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

Early Results from COVID-19 Studies (299) JMIR Theme Issue: COVID-19 Special Issue (2453) Electronic Health Records (1058) Infectious Diseases (non-STD/STI) (1576) Clinical Informatics (1028) Clinical Information and Decision Making (1411)

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
Article Authors Cited by (2) Tweetations (3) Metrics

Journals

  1. Moura C, Neville A, Liao F, Wen B, Razak F, Roberts S, Verma A, Bernatsky S. Validity of hospital diagnostic codes to identify SARS-CoV-2 infections in reference to polymerase chain reaction results: a descriptive study. CMAJ Open 2023;11(5):E982 View
  2. Moura C, Morrison L, Hohl C, Grant L, Pilote L, Neville A, Hau J, Bernatsky S. Administrative data ICD-10 diagnostic codes identifies most lab-confirmed SARS-CoV-2 admissions but misses many discharged from the Emergency Department. Scientific Reports 2024;14(1) View