The end of an era? Activity-based funding based on diagnosis-related groups: A review of payment reforms in the inpatient sector in 10 high-income countries
Bibliographic record
Abstract
CONTEXT: Across the member countries of the Organisation for Economic Co-Operation and Development, policy makers are searching for new ways to pay hospitals for inpatient care to move from volume to value. This paper offers an overview of the latest reforms and their evidence to date. METHODS: We reviewed reforms to DRG payment systems in 10 high-income countries: Australia, Austria, Canada (Ontario), Denmark, France, Germany, Norway, Poland, the United Kingdom (England), and the United States. FINDINGS: We identified four reform trends among the observed countries, them being (1) reductions in the overall share of inpatient payments based on DRGs, (2) add-on payments for rural hospitals or their exclusion from the DRG system, (3) episode-based payments, which use one joint price to pay providers for all services delivered along a patient pathway, and (4) financial incentives to shift the delivery of care to less costly settings. Some countries have combined some or all of these measures with financial adjustments for quality of care. These reforms demonstrate a shift away from activity and efficiency towards a diversified set of targets, and mirror efforts to slow the rise in health expenditures while improving quality of care. Where evaluations are available, the evidence indicates mixed success in improving quality of care and reducing costs and expenditures.
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 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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".