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Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review
Objective We identified the methods used and determined the roles of electronic health records (EHRs) in detecting and assessing adverse drug events (ADEs) in the ambulatory setting. Methods We performed a systematic literature review by searching PubMed and Google Scholar for studies on ADEs dete...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2019
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382497/ https://www.ncbi.nlm.nih.gov/pubmed/30786301 http://dx.doi.org/10.1055/s-0039-1677738 |
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author | Feng, Chenchen Le, David McCoy, Allison B. |
author_facet | Feng, Chenchen Le, David McCoy, Allison B. |
author_sort | Feng, Chenchen |
collection | PubMed |
description | Objective We identified the methods used and determined the roles of electronic health records (EHRs) in detecting and assessing adverse drug events (ADEs) in the ambulatory setting. Methods We performed a systematic literature review by searching PubMed and Google Scholar for studies on ADEs detected in the ambulatory setting involving any EHR use published before June 2017. We extracted study characteristics from included studies related to ADE detection methods for analysis. Results We identified 30 studies that evaluated ADEs in an ambulatory setting with an EHR. In 27 studies, EHRs were used only as the data source for ADE identification. In two studies, the EHR was used as both a data source and to deliver decision support to providers during order entry. In one study, the EHR was a source of data and generated patient safety reports that researchers used in the process of identifying ADEs. Methods of identification included manual chart review by trained nurses, pharmacists, and/or physicians; prescription review; computer monitors; electronic triggers; International Classification of Diseases codes; natural language processing of clinical notes; and patient phone calls and surveys. Seven studies provided examples of search phrases, laboratory values, and rules used to identify ADEs. Conclusion The majority of studies examined used EHRs as sources of data for ADE detection. This retrospective approach is appropriate to measure incidence rates of ADEs but not adequate to detect preventable ADEs before patient harm occurs. New methods involving computer monitors and electronic triggers will enable researchers to catch preventable ADEs and take corrective action. |
format | Online Article Text |
id | pubmed-6382497 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-63824972020-01-01 Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review Feng, Chenchen Le, David McCoy, Allison B. Appl Clin Inform Objective We identified the methods used and determined the roles of electronic health records (EHRs) in detecting and assessing adverse drug events (ADEs) in the ambulatory setting. Methods We performed a systematic literature review by searching PubMed and Google Scholar for studies on ADEs detected in the ambulatory setting involving any EHR use published before June 2017. We extracted study characteristics from included studies related to ADE detection methods for analysis. Results We identified 30 studies that evaluated ADEs in an ambulatory setting with an EHR. In 27 studies, EHRs were used only as the data source for ADE identification. In two studies, the EHR was used as both a data source and to deliver decision support to providers during order entry. In one study, the EHR was a source of data and generated patient safety reports that researchers used in the process of identifying ADEs. Methods of identification included manual chart review by trained nurses, pharmacists, and/or physicians; prescription review; computer monitors; electronic triggers; International Classification of Diseases codes; natural language processing of clinical notes; and patient phone calls and surveys. Seven studies provided examples of search phrases, laboratory values, and rules used to identify ADEs. Conclusion The majority of studies examined used EHRs as sources of data for ADE detection. This retrospective approach is appropriate to measure incidence rates of ADEs but not adequate to detect preventable ADEs before patient harm occurs. New methods involving computer monitors and electronic triggers will enable researchers to catch preventable ADEs and take corrective action. Georg Thieme Verlag KG 2019-01 2019-02-20 /pmc/articles/PMC6382497/ /pubmed/30786301 http://dx.doi.org/10.1055/s-0039-1677738 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited. |
spellingShingle | Feng, Chenchen Le, David McCoy, Allison B. Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review |
title | Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review |
title_full | Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review |
title_fullStr | Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review |
title_full_unstemmed | Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review |
title_short | Using Electronic Health Records to Identify Adverse Drug Events in Ambulatory Care: A Systematic Review |
title_sort | using electronic health records to identify adverse drug events in ambulatory care: a systematic review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6382497/ https://www.ncbi.nlm.nih.gov/pubmed/30786301 http://dx.doi.org/10.1055/s-0039-1677738 |
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