Adapting evidence‐based clinical practice guidelines at university teaching hospitals: A model for the Eastern Mediterranean Region
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Clinical practice guidelines (CPGs) are significant tools for evidence-based health care quality improvement. The CPG program at King Saud University was launched as a quality improvement program to fulfil the international accreditation standards. This program was a collaboration between the Research Chair for Evidence-Based Healthcare and Knowledge Translation and the Quality Management Department. This study aims to develop a fast-track method for adaptation of evidence-based CPGs and describe results of the program. METHODS: Twenty-two clinical departments participated in the program. Following a CPGs awareness week directed to all health care professionals (HCPs), 22 teams were trained to set priorities, search, screen, assess, select, and customize the best available CPGs. The teams were technically supported by the program's CPG advisors. To address the local health care context, a modified version of the ADAPTE was used where recommendations were either accepted or rejected but not changed. A strict peer-review process for clinical content and methodology was employed. RESULTS: In addition to raising awareness and building capacity, 35 CPGs were approved for implementation by March 2018. These CPGs were integrated with other existing projects such as accreditation, electronic medical records, performance management, and training and education. Preliminary implementation audits suggest a positive impact on patient outcomes. Leadership commitment was a strength, but the high turnover of the team members required frequent and extensive training for HCPs. CONCLUSION: This model for CPG adaptation represents a quick, practical, economical method with a sense of ownership by staff. Using this modified version can be replicated in other countries to assess its validity.
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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 | 0.038 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".