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Constructing a Novel Early Warning Algorithm for Global Budget Payments

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  • Che-Wei Chang

    (Department of Recreational and Graduate Institute of Recreational Sport Management, National Taiwan University of Sport, Taichung City 40404, Taiwan)

Abstract

The National Health Insurance Administration of Taiwan has implemented global budget payments, the Diagnosis-Related Group (DRG) inpatient diagnosis-related group payment system, and the same-disease payment system, in order to decrease the financial burden of medical expenditure. However, the benefit system reduces the income of doctors and hospitals. This study proposed an early warning payment algorithm that applies data analytics technology to diabetes hospitalization- and treatment-related fees. A model was constructed based on the characteristics of the Exponentially Weighted Moving Average (EWMA) algorithm to develop control charts, which were first employed using the 2001–2017 health insurance statistical database released by the Department of Health Insurance (DHI). This model was used to simulate data from inpatients with diabetes, to create an early warning algorithm for diagnosis-related groups’ (DRGs’) medical payments as well as to measure its accuracy. This study will provide a reference for the formulation of payment policies by the DHI.

Suggested Citation

  • Che-Wei Chang, 2020. "Constructing a Novel Early Warning Algorithm for Global Budget Payments," Mathematics, MDPI, vol. 8(11), pages 1-11, November.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2006-:d:442688
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    References listed on IDEAS

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    1. Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.
    2. Daniel Gartner & Rainer Kolisch & Daniel B. Neill & Rema Padman, 2015. "Machine Learning Approaches for Early DRG Classification and Resource Allocation," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 718-734, November.
    3. Michele Scagliarini & Mariarosaria Apreda & Ulrich Wienand & Giorgia Valpiani, 2016. "Exponentially Weighted Moving Average Control Schemes for Assessing Hospital Organizational Performance," Statistica, Department of Statistics, University of Bologna, vol. 76(2), pages 127-139.
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