Enhancing PCORnet Clinical Research Network data completeness by integrating multistate insurance claims with electronic health records in a cloud environment aligned with CMS security and privacy requirements

Abstract Objective The Greater Plains Collaborative (GPC) and other PCORnet Clinical Data Research Networks capture healthcare utilization within their health systems. Here, we describe a reusable environment (GPC Reusable Observable Unified Study Environment [GROUSE]) that integrates hospital and e...

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Veröffentlicht in:Journal of the American Medical Informatics Association : JAMIA 2022-03, Vol.29 (4), p.660-670
Hauptverfasser: Waitman, Lemuel R, Song, Xing, Walpitage, Dammika Lakmal, Connolly, Daniel C, Patel, Lav P, Liu, Mei, Schroeder, Mary C, VanWormer, Jeffrey J, Mosa, Abu Saleh, Anye, Ernest T, Davis, Ann M
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container_end_page 670
container_issue 4
container_start_page 660
container_title Journal of the American Medical Informatics Association : JAMIA
container_volume 29
creator Waitman, Lemuel R
Song, Xing
Walpitage, Dammika Lakmal
Connolly, Daniel C
Patel, Lav P
Liu, Mei
Schroeder, Mary C
VanWormer, Jeffrey J
Mosa, Abu Saleh
Anye, Ernest T
Davis, Ann M
description Abstract Objective The Greater Plains Collaborative (GPC) and other PCORnet Clinical Data Research Networks capture healthcare utilization within their health systems. Here, we describe a reusable environment (GPC Reusable Observable Unified Study Environment [GROUSE]) that integrates hospital and electronic health records (EHRs) data with state-wide Medicare and Medicaid claims and assess how claims and clinical data complement each other to identify obesity and related comorbidities in a patient sample. Materials and Methods EHR, billing, and tumor registry data from 7 healthcare systems were integrated with Center for Medicare (2011–2016) and Medicaid (2011–2012) services insurance claims to create deidentified databases in Informatics for Integrating Biology & the Bedside and PCORnet Common Data Model formats. We describe technical details of how this federally compliant, cloud-based data environment was built. As a use case, trends in obesity rates for different age groups are reported, along with the relative contribution of claims and EHR data-to-data completeness and detecting common comorbidities. Results GROUSE contained 73 billion observations from 24 million unique patients (12.9 million Medicare; 13.9 million Medicaid; 6.6 million GPC patients) with 1 674 134 patients crosswalked and 983 450 patients with body mass index (BMI) linked to claims. Diagnosis codes from EHR and claims sources underreport obesity by 2.56 times compared with body mass index measures. However, common comorbidities such as diabetes and sleep apnea diagnoses were more often available from claims diagnoses codes (1.6 and 1.4 times, respectively). Conclusion GROUSE provides a unified EHR-claims environment to address health system and federal privacy concerns, which enables investigators to generalize analyses across health systems integrated with multistate insurance claims.
doi_str_mv 10.1093/jamia/ocab269
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Here, we describe a reusable environment (GPC Reusable Observable Unified Study Environment [GROUSE]) that integrates hospital and electronic health records (EHRs) data with state-wide Medicare and Medicaid claims and assess how claims and clinical data complement each other to identify obesity and related comorbidities in a patient sample. Materials and Methods EHR, billing, and tumor registry data from 7 healthcare systems were integrated with Center for Medicare (2011–2016) and Medicaid (2011–2012) services insurance claims to create deidentified databases in Informatics for Integrating Biology &amp; the Bedside and PCORnet Common Data Model formats. We describe technical details of how this federally compliant, cloud-based data environment was built. As a use case, trends in obesity rates for different age groups are reported, along with the relative contribution of claims and EHR data-to-data completeness and detecting common comorbidities. Results GROUSE contained 73 billion observations from 24 million unique patients (12.9 million Medicare; 13.9 million Medicaid; 6.6 million GPC patients) with 1 674 134 patients crosswalked and 983 450 patients with body mass index (BMI) linked to claims. Diagnosis codes from EHR and claims sources underreport obesity by 2.56 times compared with body mass index measures. However, common comorbidities such as diabetes and sleep apnea diagnoses were more often available from claims diagnoses codes (1.6 and 1.4 times, respectively). Conclusion GROUSE provides a unified EHR-claims environment to address health system and federal privacy concerns, which enables investigators to generalize analyses across health systems integrated with multistate insurance claims.