The World Health Organization Performance Assessment Tool for Quality Improvement in Hospitals (PATH): An Analysis of the Pilot Implementation in 37 Hospitals
Bibliographic record
Abstract
OBJECTIVE: To evaluate the pilot implementation of the World Health Organization Performance Assessment Tool for Quality Improvement in hospitals (PATH). DESIGN: Semi-structured interviews with regional/country coordinators and Internet-based survey distributed to hospital coordinators. SETTING: A total of 37 hospitals in six regions/countries (Belgium, Ontario (Canada), Denmark, France, Slovakia, KwaZulu Natal (South Africa)). PARTICIPANTS: Six PATH regional/country coordinators and 37 PATH hospital coordinators. INTERVENTION: Implementation of a hospital performance assessment pilot project. OUTCOME MEASURE: Experience of regional/country coordinators (structured interviews) and experience of hospital coordinators (survey) with the pilot implementation. RESULTS: The main achievement has been the collection and analysis of data on a set of indicators for comprehensive performance assessment in hospitals in regions and countries with different cultures and resource availability. Both regional/country coordinators and hospital coordinators required seed funding and technical support during data collection for implementation. Based on the user evaluation, we identified the following research and development tasks: further standardization and improved validity of indicators, increased use of routine data, more timely feedback with a stronger focus on international benchmarking and further support on interpretation of results. CONCLUSIONS: Key to successful implementation was the embedding of PATH in existing performance measurement initiatives while acknowledging the core objective of the project as a self-improvement tool. The pilot test raised a number of organizational and methodological challenges in the design and implementation of international research on hospital performance assessment. Moreover, the process of evaluating PATH resulted in interesting learning points for other existing and newly emerging quality indicator projects.
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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 | 0.045 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".