Implementation of standardized patient safety and quality improvement rounds in a tertiary care paediatric centre
Bibliographic record
Abstract
Introduction: Patient safety is increasingly becoming a major priority for healthcare institutions, and various models of these rounds exist. The Ottawa M&M Model (OM3) is a structured and standardized approach to patient safety and quality improvement rounds that has been implemented at other institutions across Canada with good success. Methods: This quality improvement project invited divisions within the Children's Hospital in London, Ontario to participate in the implementation of the OM3 for M&M rounds. The project aligned with the Institute for Health Improvement's Model for Improvement. Baseline needs assessments and facilitator training were performed. The main outcome was implementation of a version of the OM3 for PSQI rounds, action items generated from these rounds, and systemic hospital changes. Results: The baseline needs assessment showed that 18 (66.7%) respondents were members of divisions with pre-existing M&M rounds. Most found their rounds at least valuable, but only two-thirds found that action items were generated following each meeting. After implementation of the OM3, 58.3% (21 of 36) of action items submitted to the CH-QCC were partially or completed actioned by the end of 2022. A post-implementation survey showed that of the 11 division representatives who responded, 7 (64%) were still participating in PSQI rounds and using the new OM3 format, which they agreed was more organized. Conclusions: We were able to successfully implement a new standardized approach to Patient Safety and Quality Improvement Rounds that has led to systemic changes within the paediatrics hospital and continues to be used today.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".