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Data transformations to improve the performance of health plan payment methods

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  • Bergquist, Savannah L.
  • Layton, Timothy J.
  • McGuire, Thomas G.
  • Rose, Sherri

Abstract

The conventional method for developing health care plan payment systems uses observed data to study alternative algorithms and set incentives for the health care system. In this paper, we take a different approach and transform the input data rather than the algorithm, so that the data used reflect the desired spending levels rather than the observed spending levels. We present a general economic model that incorporates the previously overlooked two-way relationship between health plan payment and insurer actions. We then demonstrate our systematic approach for data transformations in two Medicare applications: underprovision of care for individuals with chronic illnesses and health care disparities by geographic income levels. Empirically comparing our method to two other common approaches shows that the “side effects” of these approaches vary by context, and that data transformation is an effective tool for addressing misallocations in individual health insurance markets.

Suggested Citation

  • Bergquist, Savannah L. & Layton, Timothy J. & McGuire, Thomas G. & Rose, Sherri, 2019. "Data transformations to improve the performance of health plan payment methods," Journal of Health Economics, Elsevier, vol. 66(C), pages 195-207.
  • Handle: RePEc:eee:jhecon:v:66:y:2019:i:c:p:195-207
    DOI: 10.1016/j.jhealeco.2019.05.005
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    References listed on IDEAS

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    Cited by:

    1. Thomas G. McGuire & Anna L. Zink & Sherri Rose, 2020. "Simplifying and Improving the Performance of Risk Adjustment Systems," NBER Working Papers 26736, National Bureau of Economic Research, Inc.
    2. Anna Zink & Sherri Rose, 2020. "Fair regression for health care spending," Biometrics, The International Biometric Society, vol. 76(3), pages 973-982, September.

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