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Record W4399479314 · doi:10.54941/ahfe1004840

Patient Centered Care: Medical Error Disclosure Guidelines Across Canada

2024· article· en· W4399479314 on OpenAlexaboutno aff
Jay Kalra, Zoher Rafid-Hamed, Bryan Johnston, Patrick Seitzinger

Bibliographic record

VenueAHFE international · 2024
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsComputer sciencePatient careMedicineNursing

Abstract

fetched live from OpenAlex

The quality of healthcare is an emerging concern worldwide. Despite attempts to minimize adverse events and medical errors, the disclosure of medical errors by health professionals remains a significant challenge. We have previously reported that international policies and the Canadian Provincial College of Physicians and Surgeons both encourage the open disclosure of adverse events and have suggested its integration into a ‘no-fault’ model. Disclosure policies can provide a framework and guidelines for appropriate disclosure, leading to practices that are more transparent. The purpose of this study was to review, evaluate, and compare individual policies across Canadian health regions to provide guidelines for the best possible medical error disclosure policy. We evaluated the policies of each health region using the following five criteria (an apology or expression of regret, support for the patient, avoidance of blame, avoidance of speculation, and support for providers) which are considered critical to designing patient centered guidelines for medical error disclosure. The majority of provincial and territorial health regions (7 out of 11) have implemented disclosure policies that include all of the evaluated criteria. In Eastern Canada, more than 90% of the disclosure policies included an apology, patient support, and avoidance of blame, while more than 80% included avoiding speculation and providing support for providers. Similarly, in Western Canada, more than 80% of policies contained an apology, patient support, and avoidance of speculation, while provider support was found in at least 60% of surveyed policies. In Nunavut and the Northwest Territories, all policies contained an apology, patient support, avoidance of speculation, and provider support. On average, health region disclosure policies included an apology (98%), patient support (98%), avoidance of speculation (95%), provider support (92%), and avoidance of blame (90%). Designing best practice error disclosure policy requires integrating many aspects, including bioethics, physician-patient communication, quality of care, and team-based care delivery. We suggest that disclosure practice in Canada move toward a uniform, patient centered approach that addresses errors non-punitively to encourage medical error disclosure, reduce medical errors, and improve patient safety.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.000

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.

Opus teacher head0.086
GPT teacher head0.508
Teacher spread0.422 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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