Improving patient safety culture in Saudi Arabia (2012–2015): trending, improvement and benchmarking
Bibliographic record
Abstract
BACKGROUND: Measuring patient safety culture can provide insight into areas for improvement and help monitor changes over time. This study details the findings of a re-assessment of patient safety culture in a multi-site Medical City in Riyadh, Kingdom of Saudi Arabia (KSA). Results were compared to an earlier assessment conducted in 2012 and benchmarked with regional and international studies. Such assessments can provide hospital leadership with insight on how their hospital is performing on patient safety culture composites as a result of quality improvement plans. This paper also explored the association between patient safety culture predictors and patient safety grade, perception of patient safety, frequency of events reported and number of events reported. METHODS: We utilized a customized version of the patient safety culture survey developed by the Agency for Healthcare Research and Quality. The Medical City is a tertiary care teaching facility composed of two sites (total capacity of 904 beds). Data was analyzed using SPSS 24 at a significance level of 0.05. A t-Test was used to compare results from the 2012 survey to that conducted in 2015. Two adopted Generalized Estimating Equations in addition to two linear models were used to assess the association between composites and patient safety culture outcomes. Results were also benchmarked against similar initiatives in Lebanon, Palestine and USA. RESULTS: Areas of strength in 2015 included Teamwork within units, and Organizational Learning-Continuous Improvement; areas requiring improvement included Non-Punitive Response to Error, and Staffing. Comparing results to the 2012 survey revealed improvement on some areas but non-punitive response to error and Staffing remained the lowest scoring composites in 2015. Regression highlighted significant association between managerial support, organizational learning and feedback and improved survey outcomes. Comparison to international benchmarks revealed that the hospital is performing at or better than benchmark on several composites. CONCLUSION: The Medical City has made significant progress on several of the patient safety culture composites despite still having areas requiring additional improvement. Patient safety culture outcomes are evidently linked to better performance on specific composites. While results are comparable with regional and international benchmarks, findings confirm that regular assessment can allow hospitals to better understand and visualize changes in their performance and identify additional areas for improvement.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".