Reporting and responding to patient safety incidents based on data from hospitals’ reporting systems: A systematic review
Bibliographic record
Abstract
Objective: This review summarizes and synthesizes the evidence on follow-up activities regarding patient safety incidents reported in hospitals.Methods: Peer-reviewed papers were retrieved with electronic searches from CINAHL, Web of Science, PubMed and Scopus databases and with manual searches in most relevant journals and in the reference lists of included studies, limiting searches to papers published in English between 2014 and 2018. A systematic review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. Two authors extracted the data following a predefined extraction form.Results: All together 16 studies were selected for analysis. All studies described incidents and gave insight into problems, risks and unsafe situations which were responded to with recommended improvements. Recommended improvements in response to incidents involved guidelines, staff training, technical improvements and general safety improvements. Only five studies reported feedback and knowledge dissemination activities, referring to meetings, written support and visual support.Conclusions: Limited research has described the systematic use of report outcomes for knowledge application in organizations. However, the development of patient safety requires that reported incidents are responded to by knowledge application within feedback and knowledge dissemination activities. Therefore, healthcare professionals need to have sufficient competences in patient safety, and more research is needed on the content and effectiveness of the responding activities.
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How this classification was reachedexpand
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 | 0.016 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".