Comparison of Electronic Health Record–Based and Claims‐Based Diabetes Care Quality Measures: Causes of Discrepancies
Objective To investigate magnitude and sources of discrepancy in quality metrics using claims versus electronic health record (EHR) data. Study Design Assessment of proportions of HbA1c and LDL testing for people ascertained as diabetic from the respective sources. Qualitative interviews and review...
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Veröffentlicht in: | Health services research 2018-08, Vol.53 (4), p.2988-3006 |
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creator | Laws, Michael Barton Michaud, Joanne Shield, Renee McQuade, William Wilson, Ira B. |
description | Objective
To investigate magnitude and sources of discrepancy in quality metrics using claims versus electronic health record (EHR) data.
Study Design
Assessment of proportions of HbA1c and LDL testing for people ascertained as diabetic from the respective sources. Qualitative interviews and review of EHRs of discrepant cases.
Data Collection/Extraction
Claims submitted to Rhode Island Medicaid by three practice sites in 2013; program‐coded EHR extraction; manual review of selected EHRs.
Principal Findings
Of 21,030 adult Medicaid beneficiaries attributed to a primary care patient at a site by claims or EHR data, concordance on assignment ranged from 0.30 to 0.41. Of patients with concordant assignment, the ratio of patients ascertained as diabetic by EHR versus claims ranged from 1.06 to 1.14. For patients with concordant assignment and diagnosis, the ratio based on EHR versus claims ranged from 1.08 to 18.34 for HbA1c testing, and from 1.29 to 14.18 for lipid testing. Manual record review of 264 patients discrepant on diagnosis or testing identified problems such as misuse of ICD‐9 codes, failure to submit claims, and others.
Conclusions
Claims data underestimate performance on these metrics compared to EHR documentation, by varying amounts. Use of claims data for these metrics is problematic. |
doi_str_mv | 10.1111/1475-6773.12819 |
format | Article |
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To investigate magnitude and sources of discrepancy in quality metrics using claims versus electronic health record (EHR) data.
Study Design
Assessment of proportions of HbA1c and LDL testing for people ascertained as diabetic from the respective sources. Qualitative interviews and review of EHRs of discrepant cases.
Data Collection/Extraction
Claims submitted to Rhode Island Medicaid by three practice sites in 2013; program‐coded EHR extraction; manual review of selected EHRs.
Principal Findings
Of 21,030 adult Medicaid beneficiaries attributed to a primary care patient at a site by claims or EHR data, concordance on assignment ranged from 0.30 to 0.41. Of patients with concordant assignment, the ratio of patients ascertained as diabetic by EHR versus claims ranged from 1.06 to 1.14. For patients with concordant assignment and diagnosis, the ratio based on EHR versus claims ranged from 1.08 to 18.34 for HbA1c testing, and from 1.29 to 14.18 for lipid testing. Manual record review of 264 patients discrepant on diagnosis or testing identified problems such as misuse of ICD‐9 codes, failure to submit claims, and others.
Conclusions
Claims data underestimate performance on these metrics compared to EHR documentation, by varying amounts. Use of claims data for these metrics is problematic.</description><identifier>ISSN: 0017-9124</identifier><identifier>EISSN: 1475-6773</identifier><identifier>DOI: 10.1111/1475-6773.12819</identifier><identifier>PMID: 29282723</identifier><language>eng</language><publisher>United States: Health Research and Educational Trust</publisher><subject><![CDATA[Analysis ; Assignment ; Beneficiaries ; claims data ; Data Accuracy ; Data collection ; Data Collection - methods ; Data Collection - standards ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus - therapy ; Diabetes therapy ; Diagnosis ; Diagnostic tests ; Discrepancies ; Documentation ; EHRs ; Electronic health records ; Electronic Health Records - standards ; Electronic Health Records - statistics & numerical data ; Electronic medical records ; Electronic records ; Extraction ; Glycated Hemoglobin A - analysis ; Glycosylated hemoglobin ; Government programs ; Health care ; Health Care Quality and Measurement ; Health promotion ; Humans ; Insurance Claim Review - standards ; Insurance Claim Review - statistics & numerical data ; Lipids - blood ; Low density lipoprotein ; Low density lipoproteins ; Medicaid ; Medicaid - standards ; Medicaid - statistics & numerical data ; Medical diagnosis ; Medical records ; Office Visits - statistics & numerical data ; Patients ; Primary care ; Primary Health Care - standards ; Primary Health Care - statistics & numerical data ; Qualitative analysis ; Quality Indicators, Health Care - standards ; Quality Indicators, Health Care - statistics & numerical data ; Quality management ; Quality measurement ; Quality of care ; Rhode Island ; United States]]></subject><ispartof>Health services research, 2018-08, Vol.53 (4), p.2988-3006</ispartof><rights>Health Research and Educational Trust</rights><rights>Health Research and Educational Trust.</rights><rights>COPYRIGHT 2018 Health Research and Educational Trust</rights><rights>COPYRIGHT 2018 Health Research and Educational Trust</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c7139-c4d8e02d023a8ebe77bac990dd7ff7ce546570d9a75c5ffcb2b7e2a6d98d372c3</citedby><cites>FETCH-LOGICAL-c7139-c4d8e02d023a8ebe77bac990dd7ff7ce546570d9a75c5ffcb2b7e2a6d98d372c3</cites><orcidid>0000-0002-0790-5879</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/PMC6056571/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056571/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,1411,27901,27902,30976,45550,45551,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29282723$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Laws, Michael Barton</creatorcontrib><creatorcontrib>Michaud, Joanne</creatorcontrib><creatorcontrib>Shield, Renee</creatorcontrib><creatorcontrib>McQuade, William</creatorcontrib><creatorcontrib>Wilson, Ira B.</creatorcontrib><title>Comparison of Electronic Health Record–Based and Claims‐Based Diabetes Care Quality Measures: Causes of Discrepancies</title><title>Health services research</title><addtitle>Health Serv Res</addtitle><description>Objective
To investigate magnitude and sources of discrepancy in quality metrics using claims versus electronic health record (EHR) data.
Study Design
Assessment of proportions of HbA1c and LDL testing for people ascertained as diabetic from the respective sources. Qualitative interviews and review of EHRs of discrepant cases.
Data Collection/Extraction
Claims submitted to Rhode Island Medicaid by three practice sites in 2013; program‐coded EHR extraction; manual review of selected EHRs.
Principal Findings
Of 21,030 adult Medicaid beneficiaries attributed to a primary care patient at a site by claims or EHR data, concordance on assignment ranged from 0.30 to 0.41. Of patients with concordant assignment, the ratio of patients ascertained as diabetic by EHR versus claims ranged from 1.06 to 1.14. For patients with concordant assignment and diagnosis, the ratio based on EHR versus claims ranged from 1.08 to 18.34 for HbA1c testing, and from 1.29 to 14.18 for lipid testing. Manual record review of 264 patients discrepant on diagnosis or testing identified problems such as misuse of ICD‐9 codes, failure to submit claims, and others.
Conclusions
Claims data underestimate performance on these metrics compared to EHR documentation, by varying amounts. Use of claims data for these metrics is problematic.</description><subject>Analysis</subject><subject>Assignment</subject><subject>Beneficiaries</subject><subject>claims data</subject><subject>Data Accuracy</subject><subject>Data collection</subject><subject>Data Collection - methods</subject><subject>Data Collection - standards</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus - therapy</subject><subject>Diabetes therapy</subject><subject>Diagnosis</subject><subject>Diagnostic tests</subject><subject>Discrepancies</subject><subject>Documentation</subject><subject>EHRs</subject><subject>Electronic health records</subject><subject>Electronic Health Records - standards</subject><subject>Electronic Health Records - statistics & numerical data</subject><subject>Electronic medical records</subject><subject>Electronic records</subject><subject>Extraction</subject><subject>Glycated Hemoglobin A - analysis</subject><subject>Glycosylated hemoglobin</subject><subject>Government programs</subject><subject>Health care</subject><subject>Health Care Quality and Measurement</subject><subject>Health promotion</subject><subject>Humans</subject><subject>Insurance Claim Review - standards</subject><subject>Insurance Claim Review - statistics & numerical