Payment Reform Pilot In Beijing Hospitals Reduced Expenditures And Out-Of-Pocket Payments Per Admission
Bibliographic record
Abstract
In 2009 China announced plans to reform provider payment methods at public hospitals by moving from fee-for-service (FFS) to prospective and aggregated payment methods that included the use of diagnosis-related groups (DRGs) to control health expenditures. In October 2011 health policy makers selected six Beijing hospitals to pioneer the first DRG payment system in China. We used hospital discharge data from the six pilot hospitals and eight other hospitals, which continued to use FFS and served as controls, from the period 2010-12 to evaluate the pilot's impact on cost containment through a difference-in-differences methods design. Our study found that DRG payment led to reductions of 6.2 percent and 10.5 percent, respectively, in health expenditures and out-of-pocket payments by patients per hospital admission. We did not find evidence of any increase in hospital readmission rates or cost shifting from cases eligible for DRG payment to ineligible cases. However, hospitals continued to use FFS payments for patients who were older and had more complications than other patients, which reduced the effectiveness of payment reform. Continuous evidence-based monitoring and evaluation linked with adequate management systems are necessary to enable China and other low- and middle-income countries to broadly implement DRGs and refine payment systems.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".