Quality Care and Patient Safety: A Best Practice Model for Medical Error Disclosure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it