How hospital autonomy affects provider payment reform effectiveness
Bibliographic record
Abstract
BACKGROUND: Provider payment reforms (PPRs) have demonstrated mixed results for improving health system efficiency. Since PPRs require health care organisations to interpret and implement policies, the organizational characteristics of hospitals may affect the effectiveness of PPRs. Hospitals with more autonomy have the flexibility to respond to PPRs more efficiently, but they may not if the autonomy previously facilitated behaviours that counter the PPR's objective. This study examines whether hospitals with higher autonomy responds to PPRs more effectively. METHODS: We used data from a matched-pair, cluster randomized controlled PPR intervention in a resource-limited Chinese province between 2014 and 2018. The intervention reformed the reimbursement method from the publicly administered New Cooperative Medical Scheme (NCMS) from fee-for-service to global budget. We interacted measures of hospital autonomy over surplus, hiring, and procurement (drugs, consumables, equipment, and overall index) with the difference-in-difference estimator to examine how autonomy moderated the intervention's effect. RESULTS: Autonomy over surplus (p < 0.01) and procurement of equipment (p < 0.01) were associated with relatively faster NCMS expenditure growth, demonstrating worse PPR response. They were also associated with higher expenditure shifting to out-of-pocket expenditures (p > 0.05). Post hoc analysis suggests that hospitals with surplus autonomy had higher OOP per admission (p < 0.01), suggesting profiteering tendencies. Other dimensions of autonomy demonstrated imprecise association. DISCUSSION: Hospitals with more autonomy may not necessarily respond more effectively to PPRs that incentivise efficiency when they had previously been encouraged to maximise profit. Policymakers should assess the extent of perverse incentives before granting autonomy and adjust the incentives accordingly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".