Patient Safety Learning Systems: A Systematic Review and Qualitative Synthesis.
Bibliographic record
Abstract
BACKGROUND: A patient safety learning system (sometimes called a critical incident reporting system) refers to structured reporting, collation, and analysis of critical incidents. To inform a provincial working group's recommendations for an Ontario Patient Safety Event Learning System, a systematic review was undertaken to determine design features that would optimize its adoption into the health care system and would inform implementation strategies. METHODS: The objective of this review was to address two research questions: (a) what are the barriers to and facilitators of successful adoption of a patient safety learning system reported by health professionals and (b) what design components maximize successful adoption and implementation? To answer the first question, we used a published systematic review. To answer the second question, we used scoping study methodology. RESULTS: Common barriers reported in the literature by health care professionals included fear of blame, legal penalties, the perception that incident reporting does not improve patient safety, lack of organizational support, inadequate feedback, lack of knowledge about incident reporting systems, and lack of understanding about what constitutes an error. Common facilitators included a non-accusatory environment, the perception that incident reporting improves safety, clarification of the route of reporting and of how the system uses reports, enhanced feedback, role models (such as managers) using and promoting reporting, legislated protection of those who report, ability to report anonymously, education and training opportunities, and clear guidelines on what to report. Components of a patient safety learning system that increased successful adoption and implementation were emphasis on a blame-free culture that encourages reporting and learning, clear guidelines on how and what to report, making sure the system is user-friendly, organizational development support for data analysis to generate meaningful learning outcomes, and multiple mechanisms to provide feedback through routes to reporters and the wider community (local meetings, email alerts, bulletins, paper contributions, etc.). CONCLUSIONS: The design of a patient safety learning system can be optimized by an awareness of the barriers to and facilitators of successful adoption and implementation identified by health care professionals. Evaluation of the effectiveness of a patient safety learning system is needed to refine its design.
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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 | 0.052 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".