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Intervening on the Data to Improve the Performance of Health Plan Payment Methods

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

Abstract

The conventional method for developing health care plan payment systems uses existing data to study alternative algorithms with the purpose of creating incentives for an efficient and fair health care system. In this paper, we take a different approach and modify the input data rather than the algorithm, so that the data used for calibration reflect the desired levels of spending rather than the observed spending levels typically used for setting health plan payments. We refer to our proposed method as “intervening on the data.” We first present a general economic model that incorporates the previously overlooked two-way relationship between health plan payment and insurer actions. We then demonstrate our approach in two applications in Medicare: an inefficiency example focused on underprovision of care for individuals with chronic illnesses, and an unfairness example addressing health care disparities by geographic income levels. We empirically compare intervening on the data to two other methods commonly used to address inefficiencies and disparities: adding risk adjustor variables, and introducing constraints on the risk adjustment coefficients to redirect revenues. Adding risk adjustors, while the most common policy approach, is the least effective method in our applications. Intervening on the data and constrained regression are both effective. The “side effects” of these approaches, though generally positive, vary according to the empirical context. Intervening on the data is an easy-to-use, intuitive approach for addressing economic efficiency and fairness misallocations in individual health insurance markets.

Suggested Citation

  • Savannah L. Bergquist & Timothy J. Layton & Thomas G. McGuire & Sherri Rose, 2018. "Intervening on the Data to Improve the Performance of Health Plan Payment Methods," NBER Working Papers 24491, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24491
    Note: HC HE
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    References listed on IDEAS

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    1. Michael Geruso & Timothy Layton & Daniel Prinz, 2019. "Screening in Contract Design: Evidence from the ACA Health Insurance Exchanges," American Economic Journal: Economic Policy, American Economic Association, vol. 11(2), pages 64-107, May.
    2. Kurt Lavetti & Kosali Simon, 2018. "Strategic Formulary Design in Medicare Part D Plans," American Economic Journal: Economic Policy, American Economic Association, vol. 10(3), pages 154-192, August.
    3. Thomas G. McGuire & Jacob Glazer, 2000. "Optimal Risk Adjustment in Markets with Adverse Selection: An Application to Managed Care," American Economic Review, American Economic Association, vol. 90(4), pages 1055-1071, September.
    4. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    5. Layton, Timothy J. & Ellis, Randall P. & McGuire, Thomas G. & van Kleef, Richard, 2017. "Measuring efficiency of health plan payment systems in managed competition health insurance markets," Journal of Health Economics, Elsevier, vol. 56(C), pages 237-255.
    6. Glazer, Jacob & McGuire, Thomas G., 2002. "Setting health plan premiums to ensure efficient quality in health care: minimum variance optimal risk adjustment," Journal of Public Economics, Elsevier, vol. 84(2), pages 153-173, May.
    7. Layton, Timothy J. & McGuire, Thomas G. & van Kleef, Richard C., 2018. "Deriving risk adjustment payment weights to maximize efficiency of health insurance markets," Journal of Health Economics, Elsevier, vol. 61(C), pages 93-110.
    8. Frank, Richard G. & Glazer, Jacob & McGuire, Thomas G., 2000. "Measuring adverse selection in managed health care," Journal of Health Economics, Elsevier, vol. 19(6), pages 829-854, November.
    9. Colleen Carey, 2017. "Technological Change and Risk Adjustment: Benefit Design Incentives in Medicare Part D," American Economic Journal: Economic Policy, American Economic Association, vol. 9(1), pages 38-73, February.
    10. Joseph P. Newhouse & Mary Price & John Hsu & J. Michael McWilliams & Thomas G. McGuire, 2015. "How Much Favorable Selection Is Left in Medicare Advantage?," American Journal of Health Economics, University of Chicago Press, vol. 1(1), pages 1-26, Winter.
    11. McGuire, Thomas G. & Newhouse, Joseph P. & Normand, Sharon-Lise & Shi, Julie & Zuvekas, Samuel, 2014. "Assessing incentives for service-level selection in private health insurance exchanges," Journal of Health Economics, Elsevier, vol. 35(C), pages 47-63.
    12. Mark Shepard, 2016. "Hospital Network Competition and Adverse Selection: Evidence from the Massachusetts Health Insurance Exchange," NBER Working Papers 22600, National Bureau of Economic Research, Inc.
    13. Richard C. Kleef & Thomas G. McGuire & René C. J. A. Vliet & Wynand P. P. M. de Ven, 2017. "Improving risk equalization with constrained regression," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 18(9), pages 1137-1156, December.
    14. Han, Tony & Lavetti, Kurt, 2017. "Does Part D abet advantageous selection in Medicare Advantage?," Journal of Health Economics, Elsevier, vol. 56(C), pages 368-382.
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    1. Layton, Timothy J. & McGuire, Thomas G. & van Kleef, Richard C., 2018. "Deriving risk adjustment payment weights to maximize efficiency of health insurance markets," Journal of Health Economics, Elsevier, vol. 61(C), pages 93-110.

    More about this item

    JEL classification:

    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private

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