Quality Care and Patient Safety: A Best Practice Model for Medical Error Disclosure
Bibliographic record
Abstract
Over recent years, adverse events and medical errors have become topics of increased concern in health care. Despite the efforts of healthcare organizations and providers to prevent medical errors and adverse events, medical errors are still inevitable. Disclosure of an adverse event is essential in managing a medical error's consequences. We have previously reviewed disclosure policies at the provincial level and found no uniform approach to disclosure in Canada. Effective communication between healthcare providers, patients, and their families throughout the disclosure process is vital in supporting and fostering the physician-patient relationship. Given the variability of medical error disclosure policies, comparing the disclosure process between different health authorities may allow us to better understand the best practice model given the proper parameters. Disclosure policies can provide a framework and guidelines for appropriate disclosure, leading to more transparent practices. The purpose of this study is to review and compare the disclosure policies implemented by individual health authorities across Canada. We will evaluate each policy based on the inclusion of the following key points: avoidance of blame; support to the staff; an apology or expression of regret; avoidance of speculation; some form of patient support; education/training to healthcare workers; immediate disclosure; team-based approach; accessibility; and documentation. The clinical significance of the study is to find similarities and differences between various health regions' policies of disclosure as well as report the best practice model for medical error disclosure across Canada. We suggest implementing a uniform national policy that addresses errors in a non-punitive manner and respects the patient's right to an honest disclosure. A prime role exists for the accrediting and regulatory authorities to initiate policy changes and appropriate reforms in the area. Not only should disclosing medical errors be a routine part of medical care to enhance quality improvement, but it would also protect patients' health and autonomy.
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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 | 0.095 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.009 | 0.008 |
| 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".