The impact of transition strategies from fee-for-service to value-based payment models on quality of care, patient outcomes, and healthcare costs: A systematic analysis
Bibliographic record
Abstract
The shift from fee-for-service (FFS) to value-based payment (VBP) models aims to improve healthcare quality, patient outcomes, and cost efficiency. Despite policy initiatives like the Affordable Care Act, challenges persist in implementation and equity. This systematic review followed PRISMA guidelines, analyzing 85 studies (2016–2023) from PubMed, Scopus, and Cochrane. Inclusion criteria encompassed randomized trials, cohort studies, and direct FFS-VBP comparisons, focusing on quality, outcomes, and costs. Risk of bias was assessed using the Cochrane and Newcastle-Ottawa tools. VBP models demonstrated cost reductions (e.g., 3.7% savings in Medicare’s bundled payments) and improved quality metrics like reduced readmissions. However, success varied by setting; integrated systems (e.g., Kaiser Permanente) outperformed rural hospitals due to infrastructure disparities. Key barriers included data fragmentation, provider burnout (reported by 40%), and difficulties in measuring value. While VBP models show promise, equitable adoption requires tailored strategies: risk-adjusted benchmarks, stakeholder engagement, and robust data systems. Policymakers must balance flexibility and accountability to achieve the Quadruple Aim. Future research should prioritize longitudinal studies and context-specific frameworks.
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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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.017 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".