An evidence-based tool (PE for PS) for healthcare managers to assess patient engagement for patient safety in healthcare organizations
Bibliographic record
Abstract
In 1999, the Institute of Medicine had already warned that medical errors caused between 44,000 and 98,000 avoidable deaths per year in the United States. A similar situation was subsequently in 2000, documented in Canadian hospitals. According to a Canadian Patient Safety Institute report (2016), incidents in both acute and home care settings resulted in additional costs of $2.75 billion each year. Research suggests that Patient Engagement (PE) for Patient Safety (PS) can help address this issue. However, the use of PE in various strategies to promote PS has yet to be fully integrated across healthcare systems in OECD countries. The aim of this study was to develop a tool for managers to assess PE strategies implemented at a health system level to enhance PS. Developing the tool involved 3 phases: (1) creating a framework; (2) building a first version of the tool; (3) validating the tool by an expert committee of PS and PE managers. The final tool consists of 81 questions, divided into four sections: (1) describing the healthcare organization (n=14); (2) gathering general information on PE strategies (n=15); (3) assessing different PE strategies for PS (n=49); and (4) describing the respondent’s involvement in PS committees (n=3). The tool is currently being used (by healthcare professionals working in Risk Management (RM) or PS, or, by task groups that include patients) in a research study in Canada and France, to assist healthcare managers in monitoring the evolution of PE for PS at a system level. Experience Framework This article is associated with the Policy & Measurement lens of The Beryl Institute Experience Framework. (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.
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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 | 0.092 | 0.211 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".