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Persistent Cardiometabolic Health Gaps: Can Therapeutic Care Gaps Be Precisely Identified from Electronic Health Records
The objective of this study was to determine the strengths and limitations of using structured electronic health records (EHR) to identify and manage cardiometabolic (CM) health gaps. We used medication adherence measures derived from dispense data to attribute related therapeutic care gaps (i.e., n...
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Published in: | Healthcare (Basel) 2021-12, Vol.10 (1), p.70 |
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creator | Yan, Xiaowei Stewart, Walter F Husby, Hannah Delatorre-Reimer, Jake Mudiganti, Satish Refai, Farah Hudnut, Andrew Knobel, Kevin MacDonald, Karen Sifakis, Frangiscos Jones, James B |
description | The objective of this study was to determine the strengths and limitations of using structured electronic health records (EHR) to identify and manage cardiometabolic (CM) health gaps. We used medication adherence measures derived from dispense data to attribute related therapeutic care gaps (i.e., no action to close health gaps) to patient- (i.e., failure to retrieve medication or low adherence) or clinician-related (i.e., failure to initiate/titrate medication) behavior. We illustrated how such data can be used to manage health and care gaps for blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), and HbA1c for 240,582 Sutter Health primary care patients. Prevalence of health gaps was 44% for patients with hypertension, 33% with hyperlipidemia, and 57% with diabetes. Failure to retrieve medication was common; this patient-related care gap was highly associated with health gaps (odds ratios (OR): 1.23-1.76). Clinician-related therapeutic care gaps were common (16% for hypertension, and 40% and 27% for hyperlipidemia and diabetes, respectively), and strongly related to health gaps for hyperlipidemia (OR = 5.8; 95% CI: 5.6-6.0) and diabetes (OR = 5.7; 95% CI: 5.4-6.0). Additionally, a substantial minority of care gaps (9% to 21%) were uncertain, meaning we lacked evidence to attribute the gap to either patients or clinicians, hindering efforts to close the gaps. |
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We used medication adherence measures derived from dispense data to attribute related therapeutic care gaps (i.e., no action to close health gaps) to patient- (i.e., failure to retrieve medication or low adherence) or clinician-related (i.e., failure to initiate/titrate medication) behavior. We illustrated how such data can be used to manage health and care gaps for blood pressure (BP), low-density lipoprotein cholesterol (LDL-C), and HbA1c for 240,582 Sutter Health primary care patients. Prevalence of health gaps was 44% for patients with hypertension, 33% with hyperlipidemia, and 57% with diabetes. Failure to retrieve medication was common; this patient-related care gap was highly associated with health gaps (odds ratios (OR): 1.23-1.76). Clinician-related therapeutic care gaps were common (16% for hypertension, and 40% and 27% for hyperlipidemia and diabetes, respectively), and strongly related to health gaps for hyperlipidemia (OR = 5.8; 95% CI: 5.6-6.0) and diabetes (OR = 5.7; 95% CI: 5.4-6.0). Additionally, a substantial minority of care gaps (9% to 21%) were uncertain, meaning we lacked evidence to attribute the gap to either patients or clinicians, hindering efforts to close the gaps.</description><identifier>ISSN: 2227-9032</identifier><identifier>EISSN: 2227-9032</identifier><identifier>DOI: 10.3390/healthcare10010070</identifier><identifier>PMID: 35052233</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Blood pressure ; cardiometabolic conditions ; Cardiovascular disease ; care gaps ; Clinical medicine ; Diabetes ; electronic health record (EHR) ; Electronic health records ; health gaps ; Hypertension ; Identification ; Laboratories ; medication adherence ; Patient compliance ; Pharmacy ; Primary care</subject><ispartof>Healthcare (Basel), 2021-12, Vol.10 (1), p.70</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c496t-6ce954624762144e575b5df31e616f1bb4e1877aa91ad4f32acc16309990d3ab3</citedby><cites>FETCH-LOGICAL-c496t-6ce954624762144e575b5df31e616f1bb4e1877aa91ad4f32acc16309990d3ab3</cites><orcidid>0000-0001-8809-5582</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2621298817/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2621298817?