Treatment Level and Store Level Analyses of Healthcare Data

Kaiwen Wang, Jiehui Ding, Kristen R. Lidwell, Scott Manski, Gee Y. Lee and Emilio Xavier Esposito
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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
Jiehui Ding: Department of Statistics and Probability, Michigan State University, C413 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
Emilio Xavier Esposito: exeResearch, LLC, 32 University Dr, East Lansing, MI 48823, USA

Risks, 2019, vol. 7, issue 2, 1-22

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.

Keywords: medical data analysis; store sales analysis; predictive modeling; generalized additive models (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jrisks:v:7:y:2019:i:2:p:43-:d:223823

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