Barriers and facilitators to improving patient safety learning systems: a systematic review of qualitative studies and meta-synthesis
Bibliographic record
Abstract
BACKGROUND: The implementation and continuous improvement of patient safety learning systems (PSLS) is a principal strategy for mitigating preventable harm to patients. Although substantial efforts have sought to improve these systems, there is a need to more comprehensively understand critical success factors. This study aims to summarise the barriers and facilitators perceived by hospital staff and physicians to influence the reporting, analysis, learning and feedback within PSLS in hospitals. METHODS: We performed a systematic review and meta-synthesis by searching MEDLINE (Ovid), EMBASE (Ovid), CINAHL, Scopus and Web of Science. We included English-language manuscripts of qualitative studies evaluating effectiveness of the PSLS and excluded studies evaluating specific individual adverse events, such as systems for tracking only medication side effects, for example. We followed the Joanna Briggs Institute methodology for qualitative systematic reviews. RESULTS: We extracted data from 22 studies, after screening 2475 for inclusion/exclusion criteria. The included studies focused on reporting aspects of the PSLS, however, there were important barriers and facilitators across the analysis, learning and feedback phases. We identified the following barriers for effective use of PSLS: inadequate organisational support with shortage of resources, lack of training, weak safety culture, lack of accountability, defective policies, blame and a punitive environment, complex system, lack of experience and lack of feedback. We identified the following enabling factors: continuous training, a balance between accountability and responsibility, leaders as role models, anonymous reporting, user-friendly systems, well-structured analysis teams, tangible improvement. CONCLUSION: Multiple barriers and facilitators to uptake of PSLS exist. These factors should be considered by decision makers seeking to enhance the impact of PSLS. ETHICS AND DISSEMINATION: No formal ethical approval or consent were required as no primary data were collected.
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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 | 0.116 | 0.233 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".