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Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response
Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understandin...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892315/ https://www.ncbi.nlm.nih.gov/pubmed/33610878 http://dx.doi.org/10.1016/j.jbi.2021.103715 |
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author | Sudat, Sylvia E.K. Robinson, Sarah C. Mudiganti, Satish Mani, Aravind Pressman, Alice R. |
author_facet | Sudat, Sylvia E.K. Robinson, Sarah C. Mudiganti, Satish Mani, Aravind Pressman, Alice R. |
author_sort | Sudat, Sylvia E.K. |
collection | PubMed |
description | Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response. |
format | Online Article Text |
id | pubmed-7892315 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78923152021-02-19 Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response Sudat, Sylvia E.K. Robinson, Sarah C. Mudiganti, Satish Mani, Aravind Pressman, Alice R. J Biomed Inform Commentary Data quality is essential to the success of the most simple and the most complex analysis. In the context of the COVID-19 pandemic, large-scale data sharing across the US and around the world has played an important role in public health responses to the pandemic and has been crucial to understanding and predicting its likely course. In California, hospitals have been required to report a large volume of daily data related to COVID-19. In order to meet this need, electronic health records (EHRs) have played an important role, but the challenges of reporting high-quality data in real-time from EHR data sources have not been explored. We describe some of the challenges of utilizing EHR data for this purpose from the perspective of a large, integrated, mixed-payer health system in northern California, US. We emphasize some of the inadequacies inherent to EHR data using several specific examples, and explore the clinical-analytic gap that forms the basis for some of these inadequacies. We highlight the need for data and analytics to be incorporated into the early stages of clinical crisis planning in order to utilize EHR data to full advantage. We further propose that lessons learned from the COVID-19 pandemic can result in the formation of collaborative teams joining clinical operations, informatics, data analytics, and research, ultimately resulting in improved data quality to support effective crisis response. Elsevier Inc. 2021-04 2021-02-19 /pmc/articles/PMC7892315/ /pubmed/33610878 http://dx.doi.org/10.1016/j.jbi.2021.103715 Text en © 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Commentary Sudat, Sylvia E.K. Robinson, Sarah C. Mudiganti, Satish Mani, Aravind Pressman, Alice R. Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response |
title | Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response |
title_full | Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response |
title_fullStr | Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response |
title_full_unstemmed | Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response |
title_short | Mind the clinical-analytic gap: Electronic health records and COVID-19 pandemic response |
title_sort | mind the clinical-analytic gap: electronic health records and covid-19 pandemic response |
topic | Commentary |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7892315/ https://www.ncbi.nlm.nih.gov/pubmed/33610878 http://dx.doi.org/10.1016/j.jbi.2021.103715 |
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