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Monitoring prescribing patterns using regression and electronic health records

BACKGROUND: It is beneficial for health care institutions to monitor physician prescribing patterns to ensure that high-quality and cost-effective care is being provided to patients. However, detecting treatment patterns within an institution is challenging, given that medications and conditions are...

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Autores principales: Backenroth, Daniel, Chase, Herbert S., Wei, Ying, Friedman, Carol
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737913/
https://www.ncbi.nlm.nih.gov/pubmed/29258594
http://dx.doi.org/10.1186/s12911-017-0575-5
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author Backenroth, Daniel
Chase, Herbert S.
Wei, Ying
Friedman, Carol
author_facet Backenroth, Daniel
Chase, Herbert S.
Wei, Ying
Friedman, Carol
author_sort Backenroth, Daniel
collection PubMed
description BACKGROUND: It is beneficial for health care institutions to monitor physician prescribing patterns to ensure that high-quality and cost-effective care is being provided to patients. However, detecting treatment patterns within an institution is challenging, given that medications and conditions are often not explicitly linked in the health record. Here we demonstrate the use of statistical methods together with data from the electronic health care record (EHR) to analyze prescribing patterns at an institution. METHODS: As a demonstration of our method, which is based on regression, we collect EHR data from outpatient notes and use a case/control study design to determine the medications that are associated with hypertension. We also use regression to determine which conditions are associated with a preferential use of one or more classes of hypertension agents. Finally, we compare our method to methods based on tabulation. RESULTS: Our results show that regression methods provide more reasonable and useful results than tabulation, and successfully distinguish between medications that treat hypertension and medications that do not. These methods also provide insight into in which circumstances certain drugs are preferred over others. CONCLUSIONS: Our method can be used by health care institutions to monitor physician prescribing patterns and ensure the appropriateness of treatment.
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spelling pubmed-57379132017-12-21 Monitoring prescribing patterns using regression and electronic health records Backenroth, Daniel Chase, Herbert S. Wei, Ying Friedman, Carol BMC Med Inform Decis Mak Technical Advance BACKGROUND: It is beneficial for health care institutions to monitor physician prescribing patterns to ensure that high-quality and cost-effective care is being provided to patients. However, detecting treatment patterns within an institution is challenging, given that medications and conditions are often not explicitly linked in the health record. Here we demonstrate the use of statistical methods together with data from the electronic health care record (EHR) to analyze prescribing patterns at an institution. METHODS: As a demonstration of our method, which is based on regression, we collect EHR data from outpatient notes and use a case/control study design to determine the medications that are associated with hypertension. We also use regression to determine which conditions are associated with a preferential use of one or more classes of hypertension agents. Finally, we compare our method to methods based on tabulation. RESULTS: Our results show that regression methods provide more reasonable and useful results than tabulation, and successfully distinguish between medications that treat hypertension and medications that do not. These methods also provide insight into in which circumstances certain drugs are preferred over others. CONCLUSIONS: Our method can be used by health care institutions to monitor physician prescribing patterns and ensure the appropriateness of treatment. BioMed Central 2017-12-19 /pmc/articles/PMC5737913/ /pubmed/29258594 http://dx.doi.org/10.1186/s12911-017-0575-5 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Backenroth, Daniel
Chase, Herbert S.
Wei, Ying
Friedman, Carol
Monitoring prescribing patterns using regression and electronic health records
title Monitoring prescribing patterns using regression and electronic health records
title_full Monitoring prescribing patterns using regression and electronic health records
title_fullStr Monitoring prescribing patterns using regression and electronic health records
title_full_unstemmed Monitoring prescribing patterns using regression and electronic health records
title_short Monitoring prescribing patterns using regression and electronic health records
title_sort monitoring prescribing patterns using regression and electronic health records
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5737913/
https://www.ncbi.nlm.nih.gov/pubmed/29258594
http://dx.doi.org/10.1186/s12911-017-0575-5
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