data</subject><subject>Lipids - blood</subject><subject>Low density lipoprotein</subject><subject>Low density lipoproteins</subject><subject>Medicaid</subject><subject>Medicaid - standards</subject><subject>Medicaid - statistics & numerical data</subject><subject>Medical diagnosis</subject><subject>Medical records</subject><subject>Office Visits - statistics & numerical data</subject><subject>Patients</subject><subject>Primary care</subject><subject>Primary Health Care - standards</subject><subject>Primary Health Care - statistics & numerical data</subject><subject>Qualitative analysis</subject><subject>Quality Indicators, Health Care - standards</subject><subject>Quality Indicators, Health Care - statistics & numerical data</subject><subject>Quality management</subject><subject>Quality measurement</subject><subject>Quality of care</subject><subject>Rhode Island</subject><subject>United States</subject><issn>0017-9124</issn><issn>1475-6773</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>N95</sourceid><sourceid>7QJ</sourceid><recordid>eNqFk9-L1DAQx4so3nr67JsUBFGwe0n6I60Pwtlbb4WVw1OfQ5pOuznaZE1add_uTxD8D-8vMXtd160smkACk898M8nMeN5jjKbYjRMc0ThIKA2nmKQ4u-NNdpa73gQhTIMMk-jIe2DtFUIoDdPovndEMpISSsKJt851u-JGWq18XfmzBkRntJLCnwNvuqV_CUKb8ub65xtuofS5Kv284bK1N9c_BtOZ5AV0YP2cG_A_9LyR3dp_D9z2BuwrZ-6tO3XqZ9IKAyuuhAT70LtX8cbCo-1-7H1-O_uUz4PFxfm7_HQRCIrDLBBRmQIiJSIhT6EASgsusgyVJa0qKiCOkpiiMuM0FnFViYIUFAhPyiwtQ0pEeOy9HnRXfdFCKUB1hjdsZWTLzZppLtn4RMklq_VXlqDYSWMn8HwrYPSXHmzHWvcOaBquQPeW4SzFNIySLHLo07_QK90b5Z7HCKJhSmkWZn-omjfApKq0u1dsRNlpHGOSxIQkjgoOUDUocEFqBZV05hE_PcC7WUIrxUGHFyMHx3TwvatduixLzxf_CmbLCt00UANzCcsvxvyzPX55W0pWN30ntbJj8OUeWPRWKrBusbJednaIZYSfDLgw2loD1S6PGLFNR7BN_bNN_bPbjnAeT_bTv-N_t4ADkgH45v5n_T89Np99vByUfwGfHBRU</recordid><startdate>201808</startdate><enddate>201808</enddate><creator>Laws, Michael Barton</creator><creator>Michaud, Joanne</creator><creator>Shield, Renee</creator><creator>McQuade, William</creator><creator>Wilson, Ira B.</creator><general>Health Research and Educational Trust</general><general>Blackwell Publishing Ltd</general><general>John Wiley and Sons Inc</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>N95</scope><scope>XI7</scope><scope>8GL</scope><scope>7QJ</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-0790-5879</orcidid></search><sort><creationdate>201808</creationdate><title>Comparison of Electronic Health Record–Based and Claims‐Based Diabetes Care Quality Measures: Causes of Discrepancies</title><author>Laws, Michael Barton ; Michaud, Joanne ; Shield, Renee ; McQuade, William ; Wilson, Ira B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c7139-c4d8e02d023a8ebe77bac990dd7ff7ce546570d9a75c5ffcb2b7e2a6d98d372c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Analysis</topic><topic>Assignment</topic><topic>Beneficiaries</topic><topic>claims data</topic><topic>Data Accuracy</topic><topic>Data collection</topic><topic>Data Collection - methods</topic><topic>Data Collection - standards</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes Mellitus - therapy</topic><topic>Diabetes therapy</topic><topic>Diagnosis</topic><topic>Diagnostic tests</topic><topic>Discrepancies</topic><topic>Documentation</topic><topic>EHRs</topic><topic>Electronic health records</topic><topic>Electronic Health Records - standards</topic><topic>Electronic Health Records - statistics & numerical data</topic><topic>Electronic medical records</topic><topic>Electronic records</topic><topic>Extraction</topic><topic>Glycated Hemoglobin A - analysis</topic><topic>Glycosylated hemoglobin</topic><topic>Government programs</topic><topic>Health care</topic><topic>Health Care Quality and Measurement</topic><topic>Health promotion</topic><topic>Humans</topic><topic>Insurance Claim Review - standards</topic><topic>Insurance Claim Review - statistics & numerical data</topic><topic>Lipids - blood</topic><topic>Low density lipoprotein</topic><topic>Low density lipoproteins</topic><topic>Medicaid</topic><topic>Medicaid - standards</topic><topic>Medicaid - statistics & numerical data</topic><topic>Medical diagnosis</topic><topic>Medical records</topic><topic>Office Visits - statistics & numerical data</topic><topic>Patients</topic><topic>Primary care</topic><topic>Primary Health Care - standards</topic><topic>Primary Health Care - statistics & numerical data</topic><topic>Qualitative analysis</topic><topic>Quality Indicators, Health Care - standards</topic><topic>Quality Indicators, Health Care - statistics & numerical data</topic><topic>Quality management</topic><topic>Quality measurement</topic><topic>Quality of care</topic><topic>Rhode Island</topic><topic>United States</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Laws, Michael Barton</creatorcontrib><creatorcontrib>Michaud, Joanne</creatorcontrib><creatorcontrib>Shield, Renee</creatorcontrib><creatorcontrib>McQuade, William</creatorcontrib><creatorcontrib>Wilson, Ira B.