Does Performance-Based Remuneration for Individual Health Care Practitioners Affect Patient Care?
Bibliographic record
Abstract
BACKGROUND: Pay-for-performance (P4P) is increasingly touted as a means to improve health care quality. PURPOSE: To evaluate the effect of P4P remuneration targeting individual health care providers. DATA SOURCES: MEDLINE, EMBASE, Cochrane Library, OpenSIGLE, Canadian Evaluation Society Unpublished Literature Bank, New York Academy of Medicine Library Grey Literature Collection, and reference lists were searched up until June 2012. STUDY SELECTION: Two reviewers independently identified original research papers (randomized, controlled trials; interrupted time series; uncontrolled and controlled before-after studies; and cohort comparisons). DATA EXTRACTION: Two reviewers independently extracted the data. DATA SYNTHESIS: The literature search identified 4 randomized, controlled trials; 5 interrupted time series; 3 controlled before-after studies; 1 nonrandomized, controlled study; 15 uncontrolled before-after studies; and 2 uncontrolled cohort studies. The variation in study quality, target conditions, and reported outcomes precluded meta-analysis. Uncontrolled studies (15 before-after studies, 2 cohort comparisons) suggested that P4P improves quality of care, but higher-quality studies with contemporaneous controls failed to confirm these findings. Two of the 4 randomized trials were negative, and the 2 statistically significant trials reported small incremental improvements in vaccination rates over usual care (absolute differences, 8.4 and 7.8 percentage points). Of the 5 interrupted time series, 2 did not detect any improvements in processes of care or clinical outcomes after P4P implementation, 1 reported initial statistically significant improvements in guideline adherence that dissipated over time, and 2 reported statistically significant improvements in blood pressure control in patients with diabetes balanced against statistically significant declines in hemoglobin A1c control. LIMITATION: Few methodologically robust studies compare P4P with other payment models for individual practitioners; most are small observational studies of variable quality. CONCLUSION: The effect of P4P targeting individual practitioners on quality of care and outcomes remains largely uncertain. Implementation of P4P models should be accompanied by robust evaluation plans. PRIMARY FUNDING SOURCE: None.
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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 | 0.048 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".