COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes. (13th August 2020)

• Record Type: Journal Article
• Authors: Dong, Xiao; Li, Jianfu; Soysal, Ekin; Bian, Jiang; DuVall, Scott L; Hanchrow, Elizabeth; Liu, Hongfang; Lynch, Kristine E; Matheny, Michael; Natarajan, Karthik; Ohno-Machado, Lucila; Pakhomov, Serguei; Reeves, Ruth Madeleine; Sitapati, Amy M; Abhyankar, Swapna; Cullen, Theresa; Deckard, Jami; Jiang, Xiaoqian; Murphy, Robert; Xu, Hua
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 9(2020)
• Page Start: 1437
• Page End: 1442
• