Evidence map›Paper›PMID 37118700›Full record

ArticleBMC public health2023

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

Shekoofeh Sadat Momahhed, Sara Emamgholipour Sefiddashti, Behrouz Minaei, Zahra Shahali

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Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Shekoofeh Sadat MomahhedDepartment of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Sara Emamgholipour SefiddashtiDepartment of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. s-emamgholipour@tums.ac.ir.
Behrouz MinaeiSchool of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.
Zahra ShahaliNational Center for Health Insurance Research (Iran Health Insurance Organization), Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe 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.

settingThe 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.

designThe 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.

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

resultsInsureds 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.

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

Indexed as

OutpatientsPrescriptionsAdultCluster AnalysisFemaleHumansIranMaleRetrospective StudiesUnited StatesHealth insurancek-means clusteringMedication costPrescription claimsRisk class

Identifiers

PMID37118700
PMCPMC10142779

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