Using EHR data to identify coronavirus infections in hospitalized patients: Impact of case definitions on disease surveillance

Ann Marie Navar, Irene Cosmatos, Stacey Purinton, Janet L. Ramsey, Robert J. Taylor, Rachel E. Sobel, Ginger Barlow, Gretchen S. Dieck, Michael L. Bulgrein, Eric D. Peterson

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: To evaluate the number, characteristics, and outcomes of patients identified hospitalized with coronavirus disease 2019 (COVID-19) using two different case definitions. Procedures: Electronic Health Record data were evaluated from patients hospitalized with COVID-19 through May 2020 at 52 health systems across the United States. Characteristics of inpatients with positive laboratory tests for SARS-CoV-2 were compared with those with clinical diagnosis of COVID-19 but without a confirmatory lab result. Findings: Of 14,371 inpatients with COVID-19, 6623 (46.1 %) had a positive laboratory result, and n = 7748 (52.9 %) had only a clinical diagnosis of COVID-19. Compared with clinically diagnosed cases, those with laboratory-confirmed COVID were similar in age and sex, but differed by race, ethnicity, and insurance status. Laboratory-confirmed cases were more likely to receive certain COVID-19 therapies including hydroxychloroquine, anti-IL6 agents and antivirals (p < 0.001). Those with laboratory–confirmed COVID-19 had lower rates of most complications such as myocardial infarction, but higher overall mortality (p < 0.001). Conclusion: We observed a two–fold difference in the number of patients hospitalized with COVID-19 depending on whether the case definition required laboratory confirmation. Variations in case definitions also led to differences in cohort characteristics, treatments, and outcomes.

Original languageEnglish (US)
Article number104842
JournalInternational Journal of Medical Informatics
Volume166
DOIs
StatePublished - Oct 2022

Keywords

  • Case definition
  • COVID-19
  • Electronic health records

ASJC Scopus subject areas

  • Health Informatics

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