Long-Term Effect of Fee-For-Service–Based Reimbursement Cuts on Processes and Outcomes of Care for Stroke
Bibliographic record
Abstract
BACKGROUND: As healthcare spending continues to increase, reimbursement cuts have become 1 type of healthcare reform to contain costs. Little is known about the long-term impact of cuts in reimbursement, especially under a global budget cap with fee-for-service (FFS) reimbursement, on processes and outcomes of care. The FFS-based reimbursement cuts have been implemented since July 2002 in Taiwan. We examined the long-term association of FFS-based reimbursement cuts with trends in processes and outcomes of care for stroke. METHODS AND RESULTS: We analyzed all 411,487 patients with stroke admitted to general acute care hospitals in Taiwan during the period 1997 to 2010 through Taiwan's National Health Insurance Research Database. We used a quasi-experimental design with quarterly measures of healthcare utilization and outcomes and used segmented autoregressive integrated moving average models for the analysis. After accounting for secular trends and other confounders, the implementation of the FFS-based reimbursement cuts was associated with trend changes in computed tomography/magnetic resonance imaging scanning (0.31% per quarter; P=0.013), antiplatelet/anticoagulant use (-0.20% per quarter; P<0.001), statin use (0.18% per quarter; P=0.027), physiotherapy/occupational therapy assessment (0.25% per quarter; P<0.001), and 30-day mortality (0.06% per quarter; P<0.001). CONCLUSIONS: There are improvement trends in processes and outcomes of care over time. However, the reimbursement cuts from the FFS-based global budget cap are associated with trend changes in processes and outcomes of care for stroke. The FFS-based reimbursement cuts may have long-term positive and negative associations with stroke care.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".