Disclosing Adverse Events to Patients: International Norms and Trends
Bibliographic record
Abstract
OBJECTIVES: There is a growing expectation in health systems around the world that patients will be fully informed when adverse events occur. However, current disclosure practices often fall short of this expectation. METHODS: We reviewed trends in policy and practice in 5 countries with extensive experience with adverse event disclosure: the United States, the United Kingdom, Canada, New Zealand, and Australia. RESULTS: We identified 5 themes that reflect key challenges to disclosure: (1) the challenge of putting policy into large-scale practice, (2) the conflict between patient safety theory and patient expectations, (3) the conflict between legal privilege for quality improvement and open disclosure, (4) the challenge of aligning open disclosure with liability compensation, and (5) the challenge of measurement related to disclosure. CONCLUSIONS: Potential solutions include health worker education coupled with incentives to embed policy into practice, better communication about approaches beyond the punitive, legislation that allows both disclosure to patients and quality improvement protection for institutions, apology protection for providers, comprehensive disclosure programs that include patient compensation, delinking of patient compensation from regulatory scrutiny of disclosing physicians, legal and contractual requirements for disclosure, and better measurement of its occurrence and quality. A longer-term solution involves educating the public and health care workers about 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 | 0.062 | 0.189 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".