IMPROVING PATIENT SAFETY EVENT REPORTING AND LEARNING IN ORTHOPAEDIC SURGERY: A QUALITY IMPROVEMENT (QI) INITIATIVE IN POSTGRADUATE EDUCATION
Bibliographic record
Abstract
Reflecting on patient safety events is a crucial aspect of clinical care and an educational opportunity for surgical trainees. Diligently documenting adverse events (AE) and discussing key learning points encourages constructive reflection to prevent event recurrence. This QI initiative aims to implement a multimodal program to increase participation in and standardize AE reporting and engage trainees in safety-related learning. After 18 months of piloting this initiative, we evaluated its effectiveness in optimizing our M&M Rounds and improving case tracking. Our QI initiative involved using the Ottawa M&M Model (OM3)1 to guide M&M Rounds, creating a centralized AE tracking tool, and launching new initiatives to remind trainees to document AEs. In March 2022, we transitioned to the OM3 Model1 for holding M&M Rounds and created a GoogleDoc spreadsheet to track AE cases. We designed a PowerPoint template based on the OM3 framework for case presentations at Rounds. After Rounds, attendees completed an evaluation survey with questions formatted according to the OM3 score card.2 Each question is scored out of three points [total: 24]. We calculated the average quarterly OM3 scores and individual question scores from Quarter Three of 2021 to Quarter Three of 2023. In October 2023, we implemented initiatives to improve the consistency of AE tracking. We designed newsletters to share project progress. Monthly editions were distributed to faculty and trainee site leads and provided updates on each site's performance and recently tracked AEs. Quarterly editions were distributed to staff, residents, and fellows and highlighted key AEs and their learning points along with a detailed guide for organizing M&M Rounds. Reminder posters with a QR code linking to the AE GoogleDoc were placed in orthopaedics on-call and/or hand-over rooms. Also, we emailed reminders for completing the post-rounds surveys. There was a 3.11-point increase in OM3 scores after transitioning to the new format [14.59 pre-transition versus 17.70 post-transition]. Individual scores improved for six questions [activity frequency, method of discussions, case finding, case selection, case analysis, impact, and outcomes]. There was a significant 1.05-point improvement in the method of discussions [1.50 vs. 2.55; p Transitioning to OM3 significantly improved the structure of our M&M Rounds, case selection, and learning outcomes. Our initiatives that focused on elevating awareness for patient safety and spotlighting lessons learned increased the number of AEs reported. Future studies should examine strategies to improve (MDT) attendance at M&M Rounds.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".