</description><identifier>ISSN: 1527-974X</identifier><identifier>ISSN: 1067-5027</identifier><identifier>EISSN: 1527-974X</identifier><identifier>DOI: 10.1093/jamia/ocab269</identifier><identifier>PMID: 34897506</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Aged ; Centers for Medicare and Medicaid Services, U.S ; Editor's Choice ; Electronic Health Records ; Humans ; Medicare ; Obesity ; Privacy ; Research and Applications ; United States</subject><ispartof>Journal of the American Medical Informatics Association : JAMIA, 2022-03, Vol.29 (4), p.660-670</ispartof><rights>The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com 2021</rights><rights>The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c420t-8a886432c5f51818df72be626fddbd14f8f8cb020a3291cecbd4c91d267241033</citedby><cites>FETCH-LOGICAL-c420t-8a886432c5f51818df72be626fddbd14f8f8cb020a3291cecbd4c91d267241033</cites><orcidid>0000-0002-8036-2110 ; 0000-0002-8626-137X ; 0000-0002-3712-2904</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8922172/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8922172/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,1584,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34897506$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Waitman, Lemuel R</creatorcontrib><creatorcontrib>Song, Xing</creatorcontrib><creatorcontrib>Walpitage, Dammika Lakmal</creatorcontrib><creatorcontrib>Connolly, Daniel C</creatorcontrib><creatorcontrib>Patel, Lav P</creatorcontrib><creatorcontrib>Liu, Mei</creatorcontrib><creatorcontrib>Schroeder, Mary C</creatorcontrib><creatorcontrib>VanWormer, Jeffrey J</creatorcontrib><creatorcontrib>Mosa, Abu Saleh</creatorcontrib><creatorcontrib>Anye, Ernest T</creatorcontrib><creatorcontrib>Davis, Ann M</creatorcontrib><title>Enhancing PCORnet Clinical Research Network data completeness by integrating multistate insurance claims with electronic health records in a cloud environment aligned with CMS security and privacy requirements</title><title>Journal of the American Medical Informatics Association : JAMIA</title><addtitle>J Am Med Inform Assoc</addtitle><description>Abstract Objective The Greater Plains Collaborative (GPC) and other PCORnet Clinical Data Research Networks capture healthcare utilization within their health systems. Here, we describe a reusable environment (GPC Reusable Observable Unified Study Environment [GROUSE]) that integrates hospital and electronic health records (EHRs) data with state-wide Medicare and Medicaid claims and assess how claims and clinical data complement each other to identify obesity and related comorbidities in a patient sample. Materials and Methods EHR, billing, and tumor registry data from 7 healthcare systems were integrated with Center for Medicare (2011–2016) and Medicaid (2011–2012) services insurance claims to create deidentified databases in Informatics for Integrating Biology &amp; the Bedside and PCORnet Common Data Model formats. We describe technical details of how this federally compliant, cloud-based data environment was built. As a use case, trends in obesity rates for different age groups are reported, along with the relative contribution of claims and EHR data-to-data completeness and detecting common comorbidities. Results GROUSE contained 73 billion observations from 24 million unique patients (12.9 million Medicare; 13.9 million Medicaid; 6.6 million GPC patients) with 1 674 134 patients crosswalked and 983 450 patients with body mass index (BMI) linked to claims. Diagnosis codes from EHR and claims sources underreport obesity by 2.56 times compared with body mass index measures. However, common comorbidities such as diabetes and sleep apnea diagnoses were more often available from claims diagnoses codes (1.6 and 1.4 times, respectively). Conclusion GROUSE provides a unified EHR-claims environment to address health system and federal privacy concerns, which enables investigators to generalize analyses across health systems integrated with multistate insurance claims.</description><subject>Aged</subject><subject>Centers for Medicare and Medicaid Services, U.S</subject><subject>Editor's Choice</subject><subject>Electronic Health Records</subject><subject>Humans</subject><subject>Medicare</subject><subject>Obesity</subject><subject>Privacy</subject><subject>Research and Applications</subject><subject>United States</subject><issn>1527-974X</issn><issn>1067-5027</issn><issn>1527-974X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkstuFDEQRVsIRB6wZIu8ZNPEdr83SKgVHlIgKIDEzqq2q6cd3PbEdk80n5k_wsMMIaxY2aq6dW5JdbPsBaOvGe2Ks2uYNZw5CQOvu0fZMat4k3dN-ePxg_9RdhLCNaWs5kX1NDsqyrZrKlofZ3fndgIrtV2RL_3llcVIeqOtlmDIFQYELyfyGeOt8z-JgghEunltMKLFEMiwJdpGXHmIO8S8mKhDhIipHBafyEikAT0HcqvjRNCgjN4lPpkQTKp4lM6rkPQksY1bFEG70Ukzo40EjF5ZVPvp_tNXElAuXsctAavI2usNyG2C3Cza424iPMuejGACPj-8p9n3d-ff-g_5xeX7j_3bi1yWnMa8hbaty4LLaqxYy1o1NnzAmtejUoNi5diOrRwop1DwjkmUgyplxxSvG14yWhSn2Zs9d70MMyqZvD0YkVaawW-FAy3-7Vg9iZXbiLbjnDU8AV4dAN7dLBiimHWQaAxYdEsQvKZdWaUz0STN91LpXQgex3sbRsUuBuJ3DMQhBkn_8uFu9-o_d__r7Zb1f1i_AMSsxe8</recordid><startdate>20220315</startdate><enddate>20220315</enddate><creator>Waitman, Lemuel R</creator><creator>Song, Xing</creator><creator>Walpitage, Dammika Lakmal</creator><creator>Connolly, Daniel C</creator><creator>Patel, Lav P</creator><creator>Liu, Mei</creator><creator>Schroeder, Mary