Patient Centered Care: Medical Error Disclosure Guidelines Across Canada
Bibliographic record
Abstract
The quality of healthcare is an emerging concern worldwide. Despite attempts to minimize adverse events and medical errors, the disclosure of medical errors by health professionals remains a significant challenge. We have previously reported that international policies and the Canadian Provincial College of Physicians and Surgeons both encourage the open disclosure of adverse events and have suggested its integration into a ‘no-fault’ model. Disclosure policies can provide a framework and guidelines for appropriate disclosure, leading to practices that are more transparent. The purpose of this study was to review, evaluate, and compare individual policies across Canadian health regions to provide guidelines for the best possible medical error disclosure policy. We evaluated the policies of each health region using the following five criteria (an apology or expression of regret, support for the patient, avoidance of blame, avoidance of speculation, and support for providers) which are considered critical to designing patient centered guidelines for medical error disclosure. The majority of provincial and territorial health regions (7 out of 11) have implemented disclosure policies that include all of the evaluated criteria. In Eastern Canada, more than 90% of the disclosure policies included an apology, patient support, and avoidance of blame, while more than 80% included avoiding speculation and providing support for providers. Similarly, in Western Canada, more than 80% of policies contained an apology, patient support, and avoidance of speculation, while provider support was found in at least 60% of surveyed policies. In Nunavut and the Northwest Territories, all policies contained an apology, patient support, avoidance of speculation, and provider support. On average, health region disclosure policies included an apology (98%), patient support (98%), avoidance of speculation (95%), provider support (92%), and avoidance of blame (90%). Designing best practice error disclosure policy requires integrating many aspects, including bioethics, physician-patient communication, quality of care, and team-based care delivery. We suggest that disclosure practice in Canada move toward a uniform, patient centered approach that addresses errors non-punitively to encourage medical error disclosure, reduce medical errors, and improve patient safety.
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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 | 0.033 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".