Medical Error Disclosure: A Quality Perspective and Ethical Dilemma in Healthcare Delivery
Bibliographic record
Abstract
Medical errors are a significant public health concern that affects patient care and safety. Highlighted as a substantial problem in the 1999 Institute of Medicine report, medical errors have become the third leading cause of death in the United States of America. Failure to inform the patient of adverse events caused by a medical error compromises patient autonomy. Disclosure of adverse events to patients and families is critical in managing the consequences of a medical error and essential for maintaining patient trust. When errors occur, healthcare practitioners are faced with the ethical and moral dilemmas of if and to whom to disclose the error. Healthcare providers face these disclosure dilemmas across all disciplines, locations, and generations and have far-reaching implications on healthcare quality and the progress of medicine. We have previously reported the Canadian provincial initiatives encouraging open disclosure of adverse events and have suggested its integration into a 'no-fault' model. Though similar in content, the Canadian provincial initiatives remain isolated because of their non-mandatory nature and absence of federal or provincial laws on disclosure. The purpose of this study was to review and compare the disclosure policies implemented by individual health care regions/authorities in various parts of Canada to identify quality issues related to medical error disclosure based on several ethical and professional principles. The complexities of medical error disclosure to patients present ideal opportunities for medical educators to probe how learners balance the moral complexities involved in error disclosure. Effective communication between health care providers, patients, and their families throughout the disclosure process is integral in sustaining and developing the physician-patient relationship. We believe that the disclosure policies can provide a framework and guidelines for appropriate disclosure, leading to more transparent practices. We suggest that disclosure practice can be improved by creating a uniform policy centered on addressing errors in a non-punitive manner and respecting the patient's right to an honest disclosure and be implemented as part of the standard of care.
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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 | 0.072 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.020 | 0.080 |
| Scholarly communication | 0.029 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.001 | 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".