C</creator><creator>VanWormer, Jeffrey J</creator><creator>Mosa, Abu Saleh</creator><creator>Anye, Ernest T</creator><creator>Davis, Ann M</creator><general>Oxford University Press</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><orcidid>https://orcid.org/0000-0002-8036-2110</orcidid><orcidid>https://orcid.org/0000-0002-8626-137X</orcidid><orcidid>https://orcid.org/0000-0002-3712-2904</orcidid></search><sort><creationdate>20220315</creationdate><title>Enhancing PCORnet Clinical Research Network data completeness by integrating multistate insurance claims with electronic health records in a cloud environment aligned with CMS security and privacy requirements</title><author>Waitman, Lemuel R ; Song, Xing ; Walpitage, Dammika Lakmal ; Connolly, Daniel C ; Patel, Lav P ; Liu, Mei ; Schroeder, Mary C ; VanWormer, Jeffrey J ; Mosa, Abu Saleh ; Anye, Ernest T ; Davis, Ann M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c420t-8a886432c5f51818df72be626fddbd14f8f8cb020a3291cecbd4c91d267241033</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aged</topic><topic>Centers for Medicare and Medicaid Services, U.S</topic><topic>Editor's Choice</topic><topic>Electronic Health Records</topic><topic>Humans</topic><topic>Medicare</topic><topic>Obesity</topic><topic>Privacy</topic><topic>Research and Applications</topic><topic>United States</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Waitman, Lemuel R</creatorcontrib><creatorcontrib>Song, Xing</creatorcontrib><creatorcontrib>Walpitage, Dammika Lakmal</creatorcontrib><creatorcontrib>Connolly, Daniel C</creatorcontrib><creatorcontrib>Patel, Lav P</creatorcontrib><creatorcontrib>Liu, Mei</creatorcontrib><creatorcontrib>Schroeder, Mary C</creatorcontrib><creatorcontrib>VanWormer, Jeffrey J</creatorcontrib><creatorcontrib>Mosa, Abu Saleh</creatorcontrib><creatorcontrib>Anye, Ernest T</creatorcontrib><creatorcontrib>Davis, Ann M</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><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Waitman, Lemuel R</au><au>Song, Xing</au><au>Walpitage, Dammika Lakmal</au><au>Connolly, Daniel C</au><au>Patel, Lav P</au><au>Liu, Mei</au><au>Schroeder, Mary C</au><au>VanWormer, Jeffrey J</au><au>Mosa, Abu Saleh</au><au>Anye, Ernest T</au><au>Davis, Ann M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Enhancing PCORnet Clinical Research Network data completeness by integrating multistate insurance claims with electronic health records in a cloud environment aligned with CMS security and privacy requirements</atitle><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle><addtitle>J Am Med Inform Assoc</addtitle><date>2022-03-15</date><risdate>2022</risdate><volume>29</volume><issue>4</issue><spage>660</spage><epage>670</epage><pages>660-670</pages><issn>1527-974X</issn><issn>1067-5027</issn><eissn>1527-974X</eissn><abstract>Abstract Objective The Greater Plains Collaborative (GPC) and other PCORnet Clinical Data Research Networks capture healthcare utilization within their health systems. Here, we describe a reusable environment (GPC Reusable Observable Unified Study Environment [GROUSE]) that integrates hospital and electronic health records (EHRs) data with state-wide Medicare and Medicaid claims and assess how claims and clinical data complement each other to identify obesity and related comorbidities in a patient sample. Materials and Methods EHR, billing, and tumor registry data from 7 healthcare systems were integrated with Center for Medicare (2011–2016) and Medicaid (2011–2012) services insurance claims to create deidentified databases in Informatics for Integrating Biology &amp; the Bedside and PCORnet Common Data Model formats. We describe technical details of how this federally compliant, cloud-based data environment was built. As a use case, trends in obesity rates for different age groups are reported, along with the relative contribution of claims and EHR data-to-data completeness and detecting common comorbidities. Results GROUSE contained 73 billion observations from 24 million unique patients (12.9 million Medicare; 13.9 million Medicaid; 6.6 million GPC patients) with 1 674 134 patients crosswalked and 983 450 patients with body mass index (BMI) linked to claims. Diagnosis codes from EHR and claims sources underreport obesity by 2.56 times compared with body mass index measures. However, common comorbidities such as diabetes and sleep apnea diagnoses were more often available from claims diagnoses codes (1.6 and 1.4 times, respectively). 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source MEDLINE; Oxford University Press Journals All Titles (1996-Current); EZB-FREE-00999 freely available EZB journals; PubMed Central
subjects Aged
Centers for Medicare and Medicaid Services, U.S
Editor's Choice
Electronic Health Records
Humans
Medicare
Obesity
Privacy
Research and Applications
United States
title Enhancing PCORnet Clinical Research Network data completeness by integrating multistate insurance claims with electronic health records in a cloud environment aligned with CMS security and privacy requirements
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