Novel Approach to Patient Safety Education in Nephrology: Integrating the Ottawa Morbidity and Mortality Model with the Health Care Matrix
Bibliographic record
Abstract
Background: The 2016 ACGME National CLER Report highlighted widespread deficiencies in patient safety education across residency and fellowship programs, urging “intensive improvement” through interprofessional, team-based strategies to achieve sustainable change. In response, we implemented a novel educational model in our nephrology fellowship to address these gaps by integrating the Ottawa M&M model with the Health Care Matrix. Methods: Over the past six years, we have conducted structured morbidity and mortality (M&M) conferences using a combined format: patient safety cases selected using Ottawa M&M criteria—including death, disability, harm, near misses, preventable events, and systems issues—are analyzed through the Health Care Matrix, which maps each case onto the six ACGME core competencies (CC): Patient Care (PC), Medical Knowledge (MK), Interpersonal and Communication Skills (ICS), Professionalism (PRO), Systems-Based Practice (SBP), and Practice-Based Learning and Improvement (PBLI). Each conference includes three or more interprofessional teams led by nephrology faculty. Cases are discussed through the lens of core competencies, emphasizing communication breakdowns and system failures. Figure 1 shows the proportion of cases with repeat deficiencies by core competency. Results: Discordance between teams often revealed deficiencies in ICS, PRO, and SBP. These domains were commonly implicated in recurring safety lapses. At each conference’s conclusion, teams reached consensus on targeted, competency-based recommendations to improve safety practices. Conclusion: This integrated educational model fosters a comprehensive, competency-driven understanding of patient safety. Beyond enhancing MK, it highlights the vital roles of PRO, ICS, and SBP in advancing a culture of safety. Our experience suggests that combining the Ottawa M&M framework with the Health Care Matrix offers a reproducible and impactful method for embedding patient safety into nephrology fellowship training.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".