ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2023
"The reputation premium": does hospital ranking improvement lead to a higher healthcare spending?
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- The impact of hospital price and quality transparency tools on healthcare spending: a systematic review.Health economics review · 2022Review
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Authors and funding
2 authors.
Funding
Abstract
Global health systems have often disclosed hospital quality and performance information via hospital ranking or rating programs over the last 20 years. This study aims to examine the relationship between hospital ranking and healthcare spending. Using the Basic Medical Insurance claims data from a big city in central China and the hospital ranking data from the Fudan Chinese Hospital League Table from 2016 to 2018, this study exploits the variation of hospital reputable ranking across hospitals and periods to employ the difference-in-differences (DiD) design. To alleviate the self-selection bias emerging from inpatients' selection of hospitals and the extrapolation bias emerging from the potential mis-specification of our linear model, we combine the DiD design with the 3-to-1 optimal Mahalanobis metric matching method. This study finds that ceteris paribus one hospital ascending from the Regional Famous Hospital Group to the National Famous Hospital Group significantly increases inpatients' total healthcare costs, reimbursement costs, and out-of-pocket costs by 5.9%, 6.2%, and 4.0%, respectively. Mechanism analysis reveals that it should be attributed more to physician moral hazard than patient willingness-to-pay. Leads and lags (event study) analysis validates our DiD identification framework and shows that the impact materializes slowly but significantly. In the robustness check, we transfer the outcome variables from the log value to the level value and control five digits of ICD-10 for the disease fixed-effects. The results are highly robust.
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Registered trials
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