COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes

Xiao Dong, Jianfu Li, Ekin Soysal, Jiang Bian, Scott L. DuVall, Elizabeth Hanchrow, Hongfang Liu, Kristine E. Lynch, Michael Matheny, Karthik Natarajan, Lucila Ohno-Machado, Serguei Pakhomov, Ruth Madeleine Reeves, Amy M. Sitapati, Swapna Abhyankar, Theresa Cullen, Jami Deckard, Xiaoqian Jiang, Robert Murphy, Hua Xu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1437-1442
Number of pages6
JournalJournal of the American Medical Informatics Association : JAMIA
Volume27
Issue number9
DOIs
StatePublished - Jul 1 2020

Keywords

  • COVID-19
  • COVID-19 TestNorm
  • LOINC
  • natural language processing
  • testing name normalization

ASJC Scopus subject areas

  • Health Informatics

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