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Plan responses to diagnosis-based payment: Evidence from Germany’s morbidity-based risk adjustment

Author

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  • Bauhoff, Sebastian
  • Fischer, Lisa
  • Göpffarth, Dirk
  • Wuppermann, Amelie C.

Abstract

Many competitive health insurance markets adjust payments to participating health plans according to their enrollees’ risk − including based on diagnostic information. We investigate responses of German health plans to the introduction of morbidity-based risk adjustment in the Statutory Health Insurance in 2009, which triggers payments based on “validated” diagnoses by providers. Using the regulator’s data from office-based physicians, we estimate a difference-in-difference analysis of the change in the share and number of validated diagnoses for ICD codes that are inside or outside the risk adjustment but are otherwise similar. We find a differential increase in the share of validated diagnoses of 2.6 and 3.6 percentage points (3–4%) between 2008 and 2013. This increase appears to originate from both a shift from not-validated toward validated diagnoses and an increase in the number of such diagnoses. Overall, our results indicate that plans were successful in influencing physicians’ coding practices in a way that could lead to higher payments.

Suggested Citation

  • Bauhoff, Sebastian & Fischer, Lisa & Göpffarth, Dirk & Wuppermann, Amelie C., 2017. "Plan responses to diagnosis-based payment: Evidence from Germany’s morbidity-based risk adjustment," Journal of Health Economics, Elsevier, vol. 56(C), pages 397-413.
    • Handle: RePEc:eee:jhecon:v:56:y:2017:i:c:p:397-413
    DOI: 10.1016/j.jhealeco.2017.03.001
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    Cited by:

    1. Chien, Ling-Chen & Chou, Yiing-Jenq & Huang, Yu-Chin & Shen, Yi-Jung & Huang, Nicole, 2020. "Reducing low value services in surgical inpatients in Taiwan: Does diagnosis-related group payment work?," Health Policy, Elsevier, vol. 124(1), pages 89-96.
    2. Marica Iommi & Savannah Bergquist & Gianluca Fiorentini & Francesco Paolucci, 2022. "Comparing risk adjustment estimation methods under data availability constraints," Health Economics, John Wiley & Sons, Ltd., vol. 31(7), pages 1368-1380, July.
    3. Danny Wende, 2019. "Spatial risk adjustment between health insurances: using GWR in risk adjustment models to conserve incentives for service optimisation and reduce MAUP," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(7), pages 1079-1091, September.
    4. Richard C. van Kleef & René C. J. A. van Vliet, 2022. "How to deal with persistently low/high spenders in health plan payment systems?," Health Economics, John Wiley & Sons, Ltd., vol. 31(5), pages 784-805, May.
    5. Jürges, Hendrik & Kopetsch, Thomas, 2021. "Prenatal exposure to the German food crisis 1944–1948 and health after 65 years," Economics & Human Biology, Elsevier, vol. 40(C).

    More about this item

    Keywords

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    JEL classification:

    • H51 - Public Economics - - National Government Expenditures and Related Policies - - - Government Expenditures and Health
    • I1 - Health, Education, and Welfare - - Health
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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