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Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic Abstract Objective This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019. Materials and Methods We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital’s COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. Results Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. Conclusion Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions. contact tracing, COVID-19, infection control, electronic health records, clinical informatics, information technology This content is only available as a PDF. © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of the American Medical Informatics Association Oxford University Press

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic

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Publisher
Oxford University Press
Copyright
Copyright © 2021 American Medical Informatics Association
ISSN
1067-5027
eISSN
1527-974X
DOI
10.1093/jamia/ocab231
Publisher site
See Article on Publisher Site

Abstract

Abstract Objective This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019. Materials and Methods We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital’s COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. Results Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. Conclusion Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions. contact tracing, COVID-19, infection control, electronic health records, clinical informatics, information technology This content is only available as a PDF. © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Journal of the American Medical Informatics AssociationOxford University Press

Published: Oct 8, 2021

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