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25752,27923,27924,37011,37012,44589,53790,53792,74997</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35052233$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Yan, Xiaowei</creatorcontrib><creatorcontrib>Stewart, Walter F</creatorcontrib><creatorcontrib>Husby, Hannah</creatorcontrib><creatorcontrib>Delatorre-Reimer, Jake</creatorcontrib><creatorcontrib>Mudiganti, Satish</creatorcontrib><creatorcontrib>Refai, Farah</creatorcontrib><creatorcontrib>Hudnut, Andrew</creatorcontrib><creatorcontrib>Knobel, Kevin</creatorcontrib><creatorcontrib>MacDonald, Karen</creatorcontrib><creatorcontrib>Sifakis, Frangiscos</creatorcontrib><creatorcontrib>Jones, James B</creatorcontrib><title>Persistent Cardiometabolic Health Gaps: Can Therapeutic Care Gaps Be Precisely Identified from Electronic Health Records</title><title>Healthcare (Basel)</title><addtitle>Healthcare (Basel)</addtitle><description>The objective of this study was to determine the strengths and limitations of using structured electronic health records (EHR) to identify and manage cardiometabolic (CM) health gaps. 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Clinician-related therapeutic care gaps were common (16% for hypertension, and 40% and 27% for hyperlipidemia and diabetes, respectively), and strongly related to health gaps for hyperlipidemia (OR = 5.8; 95% CI: 5.6-6.0) and diabetes (OR = 5.7; 95% CI: 5.4-6.0). Additionally, a substantial minority of care gaps (9% to 21%) were uncertain, meaning we lacked evidence to attribute the gap to either patients or clinicians, hindering efforts to close the gaps.</description><subject>Blood pressure</subject><subject>cardiometabolic conditions</subject><subject>Cardiovascular disease</subject><subject>care gaps</subject><subject>Clinical medicine</subject><subject>Diabetes</subject><subject>electronic health record (EHR)</subject><subject>Electronic health records</subject><subject>health gaps</subject><subject>Hypertension</subject><subject>Identification</subject><subject>Laboratories</subject><subject>medication adherence</subject><subject>Patient compliance</subject><subject>Pharmacy</subject><subject>Primary care</subject><issn>2227-9032</issn><issn>2227-9032</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNplkl1rFDEUhoMottT-AS9kwBtvVvM1mcQLwS61XShYpF6HfJzpZslM1mRG7L83u1trqyGQkPc9z8lJDkKvCX7PmMIf1mDitHYmA8G4zg4_Q8eU0m6hMKPPH-2P0GkpG1yHIkyy9iU6Yi1uKWXsGP26hlxCmWCcmqXJPqQBJmNTDK653KdoLsy2fKzi2NysIZstzFMVqxn2UnMGzXUGFwrEu2blKyn0AXzT5zQ05xHclNP4F_cNXMq-vEIvehMLnN6vJ-j7l_Ob5eXi6uvFavn5auG4EtNCOFAtF5R3ghLOoe1a2_qeERBE9MRaDkR2nTGKGM97Ro1zRDCslMKeGctO0OrA9cls9DaHweQ7nUzQ-4OUb7XJtaAI2hPMpMLYEl7zMWKFcLan4KizlHFZWZ8OrO1sB_CulppNfAJ9qoxhrW_TT11v2ErZVcC7e0BOP2Yokx5CcRCjGSHNRVNRf00KjlW1vv3HuklzHutT7VyEKinJDkgPLpdTKRn6h8sQrHd9ov_vkxr05nEZDyF_uoL9BrPEuzg</recordid><startdate>20211231</startdate><enddate>20211231</enddate><creator>Yan, Xiaowei</creator><creator>Stewart, Walter F</creator><creator>Husby, Hannah</creator><creator>Delatorre-Reimer, Jake</creator><creator>Mudiganti, Satish</creator><creator>Refai, Farah</creator><creator>Hudnut, Andrew</creator><creator>Knobel, Kevin</creator><creator>MacDonald, Karen</creator><creator>Sifakis, Frangiscos</creator><creator>Jones, James B</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7XB</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>KB0</scope><scope>M2O</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-8809-5582</orcidid></search><sort><creationdate>20211231</creationdate><title>Persistent Cardiometabolic Health Gaps: Can Therapeutic Care Gaps Be Precisely Identified from Electronic Health Records</title><author>Yan, Xiaowei ; 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Clinician-related therapeutic care gaps were common (16% for hypertension, and 40% and 27% for hyperlipidemia and diabetes, respectively), and strongly related to health gaps for hyperlipidemia (OR = 5.8; 95% CI: 5.6-6.0) and diabetes (OR = 5.7; 95% CI: 5.4-6.0). Additionally, a substantial minority of care gaps (9% to 21%) were uncertain, meaning we lacked evidence to attribute the gap to either patients or clinicians, hindering efforts to close the gaps.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>35052233</pmid><doi>10.3390/healthcare10010070</doi><orcidid>https://orcid.org/0000-0001-8809-5582</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Blood pressure cardiometabolic conditions Cardiovascular disease care gaps Clinical medicine Diabetes electronic health record (EHR) Electronic health records health gaps Hypertension Identification Laboratories medication adherence Patient compliance Pharmacy Primary care |
title | Persistent Cardiometabolic Health Gaps: Can Therapeutic Care Gaps Be Precisely Identified from Electronic Health Records |
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