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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
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