Paying hospital specialists: Experiences and lessons from eight high-income countries
Bibliographic record
Abstract
Payment systems for specialists in hospitals can have far reaching consequences for the efficiency and quality of care. This article presents a comparative analysis of payment systems for specialists in hospitals of eight high-income countries (Canada, England, France, Germany, Sweden, Switzerland, the Netherlands, and the USA/Medicare system). A theoretical framework highlighting the incentives of different payment systems is used to identify potentially interesting reform approaches. In five countries,most specialists work as employees - but in Canada, the Netherlands and the USA, a majority of specialists are self-employed. The main findings of our review include: (1) many countries are increasingly shifting towards blended payment systems; (2) bundled payments introduced in the Netherlands and Switzerland as well as systematic bonus schemes for salaried employees (most countries) contribute to broadening the scope of payment; (3) payment adequacy is being improved through regular revisions of fee levels on the basis of more objective data sources (e.g. in the USA) and through individual payment negotiations (e.g. in Sweden and the USA); and (4) specialist payment has so far been adjusted for quality of care only in hospital specific bonus programs. Policy-makers across countries struggle with similar challenges, when aiming to reform payment systems for specialists in hospitals. Examples from our reviewed countries may provide lessons and inspiration for the improvement of payment systems internationally.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".