Evidence map›Paper›PMID 40824192›Full record

ArticleHealth economics2025

Incentivizing Hospital Quality Through Care Bundling.

Katja Grašič, Adrián Villaseñor, James Gaughan, Nils Gutacker, Luigi Siciliani

Abstract read
In one paragraph

Article in Health economics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Katja GrašičCentre for Health Economics, University of York, York, UK.
Adrián VillaseñorCentre for Health Economics, University of York, York, UK.ORCID 0000-0003-0940-045X
James GaughanCentre for Health Economics, University of York, York, UK.
Nils GutackerCentre for Health Economics, University of York, York, UK.ORCID 0000-0002-2833-0621
Luigi SicilianiDepartment of Economics and Related Studies, University of York, York, UK.ORCID 0000-0003-1739-7289

Funding

National Institute for Health and Care Research
6 · The paper itself

Abstract

Policymakers increasingly implement pay-for-performance schemes to incentivize quality of care. A key design issue when incentivizing several process measures of quality relates to whether the payment should be linked to the performance on each measure or whether the payment should be conditional on all of the process measures of quality being provided, which we refer to as "care bundling". After developing a theoretical framework of provider incentives under care bundling, we employ a difference-in-difference analysis to evaluate the Best Practice Tariff for fragility hip fracture, introduced in England in 2010, which rewards providers based on a care bundle of nine process measures that need to be jointly achieved. The design of the processes was evidence-based and the size of the bonus was significant, up to 20% of the baseline tariff. The results suggest that the policy was successful in increasing the proportion of patients for whom all of the criteria are met by 52.5 percentage points in the first 5 years after its introduction. Temporal ordering of processes might matter under care bundling, but we do not find evidence that English providers exerted less effort to meet process measures if they already failed to meet an earlier one. Overall, we find that a scheme based on care bundle, which is evidence based and uses a sizable bonus, can be effective in improving hospital performance.

Indexed as

HospitalsPatient Care BundlesQuality of Health CareReimbursement, IncentiveEnglandHip FracturesHumansState Medicineincentivespay for performanceprovider behaviorquality

Identifiers

PMID40824192
PMCPMC12496025

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.