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International strategies, experiences, and payment models to incentivise day surgery

2023· article· en· W4389856853 on OpenAlexaboutno aff
Anika Kreutzberg, Helene Eckhardt, Ricarda Milstein, Reinhard Busse

Bibliographic record

VenueHealth Policy · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsIncentiveRestructuringPaymentHealth careBusinessHealth policyMedicinePublic economicsFinanceEconomic growthEconomics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.244
GPT teacher head0.512
Teacher spread0.268 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations20
Published2023
Admission routes1
Has abstractyes

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