Web‐based comparative study of some national experiences of health care quality management
Bibliographic record
Abstract
Since the publication of study results on adverse events to health care in OECD countries, the importance of the national quality improvement strategies has been recognised. To examine how these strategies have been shaped in different jurisdictions, we carried out this study. We conducted a web-based comparative study of international practices. We first defined seven key health care and services quality management functions. We then drew on the experience of authors to make a reasoned selection of 13 countries or states across the world. We determined the distance that separates each of these functions from a country's Ministry of Health (MoH); and examined whether these functions are concentrated in a single organisation or dispersed across several organisations. Afterwards, we correlated our results with the quality level of these countries based on the OECD's health care indicators. Overall, Netherlands, Québec (Canada), Korea, Germany, England (UK), and the United States had at least 50% of their quality management functions controlled by self-regulated organisations. The Market Concentration Index ranged from 937 for the United States to 6800 for Russia. Graphical representation has shown us two health system models. Our results also clearly showed that countries had a better quality of care most often when they belong to model 1 of our taxonomy. These findings will help countries design and implement large-scale health care and services quality strategies for better and safer health care and services.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".