Patients' and Physicians' Attitudes Regarding the Disclosure of Medical Errors
Bibliographic record
Abstract
CONTEXT: Despite the best efforts of health care practitioners, medical errors are inevitable. Disclosure of errors to patients is desired by patients and recommended by ethicists and professional organizations, but little is known about how patients and physicians think medical errors should be discussed. OBJECTIVE: To determine patients' and physicians' attitudes about error disclosure. DESIGN, SETTING, AND PARTICIPANTS: Thirteen focus groups were organized, including 6 groups of adult patients, 4 groups of academic and community physicians, and 3 groups of both physicians and patients. A total of 52 patients and 46 physicians participated. MAIN OUTCOME MEASURES: Qualitative analysis of focus group transcripts to determine the attitudes of patients and physicians about medical error disclosure; whether physicians disclose the information patients desire; and patients' and physicians' emotional needs when an error occurs and whether these needs are met. RESULTS: Both patients and physicians had unmet needs following errors. Patients wanted disclosure of all harmful errors and sought information about what happened, why the error happened, how the error's consequences will be mitigated, and how recurrences will be prevented. Physicians agreed that harmful errors should be disclosed but "choose their words carefully" when telling patients about errors. Although physicians disclosed the adverse event, they often avoided stating that an error occurred, why the error happened, or how recurrences would be prevented. Patients also desired emotional support from physicians following errors, including an apology. However, physicians worried that an apology might create legal liability. Physicians were also upset when errors happen but were unsure where to seek emotional support. CONCLUSIONS: Physicians may not be providing the information or emotional support that patients seek following harmful medical errors. Physicians should strive to meet patients' desires for an apology and for information on the nature, cause, and prevention of errors. Institutions should also address the emotional needs of practitioners who are involved in medical errors.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".