Disclosure of Adverse Events in the United States and Canada: An Update, and a Proposed Framework for Improvement
Bibliographic record
Abstract
There is consensus that physicians, health professionals and health care organizations should discuss harm that results from health care delivery (adverse events), including the reasons for harm, with patients and their families. Thought leaders and policy makers in the USA and Canada support this goal. However, there are gaps in both countries between patients and physicians in their attitudes about how errors should be handled, and between disclosure policies and their implementation in practice. This paper reviews the state of disclosure policy and practice in the two countries, and the barriers to full disclosure. Important barriers include fear of consequences, attitudes about disclosure, lack of skill and role models, and lack of peer and institutional support. The paper also describes the problem of the second victim, a corollary of disclosure whereby health care workers are also traumatized by the same events that harm patients. The presence of multiple practical and personal barriers to disclosure suggests the need for a comprehensive solution directed at multiple levels of the health care system, including health departments, institutions, local managers, professional staff, patients and families, and including legal, health system and local institutional support. At the local level, implementation could be based on a translating-evidence-into-practice framework. Applying this framework would involve the formation of teams, training, measurement and identification of local barriers to achieving universal disclosure of adverse events. Significance for public healthIt is inevitable that some patients will be harmed rather than helped by health care. There is consensus that patients and their families must be told about these harmful events. However, there are gaps between patient and physician attitudes about how errors should be handled, and between disclosure policies and their implementation. There are important barriers that impede disclosure, including fear of consequences, attitudes about disclosure, lack of skill, and lack of institutional support. A related problem is that of the second victim, whereby health care workers are traumatized by the same harmful events. This can impair their performance and further compromise safety. The problem is unlikely to be solved by focusing solely on increasing disclosure. A comprehensive solution is needed, directed at multiple levels of the health care system, including health departments, institutions, local managers, professional staff, patients and families, and including legal, health system and local institutional support.
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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 | 0.142 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.025 | 0.027 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".