The Use of Three Strategies to Improve Quality of Care at a National Level
Bibliographic record
Abstract
BACKGROUND: Improving the quality of care is essential and a priority for patients, surgeons, and healthcare providers. Strategies to improve quality have been proposed at the national level either through accreditation standards or through national payment schemes; however, their effectiveness in improving quality is controversial. QUESTIONS/PURPOSES: The purpose of this review was to address three questions: (1) does pay-for-performance improve the quality of care; (2) do surgical safety checklists improve the quality of surgical care; and (3) do practice guidelines improve the quality of care? These three strategies were chosen because there has been some research assessing their effectiveness in improving quality, and implementation had been attempted on a large scale such as entire countries. METHODS: We performed a literature review from 1950 forward using Medline to identify Level I and II studies. We evaluated the three strategies and their effects on processes and outcomes of care. When possible, we examined strategy implementation, patients, and systems, including provider characteristics, which may affect the relationship between intervention and outcomes with a focus on factors that may have influenced effect size. RESULTS: Pay-for-performance improved the process and to a lesser extent the outcome of care. Surgical checklists reduced morbidity and mortality. Explicit practice guidelines influenced the process and to a lesser extent the outcome of care. Although not definitively showed, clinician involvement during development of intervention and outcomes, with explicit strategies for communication and implementation, appears to increase the likelihood of positive results. CONCLUSION: Although the cost-effectiveness of these three strategies is unknown, quality of care could be enhanced by implementing pay-for-performance, surgical safety checklists, and explicit practice guidelines. However, this review identified that the effectiveness of these strategies is highly context-specific.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".