Constructing a Novel Early Warning Algorithm for Global Budget Payments

• Published in: Mathematics (Basel) Vol. 8; no. 11; p. 2006
• Main Author: Chang, Che-Wei
• Format: Journal Article
• Language: English
• Published: MDPI AG 01.11.2020
• Subjects: decision science analysis; early warning system decision model; Exponentially Weighted Moving Average (EWMA); global payment; machine learning; statistical model
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math8112006

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.