Faculty, Staff and Student Publications

Language

English

Publication Date

7-1-2020

Journal

Journal of the American Medical Informatics Association

DOI

10.1093/jamia/ocaa145

PMID

32569358

PMCID

PMC7337837

PubMedCentral® Posted Date

July 2020

PubMedCentral® Full Text Version

Post-Print

Abstract

Large observational data networks that leverage routine clinical practice data in electronic health records (EHRs) are critical resources for research on coronavirus disease 2019 (COVID-19). Data normalization is a key challenge for the secondary use of EHRs for COVID-19 research across institutions. In this study, we addressed the challenge of automating the normalization of COVID-19 diagnostic tests, which are critical data elements, but for which controlled terminology terms were published after clinical implementation. We developed a simple but effective rule-based tool called COVID-19 TestNorm to automatically normalize local COVID-19 testing names to standard LOINC (Logical Observation Identifiers Names and Codes) codes. COVID-19 TestNorm was developed and evaluated using 568 test names collected from 8 healthcare systems. Our results show that it could achieve an accuracy of 97.4% on an independent test set. COVID-19 TestNorm is available as an open-source package for developers and as an online Web application for end users (https://clamp.uth.edu/covid/loinc.php). We believe that it will be a useful tool to support secondary use of EHRs for research on COVID-19.

Keywords

Betacoronavirus, COVID-19, COVID-19 Testing, Clinical Laboratory Techniques, Coronavirus Infections, Electronic Health Records, Humans, Logical Observation Identifiers Names and Codes, Pandemics, Pneumonia, Viral, SARS-CoV-2, Terminology as Topic

Published Open-Access

yes

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