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From downcoding to upcoding: DRG based payment in hospitals

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  • Carine Milcent

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract

A prospective disease group-based payment is a reimbursement rule used in a wide array of countries. It turns to be the hospital's payment rule to imply. The secret of this payment is a fee payment as well as a hospital's activity based payment. There is a consensus to consider this rule of payment as the least likely to be manipulated by the actors. However, the defined fee per group depends on recorded information that is then processed using complex algorithms. What if the data itself can be manipulated? The result would be a fee per group based on manipulated factors that would lead to an inefficient budget allocation between hospitals. Using a unique French longitudinal database with 145 million stays, I unambiguously demonstrate that the implementation of a finer classification led to an upcoding-learning effect. The end result has been a budget transfer from public non-research hospitals to for-profit hospitals. The 2009 policy lead to upcoding disconnected from any changes in the trend of production of care.

Suggested Citation

  • Carine Milcent, 2021. "From downcoding to upcoding: DRG based payment in hospitals," Post-Print halshs-02317416, HAL.
  • Handle: RePEc:hal:journl:halshs-02317416
    DOI: 10.1007/s10754-020-09287-x
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-02317416
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 22nd March 2021
      by Chris Sampson in The Academic Health Economists' Blog on 2021-03-22 12:00:01

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    Cited by:

    1. Carine Milcent, 2023. "Bias due to re-used databases: Coding in hospital for extremely vulnerable patients," Working Papers hal-03960584, HAL.
    2. Kjartan Sarheim Anthun, 2022. "Predicting diagnostic coding in hospitals: individual level effects of price incentives," International Journal of Health Economics and Management, Springer, vol. 22(2), pages 129-146, June.
    3. András Kiss & Norbert Kiss & Balázs Váradi, 2023. "Do budget constraints limit access to health care? Evidence from PCI treatments in Hungary," International Journal of Health Economics and Management, Springer, vol. 23(2), pages 281-302, June.
    4. Alex Proshin & Alexandre Cazenave‐Lacroutz & Lise Rochaix, 2023. "Impact of tariff refinement on the choice between scheduled C‐section and normal delivery: Evidence from France," Health Economics, John Wiley & Sons, Ltd., vol. 32(7), pages 1397-1433, July.
    5. François Maréchal & Lionel Thomas, 2021. "The impact of medical complications on optimal hospital payment," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 23(6), pages 1144-1173, December.

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    More about this item

    Keywords

    Hospital stays; Diagnosis-related groups (DRGs); Upcoding; Manipulated database; heterogeneity in responses;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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