The Optimal Co-Insurance Rate for Outpatient Drug Expenses of Iranian Health Insured Based on the Data Mining Method Publisher Pubmed



Momahhed SS¹ ; Sefiddashti SE¹ ; Minaei B² ; Arab M³ Show Affiliations
Source: International Journal for Equity in Health Published:2024

Abstract

Objective: A more equal allocation of healthcare funds for patients who must pay high costs of care ensures the welfare of society. This study aimed to estimate the optimal co-insurance for outpatient drug costs for health insurance. Setting: The research population includes outpatient prescription claims made by the Health Insurance Organization that outpatient prescriptions in a timely manner in 2016, 2017, 2018, and 2019 were utilized to calculate the optimal co-insurance. The study population was representative of the research sample. Design: At the secondary level of care, 11 features of outpatient claims were studied cross-sectionally and retrospectively using data mining. Optimal co-insurance was estimated using Westerhut and Folmer's utility model. Participants: One hundred ninety-three thousand five hundred fifty-two individuals were created from 21 776 350 outpatient claims of health insurance. Because of cost-sharing, insured individuals in a low-income subsidy plan and those with refractory diseases were excluded. Results: Insureds were divided into three classes of low, middle, and high risk based on IQR and were separated to three clusters using the silhouette coefficient. For the first, second, and third clusters of the low-risk class, the optimal co-insurance estimates are 0.81, 0.76, and 0.84, respectively. It was equal to one for all middle-class clusters and 0.38, 0.45, and 0.42, respectively, for the high-risk class. The insurer's expenses were altered by $3,130,463, $3,451,194, and $ 1,069,859 profit for the first, second, and third clusters, respectively, when the optimal co-insurance strategy is used for the low-risk class. For middle risks, it was US$29,239,815, US$13,863,810, and US$ 14,573,432 while for high risks, US$4,722,099, US$ 6,339,317, and US$19,627,062, respectively. Conclusions: These findings can improve vulnerable populations' access to costly medications, reduce resource waste, and help insurers distribute funds more efficiently. © The Author(s) 2024.