaders on patient safety-related issues. At William Osler Health System (Osler), Executive Patient Safety Rounds (EPSRs) evolved to adopt a structured, streamlined and automated approach through integration with digital iHuddle Boards (figure 1) and team huddles for proactive identification and resolution of safety concerns. Objectives This abstract highlights Osler’s journey in establishing EPSRs, their integration with existing safety processes and iHuddle Boards while assessing their impact on patient safety culture, as well as the importance of leadership commitment and process integration. Methods Osler initiated EPSRs after the need was highlighted through results of Accreditation Canada’s Patient Safety Culture Survey by securing strong executive leadership engagement. The process expanded to include senior leaders and directors conducting weekly rounds during the unit huddle time. Improvement opportunities are prioritized using a Risk Assessment Matrix (figure 2) and classified based upon impact and frequency of occurrence complemented by a Completion Threshold (figure 3) for timelines and added to an Improvement Priority Matrix (figure 4). An EPSR dashboard enhanced transparency, allowing progress monitoring and reporting up to the Executive Team and Board. Integration with iHuddle board empowered frontline staff to monitor progress thereby closing the feedback loop (figure 5). Results Adoption of an integrated approach yielded a remarkable 15% improvement in overall patient safety culture at Osler by proactively identifying and addressing potential risks and opportunities for improvement. Conclusions EPSRs prove instrumental in enhancing patient safety and instilling a safety culture. However, their success depends on leadership engagement, action plan execution, process integration and continual communication with frontline staff to sustain their impact. Osler’s journey exemplifies proactive leadership and serves as a model for other healthcare organizations seeking to elevate their patient safety standards.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".