A Decade of Excellence: Accreditation’s Long-term Impact on Quality, Safety, and Performance at King Saud University Medical City
Bibliographic record
Abstract
Abstract Background: The Kingdom of Saudi Arabia has undergone a healthcare system transformation to improve healthcare delivery and central to this is accreditation of hospitals. This article assesses the long-term effects of national and international accreditations through measuring staff perception after 10 years of participation in multiple accreditation surveys. Methods: This mixed-methods study was conducted at the King Saud University Medical City. The tool was adapted from previous studies. Respondents were asked to evaluate their involvement in accreditation and readiness for another survey. A qualitative interview tool was also used to elicit input from key stakeholders, senior leaders, and managers. Results: Six hundred and thirty respondents participated, reporting the perception of their own performance, their role in the accreditation process, the hospitals’ overall performance, and the impact of accreditation on quality and safety. Analysis of variance showed a significantly increasing mean score with increasing involvement of respondents in accreditation with the highest scores for the first accreditation survey. Regression showed increases in selected outcomes with increasing subscale scores for patient satisfaction, management, and leadership. Accreditation supported improved and sustained quality of care despite differences in implementing both international and national accreditation standards. Conclusion: The long-term assessment of accreditation revealed that staff perception about performance was highest during the first cycle and consistently decreased with consequent surveys. The slight decrease in scale scores reveals that the benefits of accreditation gradually decrease over time but mostly retain a positive impact.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".