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Treatment Level and Store Level Analyses of Healthcare Data

Author

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  • Kaiwen Wang

    (Department of Statistics and Probability, Michigan State University, C413 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA
    Department of Mathematics, Michigan State University, C212 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA)

  • Jiehui Ding

    (Department of Statistics and Probability, Michigan State University, C413 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA
    Department of Mathematics, Michigan State University, C212 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA)

  • Kristen R. Lidwell

    (Department of Statistics and Probability, Michigan State University, C413 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA)

  • Scott Manski

    (Department of Statistics and Probability, Michigan State University, C413 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA)

  • Gee Y. Lee

    (Department of Statistics and Probability, Michigan State University, C413 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA
    Department of Mathematics, Michigan State University, C212 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA)

  • Emilio Xavier Esposito

    (exeResearch, LLC, 32 University Dr, East Lansing, MI 48823, USA)

Abstract

The presented research discusses general approaches to analyze and model healthcare data at the treatment level and at the store level. The paper consists of two parts: (1) a general analysis method for store-level product sales of an organization and (2) a treatment-level analysis method of healthcare expenditures. In the first part, our goal is to develop a modeling framework to help understand the factors influencing the sales volume of stores maintained by a healthcare organization. In the second part of the paper, we demonstrate a treatment-level approach to modeling healthcare expenditures. In this part, we aim to improve the operational-level management of a healthcare provider by predicting the total cost of medical services. From this perspective, treatment-level analyses of medical expenditures may help provide a micro-level approach to predicting the total amount of expenditures for a healthcare provider. We present a model for analyzing a specific type of medical data, which may arise commonly in a healthcare provider’s standardized database. We do this by using an extension of the frequency-severity approach to modeling insurance expenditures from the actuarial science literature.

Suggested Citation

  • Kaiwen Wang & Jiehui Ding & Kristen R. Lidwell & Scott Manski & Gee Y. Lee & Emilio Xavier Esposito, 2019. "Treatment Level and Store Level Analyses of Healthcare Data," Risks, MDPI, vol. 7(2), pages 1-22, April.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:2:p:43-:d:223823
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    References listed on IDEAS

    as
    1. Frees, Edward W. & Meyers, Glenn & Cummings, A. David, 2011. "Summarizing Insurance Scores Using a Gini Index," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1085-1098.
    2. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521828284, October.
    3. Shi, Peng, 2012. "Multivariate longitudinal modeling of insurance company expenses," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 204-215.
    4. de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149, October.
    5. Frees, Edward W. & Valdez, Emiliano A., 2008. "Hierarchical Insurance Claims Modeling," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1457-1469.
    6. Smith, Michael & Min, Aleksey & Almeida, Carlos & Czado, Claudia, 2010. "Modeling Longitudinal Data Using a Pair-Copula Decomposition of Serial Dependence," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1467-1479.
    7. Sun, Jiafeng & Frees, Edward W. & Rosenberg, Marjorie A., 2008. "Heavy-tailed longitudinal data modeling using copulas," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 817-830, April.
    8. Edward Frees & Jie Gao & Marjorie Rosenberg, 2011. "Predicting the Frequency and Amount of Health Care Expenditures," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(3), pages 377-392.
    9. Frees,Edward W., 2004. "Longitudinal and Panel Data," Cambridge Books, Cambridge University Press, number 9780521535380, October.
    10. Edward W. Frees, 2015. "Analytics of Insurance Markets," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 253-277, December.
    11. Keeler, Emmett B. & Rolph, John E., 1988. "The demand for episodes of treatment in the health insurance experiment," Journal of Health Economics, Elsevier, vol. 7(4), pages 337-367, December.
    12. Edward W. Frees & Gee Lee & Lu Yang, 2016. "Multivariate Frequency-Severity Regression Models in Insurance," Risks, MDPI, vol. 4(1), pages 1-36, February.
    13. Marjorie Rosenberg & Phillip Farrell, 2008. "Predictive Modeling of Costs for a Chronic Disease with Acute High-Cost Episodes," North American Actuarial Journal, Taylor & Francis Journals, vol. 12(1), pages 1-19.
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