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Record W7101430374 · doi:10.1093/eurpub/ckaf161.736

4.Y.1. PechaKucha: Value based payments: do they deliver on their promises? Recent experiences in Europe and Canada

2025· article· en· W7101430374 on OpenAlexaboutno aff

Bibliographic record

VenueEuropean Journal of Public Health · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsEquity (law)PaymentUnintended consequencesHealth careQuality (philosophy)ProductivityValue (mathematics)Financial risk

Abstract

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Abstract Background Healthcare spending in Europe has steadily increased, often without corresponding gains in value. In response to financial pressures, countries have sought to improve value either by reducing unnecessary costs or by increasing productivity or quality of care. One common strategy for this objective has been to replace input-based payments with “value-based” payments (VBP). Definitions of VBP vary, but VBP models typically bundle payments across episodes of care, chronic conditions, or patient populations across several providers. VBPs intend to shift financial risk from payers to providers, holding the latter accountable for the cost and quality of care delivered. They are designed to foster coordination among providers, and adopt a broader view of value, including care integration, cost reduction, and site optimization, and consider social risk factors to promote equity. Their strength lies in moving away from micromanagement, enabling providers across settings to collaborate flexibly. However, VBPs are not new, and evidence shows modest or no improvements in efficiency and quality, with varying impacts on healthcare spending, outcomes, and patient experience. Difficulties in adequately adjusting payments for patient and provider characteristics can result in risk selection and ‘gaming’ practices, undermining equity and quality. As a result, countries have increasingly refined VBP models to better manage risk and incentives, improve care coordination and increasingly involve patients in assessing quality of care, for example, by incorporating patient-reported outcome and experience measures (PROMs and PREMs). Objectives This workshop will present recent experiences with VBP models in Europe, highlighting their intended and unintended consequences. This discussion aims to inspire and inform other countries looking to design or (re)shape their own payment mechanisms. Format This will be an interactive session and engage the audience. 1. The first presentation will define and characterize VBP, highlighting examples across six countries. Three case studies will showcase how different VBP models tackle previous limitations: 2. Article 51 promotes innovations in the French health system. Three pilots will be presented, including “coordinated care pathways” and capitation, that bundle payments for primary care across providers to integrate care; and pay for performance add-ons to promote quality of care. 3. The German “Quality contracts” aim to foster provider competition based on quality, and account and pay hospitals for quality of care, inter alia, with PROMs as indicators. Yet, it does not always reduce costs. 4. An experiment in the Netherlands tackles this problem by unconventionally replacing fee-for-service by SES-risk-adjusted capitation for oral health. The aims are to reduce unnecessary treatment and costs, while also minimizing social inequalities. Key messages • While policies to reform payment mechanisms are usually relatively easy-to-implement, if they are not accompanied by other policy tools they are unlikely to achieve all intended objectives. • The implementation of VBPs can result in unintended consequences, which may be mitigated through careful design balancing the risks between payers and providers, while accounting for quality of care. Speakers/Panellists Maiwenn Meyer Caisse nationale de l’Assurance Maladie, Paris, France Yohan Wloczysiak Caisse Nationale de l’Assurance Maladie, Paris, France Lukas Schöner TU Berlin, Berlin, Germany Stefan Listl Radboud University, Nijmegen, Netherlands

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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.150
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0060.004
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0200.002

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 head0.294
GPT teacher head0.375
Teacher spread0.081 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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