Payment Systems for Hospital Specialists in 10 High-Income Countries
Bibliographic record
Abstract
Background Payment mechanisms for specialists are important because the inherent financial incentives influence treatment decision of specialists – who control the vast majority of resources used in hospitals. This study aimed at comparing specialist payment systems in ten high-income countries based on a predefined framework in order to systematically describe different payment mechanisms and non-financial benefits. Methods Based on a scoping review, 10 high-income countries (Canada, England, France, Germany, Korea, Luxemburg, Sweden, Switzerland, The Netherlands, USA (Medicare)) with different payment systems (mainly fee-for-service (FFS), mainly salary, and combinations of both) were selected for analysis with a view to including countries with particularly sophisticated systems and/or countries with recent reform initiatives. A survey was designed to collect information from national experts on (1) main national payment mechanisms, (2) contractual relationships between hospitals and specialists, and (3) the different components that make up the total income of specialists. Results In England, Germany, Sweden, and Switzerland, almost all specialists (≥90%) are employed by hospitals and receive a salary, while specialists in the United States, Canada, and Luxembourg are mostly (≥70%) self-employed and paid on the basis of FFS. Payment mechanisms may differ across hospitals (e.g. public vs. private or teaching vs. non-teaching), across specialties (e.g. surgical versus medical specialties), or by setting (inpatient care vs. outpatient care). In Switzerland, the United States and Korea, base salaries are increasingly combined with FFS based bonuses. In the Netherlands, the scope of FFS payments was broadened with the introduction of DRG-based hospital payment. Conclusions Specialist payment systems differ greatly across and usually also within countries. Specialist payment could be optimized in several countries by taking into consideration experiences from other countries. Key messages Specialist payment systems can combine different payment mechanisms in order to balance the intended and unintended incentives of fee-for-service payments or traditional salary systems Differences in payment systems across countries can motivate change by providing examples of alternative options for payment of specialists in hospitals
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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".