A comparison between alternative primary care physician payment models: A systematic review and policy analysis
Bibliographic record
Abstract
Objective: Alternative models of primary care physician payment are being considered by policy-makers as a potential way to contain healthcare expenditures. The purpose of this thesis was to synthesize the evidence for alternative primary care physician payment models on quality and economic outcomes worldwide and to make recommendations with respect to payment models that may improve chronic disease management in Canada. Methods: We first conducted a systematic review, searching selected databases from inception to October 2018, for studies that compared primary care physician payment models. There were no restrictions on language, country, or publication date, however studies were restricted to specific study designs (randomized controlled trial, controlled cohort and interrupted time series). A gray literature search was also conducted. The outcomes considered were quality and access to care, patient and physician satisfaction, clinical outcomes, healthcare utilization and costs. Thirteen studies were selected for synthesis, comparing fee-for-service, capitation, incentive payments, and mixed models. We then identified primary care payment methods currently used in Canada through an environmental scan. We applied evidence from the systematic review to evaluate the impact of the three most promising models on quality, utilization, cost, and implementation feasibility, and made a recommendation. Conclusion: Primary care payment models have moved toward incentive payments and mixed models in recent years, and mixed models have promising effects on cost and utilization overall and for managing chronic disease in primary care in Canada. Incentive payments show low sustainability in quality improvements, and a gap in incentivized and non-incentivized aspects of care. Mixed models have been introduced in primary care in Canada. Based on current evidence, the recommended payment model for Canadian primary care physicians that is most likely to optimize chronic disease management is blended capitation. Future studies should focus on long-term quality improvements and improving the quality of non-incentivized activities in incentive models. Further study would help to elucidate the potential benefit of mixed models, in particular their effect on patient-oriented aspects of care: access, continuity, and quality. More studies are needed to understand how blended capitation payment models affect costs and utilization.
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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 | 0.054 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".