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Record W2588756071 · doi:10.1093/eurpub/ckv172.110

Payment Systems for Hospital Specialists in 10 High-Income Countries

2015· article· en· W2588756071 on OpenAlexaboutno aff
Wilm Quentin, Alexander Geißler, Reinhard Busse

Bibliographic record

VenueEuropean Journal of Public Health · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentBusinessFinance

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0130.018
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.123
GPT teacher head0.296
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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