K-means clustering of outpatient prescription claims for health insureds in Iran

Abstract

Objective: The segmentation of consumers based on their behavior and needs is the most crucial action of the health insurance organization. This study's objective is to cluster Iranian health insureds according to their demographics and data on outpatient prescriptions.

Setting: The population in this study corresponded to the research sample. The Health Insurance Organization's outpatient claims were registered consecutively in 2016, 2017, 2018, and 2019 were clustered.

Design: The k-means clustering algorithm was used to cross-sectionally and retrospectively analyze secondary data from outpatient prescription claims for secondary care using Python 3.10.

Participants: The current analysis transformed 21 776 350 outpatient prescription claims from health insured into 193 552 insureds.

Results: Insureds using IQR were split into three classes: low, middle, and high risk. Based on the silhouette coefficient, the insureds of all classes were divided into three clusters. In all data for a period of four years, the first through third clusters, there were 21 799, 7170, and 19 419 insureds in the low-risk class. Middle-risk class had 48 348,23 321, 25 107 insureds, and 14 037, 28 504, 5847 insured in the high-risk class were included. For the first cluster of low-risk insureds: the total average cost of prescriptions paid by the insurance for the insureds was $211, the average age was 26 years, the average franchise was 88.5US$, the average number of medications and prescriptions were 409 and 62, the total average costs of prescriptions Outpatient was 302.5 US$, the total average number of medications for acute and chronic disease was 178 and 215, respectively. The majority of insureds were men, and those who were part of the householder's family.

Conclusions: By segmenting insurance customers, insurers can set insurance premium rates, controlling the risk of loss, which improves their capacity to compete in the insurance market.

Keywords: Health insurance; Medication cost; Prescription claims; Risk class; k-means clustering.

Fig. 1

Silhouette plot produced empirically for low risk k-means clustering

Fig. 2

Silhouette plot produced empirically for middle risk k-means clustering

Fig. 3

Silhouette plot produced empirically for high risk k-means clustering

Fig. 4

The number of insureds in each class, broken down by gender

Fig. 5

The number of insureds in each class, broken down by age