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Concordance Between Common Hypertension Control Algorithms in Electronic Medical Record Data

Because quality improvement metrics and treatment guidelines are used to conduct research, evaluate care quality, and assess population health, they should, ideally, align. We used electronic medical record data to analyze variation between blood pressure control estimates calculated by using thresh...

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Detalles Bibliográficos
Autores principales: Nielsen, Victoria M., Bettano, Amy, Josephson, Mark, Nasuti, Laura, Ursprung, W.W. Sanouri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Centers for Disease Control and Prevention 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609492/
https://www.ncbi.nlm.nih.gov/pubmed/28910594
http://dx.doi.org/10.5888/pcd14.170032
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author Nielsen, Victoria M.
Bettano, Amy
Josephson, Mark
Nasuti, Laura
Ursprung, W.W. Sanouri
author_facet Nielsen, Victoria M.
Bettano, Amy
Josephson, Mark
Nasuti, Laura
Ursprung, W.W. Sanouri
author_sort Nielsen, Victoria M.
collection PubMed
description Because quality improvement metrics and treatment guidelines are used to conduct research, evaluate care quality, and assess population health, they should, ideally, align. We used electronic medical record data to analyze variation between blood pressure control estimates calculated by using thresholds derived from National Quality Forum 0018 (NQF 0018) and Joint National Committee (JNC) treatment guidelines in a cohort of patients with hypertension. Percentage of patients with controlled blood pressure derived from each quality improvement or treatment guideline cutoff varied up to 16.1 percentage points. This variance demonstrates that discrepancies in blood pressure thresholds produce considerable variation in estimates; thus, treatment guidance and metrics should be selected carefully.
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spelling pubmed-56094922017-10-03 Concordance Between Common Hypertension Control Algorithms in Electronic Medical Record Data Nielsen, Victoria M. Bettano, Amy Josephson, Mark Nasuti, Laura Ursprung, W.W. Sanouri Prev Chronic Dis Brief Because quality improvement metrics and treatment guidelines are used to conduct research, evaluate care quality, and assess population health, they should, ideally, align. We used electronic medical record data to analyze variation between blood pressure control estimates calculated by using thresholds derived from National Quality Forum 0018 (NQF 0018) and Joint National Committee (JNC) treatment guidelines in a cohort of patients with hypertension. Percentage of patients with controlled blood pressure derived from each quality improvement or treatment guideline cutoff varied up to 16.1 percentage points. This variance demonstrates that discrepancies in blood pressure thresholds produce considerable variation in estimates; thus, treatment guidance and metrics should be selected carefully. Centers for Disease Control and Prevention 2017-09-14 /pmc/articles/PMC5609492/ /pubmed/28910594 http://dx.doi.org/10.5888/pcd14.170032 Text en https://creativecommons.org/licenses/by/4.0/This is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.
spellingShingle Brief
Nielsen, Victoria M.
Bettano, Amy
Josephson, Mark
Nasuti, Laura
Ursprung, W.W. Sanouri
Concordance Between Common Hypertension Control Algorithms in Electronic Medical Record Data
title Concordance Between Common Hypertension Control Algorithms in Electronic Medical Record Data
title_full Concordance Between Common Hypertension Control Algorithms in Electronic Medical Record Data
title_fullStr Concordance Between Common Hypertension Control Algorithms in Electronic Medical Record Data
title_full_unstemmed Concordance Between Common Hypertension Control Algorithms in Electronic Medical Record Data
title_short Concordance Between Common Hypertension Control Algorithms in Electronic Medical Record Data
title_sort concordance between common hypertension control algorithms in electronic medical record data
topic Brief
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5609492/
https://www.ncbi.nlm.nih.gov/pubmed/28910594
http://dx.doi.org/10.5888/pcd14.170032
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