Medical Error Disclosure in Healthcare – The Scene across Canada
Bibliographic record
Abstract
The quality of healthcare is an emerging concern worldwide. Despite the advancement in the medical field, adverse events resulting from medical errors are relatively common in healthcare systems. Disclosure of an adverse event is an important element in managing the consequences of a medical error. We have previously reviewed and compared various disclosure policies that are in practice in Canada and around the globe to analyze the progress made in this area and suggested a non-punitive, “no-fault” model for reporting medical errors. The purpose of this study was to review and compare the disclosure policies implemented by individual health authorities across the Canadian provinces and territories. We evaluated each policy based on the inclusion of the following key points: Apology, avoidance of blame, avoidance of speculation, immediate disclosure, patient support, provider support, provider training, team-based approach, accessibility, and documentation. The clinical significance of the study was to evaluate various health authorities’ policies of disclosure and report a practice model for medical error disclosure across Canada. The three top parameters found within the disclosure policies include an apology or expression of regret, a team-based approach and documentation of disclosure, all three averaging at 98% respectively across the provinces and territories. The bottom two parameters found within the disclosure policies include provider training and accessibility of disclosure policy through the health authorities’ website, both averaging at 34% respectively. We believe healthcare providers' top priority should be correcting flaws in the medical system and protecting patients' health. Despite the obstacles, physicians should seek to disclose medical errors to patients and their families on both ethical and pragmatic grounds. We believe that the disclosure policies can provide framework and guidelines for appropriate disclosure, which can lead to improved quality care and practices that are more transparent. We suggest that disclosure practice can be improved by creating a uniform policy, centered on honest disclosure and addressing errors in a non-punitive manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".