International strategies, experiences, and payment models to incentivise day surgery
Bibliographic record
Abstract
The importance of day surgery as a less costly alternative compared to conventional inpatient hospital stays is growing internationally. The rate of day surgery activities has increased across Europe. However, this trend has been heterogeneous across countries, and might still be below its potential. Since payment systems affect how providers offer care, they represent a policy instrument to further increase the rate of day surgeries. In this paper, we review international strategies to promote day surgery with a particular focus on payment models for 13 OECD countries (Australia, Austria, Canada, Denmark, England, Estonia, Finland, France, Germany, Netherlands, Norway, Sweden, Switzerland). We conduct a cross-country comparison based on an email survey of health policy experts and a comprehensive literature review of peer-reviewed papers and grey literature. Our research shows that all countries aim to strengthen day surgery activity to increase health system efficiency. Several countries used financial and non-financial policy measures to overcome misaligned incentive structures and promote day surgery activity. Financial incentives for day surgery can serve as a policy instrument to promote change. We recommend embedding these incentives in a comprehensive approach of restructuring health systems. In addition, we encourage countries to monitor and evaluate the effect of changes to payment systems on day surgeries to allow for more informed decision-making.
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 | 0.028 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".