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale Business Insights</collection><collection>Business Insights: Essentials</collection><collection>Gale In Context: High School</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Health services research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Laws, Michael Barton</au><au>Michaud, Joanne</au><au>Shield, Renee</au><au>McQuade, William</au><au>Wilson, Ira B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of Electronic Health Record–Based and Claims‐Based Diabetes Care Quality Measures: Causes of Discrepancies</atitle><jtitle>Health services research</jtitle><addtitle>Health Serv Res</addtitle><date>2018-08</date><risdate>2018</risdate><volume>53</volume><issue>4</issue><spage>2988</spage><epage>3006</epage><pages>2988-3006</pages><issn>0017-9124</issn><eissn>1475-6773</eissn><abstract>Objective
To investigate magnitude and sources of discrepancy in quality metrics using claims versus electronic health record (EHR) data.
Study Design
Assessment of proportions of HbA1c and LDL testing for people ascertained as diabetic from the respective sources. Qualitative interviews and review of EHRs of discrepant cases.
Data Collection/Extraction
Claims submitted to Rhode Island Medicaid by three practice sites in 2013; program‐coded EHR extraction; manual review of selected EHRs.
Principal Findings
Of 21,030 adult Medicaid beneficiaries attributed to a primary care patient at a site by claims or EHR data, concordance on assignment ranged from 0.30 to 0.41. Of patients with concordant assignment, the ratio of patients ascertained as diabetic by EHR versus claims ranged from 1.06 to 1.14. For patients with concordant assignment and diagnosis, the ratio based on EHR versus claims ranged from 1.08 to 18.34 for HbA1c testing, and from 1.29 to 14.18 for lipid testing. Manual record review of 264 patients discrepant on diagnosis or testing identified problems such as misuse of ICD‐9 codes, failure to submit claims, and others.
Conclusions
Claims data underestimate performance on these metrics compared to EHR documentation, by varying amounts. Use of claims data for these metrics is problematic.</abstract><cop>United States</cop><pub>Health Research and Educational Trust</pub><pmid>29282723</pmid><doi>10.1111/1475-6773.12819</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-0790-5879</orcidid><oa>free_for_read</oa></addata></record> |
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source | Applied Social Sciences Index & Abstracts (ASSIA); MEDLINE; Wiley Online Library All Journals; PubMed Central; Alma/SFX Local Collection; EZB Electronic Journals Library |
subjects | Analysis Assignment Beneficiaries claims data Data Accuracy Data collection Data Collection - methods Data Collection - standards Diabetes Diabetes mellitus Diabetes Mellitus - therapy Diabetes therapy Diagnosis Diagnostic tests Discrepancies Documentation EHRs Electronic health records Electronic Health Records - standards Electronic Health Records - statistics & numerical data Electronic medical records Electronic records Extraction Glycated Hemoglobin A - analysis Glycosylated hemoglobin Government programs Health care Health Care Quality and Measurement Health promotion Humans Insurance Claim Review - standards Insurance Claim Review - statistics & numerical data Lipids - blood Low density lipoprotein Low density lipoproteins Medicaid Medicaid - standards Medicaid - statistics & numerical data Medical diagnosis Medical records Office Visits - statistics & numerical data Patients Primary care Primary Health Care - standards Primary Health Care - statistics & numerical data Qualitative analysis Quality Indicators, Health Care - standards Quality Indicators, Health Care - statistics & numerical data Quality management Quality measurement Quality of care Rhode Island United States |
title | Comparison of Electronic Health Record–Based and Claims‐Based Diabetes Care Quality Measures: Causes of Discrepancies |
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