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K-means clustering of outpatient prescription claims for health insureds in Iran
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. The population in this study corres...
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Published in: | BMC public health 2023-04, Vol.23 (1), p.788-788, Article 788 |
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description | 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.
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.
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.
The current analysis transformed 21 776 350 outpatient prescription claims from health insured into 193 552 insureds.
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.
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. |
doi_str_mv | 10.1186/s12889-023-15753-1 |
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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.
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.
The current analysis transformed 21 776 350 outpatient prescription claims from health insured into 193 552 insureds.
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.
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.</description><identifier>ISSN: 1471-2458</identifier><identifier>EISSN: 1471-2458</identifier><identifier>DOI: 10.1186/s12889-023-15753-1</identifier><identifier>PMID: 37118700</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Adult ; Cluster Analysis ; Female ; Health insurance ; Humans ; Iran ; k-means clustering ; Male ; Medication cost ; Outpatients ; Prescription claims ; Prescriptions ; Retrospective Studies ; Risk class ; United States</subject><ispartof>BMC public health, 2023-04, Vol.23 (1), p.788-788, Article 788</ispartof><rights>2023. The Author(s).</rights><rights>The Author(s) 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-78067fa28c3a5ca28e5048a0e6cf2352f4fb857c3863a84ea35fd314334844983</citedby><cites>FETCH-LOGICAL-c469t-78067fa28c3a5ca28e5048a0e6cf2352f4fb857c3863a84ea35fd314334844983</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142779/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142779/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,37013,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37118700$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Momahhed, Shekoofeh Sadat</creatorcontrib><creatorcontrib>Emamgholipour Sefiddashti, Sara</creatorcontrib><creatorcontrib>Minaei, Behrouz</creatorcontrib><creatorcontrib>Shahali, Zahra</creatorcontrib><title>K-means clustering of outpatient prescription claims for health insureds in Iran</title><title>BMC public health</title><addtitle>BMC Public Health</addtitle><description>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.
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.
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.
The current analysis transformed 21 776 350 outpatient prescription claims from health insured into 193 552 insureds.
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.
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.</description><subject>Adult</subject><subject>Cluster Analysis</subject><subject>Female</subject><subject>Health insurance</subject><subject>Humans</subject><subject>Iran</subject><subject>k-means clustering</subject><subject>Male</subject><subject>Medication cost</subject><subject>Outpatients</subject><subject>Prescription claims</subject><subject>Prescriptions</subject><subject>Retrospective Studies</subject><subject>Risk class</subject><subject>United States</subject><issn>1471-2458</issn><issn>1471-2458</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpVkc1uFTEMhSMEoqXwAizQLNlMyX8yK4SqAldUggWsIzfj3JtqZjIkmUq8PaG3VO3Gtuzjz4kOIW8ZPWfM6g-FcWuHnnLRM2VUi8_IKZOG9Vwq-_xRfUJelXJDKTNW8ZfkRJgGMJSekh_f-hlhKZ2ftlIxx2XfpdClra5QIy61WzMWn-NaY1qaCuJcupByd0CY6qGLS9kyjqUV3S7D8pq8CDAVfHOfz8ivz5c_L772V9-_7C4-XfVe6qH2xlJtAnDrBSjfMioqLVDUPnCheJDh2irjhdUCrEQQKoyCSSGklXKw4ozsjtwxwY1bc5wh_3EJortrpLx3kGv0EzqrKVCrB9B8lGHkg-QguReGMhx9wMb6eGSt2_XcWu3bGaYn0KeTJR7cPt06RpnkxgyN8P6ekNPvDUt1cywepwkWTFtx3FIzMCu1bFJ-lPqcSskYHu4w6v756o6-uuaru_PVsbb07vELH1b-Gyn-Aj6QnqM</recordid><startdate>20230428</startdate><enddate>20230428</enddate><creator>Momahhed, Shekoofeh Sadat</creator><creator>Emamgholipour Sefiddashti, Sara</creator><creator>Minaei, Behrouz</creator><creator>Shahali, Zahra</creator><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20230428</creationdate><title>K-means clustering of outpatient prescription claims for health insureds in Iran</title><author>Momahhed, Shekoofeh Sadat ; Emamgholipour Sefiddashti, Sara ; Minaei, Behrouz ; Shahali, Zahra</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c469t-78067fa28c3a5ca28e5048a0e6cf2352f4fb857c3863a84ea35fd314334844983</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adult</topic><topic>Cluster Analysis</topic><topic>Female</topic><topic>Health insurance</topic><topic>Humans</topic><topic>Iran</topic><topic>k-means clustering</topic><topic>Male</topic><topic>Medication cost</topic><topic>Outpatients</topic><topic>Prescription claims</topic><topic>Prescriptions</topic><topic>Retrospective Studies</topic><topic>Risk class</topic><topic>United States</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Momahhed, Shekoofeh Sadat</creatorcontrib><creatorcontrib>Emamgholipour Sefiddashti, Sara</creatorcontrib><creatorcontrib>Minaei, Behrouz</creatorcontrib><creatorcontrib>Shahali, Zahra</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Momahhed, Shekoofeh Sadat</au><au>Emamgholipour Sefiddashti, Sara</au><au>Minaei, Behrouz</au><au>Shahali, Zahra</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>K-means clustering of outpatient prescription claims for health insureds in Iran</atitle><jtitle>BMC public health</jtitle><addtitle>BMC Public Health</addtitle><date>2023-04-28</date><risdate>2023</risdate><volume>23</volume><issue>1</issue><spage>788</spage><epage>788</epage><pages>788-788</pages><artnum>788</artnum><issn>1471-2458</issn><eissn>1471-2458</eissn><abstract>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.
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.
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.
The current analysis transformed 21 776 350 outpatient prescription claims from health insured into 193 552 insureds.
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.
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.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>37118700</pmid><doi>10.1186/s12889-023-15753-1</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Cluster Analysis Female Health insurance Humans Iran k-means clustering Male Medication cost Outpatients Prescription claims Prescriptions Retrospective Studies Risk class United States |
title | K-means clustering of outpatient prescription claims for health insureds in Iran |
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