Factors affecting implementation of accreditation programmes and the impact of the accreditation process on quality improvement in hospitals: a SWOT analysis
Bibliographic record
Abstract
OBJECTIVES: The objectives of this review were to identify factors that influence implementation of hospital accreditation programmes and to assess the impact of the accreditation process on quality improvement in public hospitals. DATA SOURCES: Two electronic databases, Medline (OvidSP) and PubMed, were systematically searched. STUDY SELECTION: "Public hospital", "hospital accreditation", and "quality improvement" were used as the search terms. A total of 348 citations were initially identified. After critical appraisal and study selection, 26 articles were included in the review. DATA EXTRACTION: The data were extracted and analysed using a SWOT (strengths, weaknesses, opportunities, threats) analysis. DATA SYNTHESIS: Increased staff engagement and communication, multidisciplinary team building, positive changes in organisational culture, and enhanced leadership and staff awareness of continuous quality improvement were identified as strengths. Weaknesses included organisational resistance to change, increased staff workload, lack of awareness about continuous quality improvement, insufficient staff training and support for continuous quality improvement, lack of applicable accreditation standards for local use, and lack of performance outcome measures. Opportunities included identification of improvement areas, enhanced patient safety, additional funding, public recognition, and market advantage. Threats included opportunistic behaviours, funding cuts, lack of incentives for participation, and a regulatory approach to mandatory participation. CONCLUSIONS: By relating the findings to the operational issues of accreditation, this review discussed the implications for successful implementation and how accreditation may drive quality improvement. These findings have implications for various stakeholders (government, the public, patients and health care providers), when it comes to embarking on accreditation exercises.
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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 | 0.095 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.021 | 0.034 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".