This paper is in the following e-collection/theme issue:

Early Results from COVID-19 Studies (332) JMIR Theme Issue: COVID-19 Special Issue (2484) Electronic Health Records (1149) Infectious Diseases (non-STD/STI) (1646) Clinical Informatics (1126) Clinical Information and Decision Making (1599)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/39231, first published .
Accuracy of COVID-19–Like Illness Diagnoses in Electronic Health Record Data: Retrospective Cohort Study

Accuracy of COVID-19–Like Illness Diagnoses in Electronic Health Record Data: Retrospective Cohort Study

Accuracy of COVID-19–Like Illness Diagnoses in Electronic Health Record Data: Retrospective Cohort Study

Authors of this article:

Suchitra Rao1 Author Orcid Image ;   Catherine Bozio2 Author Orcid Image ;   Kristen Butterfield3 Author Orcid Image ;   Sue Reynolds2 Author Orcid Image ;   Sarah E Reese3 Author Orcid Image ;   Sarah Ball3 Author Orcid Image ;   Andrea Steffens2 Author Orcid Image ;   Maria Demarco3 Author Orcid Image ;   Charlene McEvoy4 Author Orcid Image ;   Mark Thompson2 Author Orcid Image ;   Elizabeth Rowley3 Author Orcid Image ;   Rachael M Porter2 Author Orcid Image ;   Rebecca V Fink3 Author Orcid Image ;   Stephanie A Irving5 Author Orcid Image ;   Allison Naleway5 Author Orcid Image
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doctick doctick @doctick

#ehr "Accuracy of COVID-19-Like-Illness Diagnoses in Electronic Health Record Data: Retrospective Cohort Study" https://t.co/gbkIYPB9Uc

12:48 AM · Nov 17, 2022

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JMIR Formative Res: Accuracy of #covid19–Like Illness Diagnoses in Electronic Health Record Data: Retrospective Coh… https://t.co/W4pscXyhIq

1:54 PM · Jan 17, 2023

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RT @jmirpub: JMIR Formative Res: Accuracy of #covid19–Like Illness Diagnoses in Electronic Health Record Data: Retrospective Cohort Study h…

1:55 PM · Jan 17, 2023

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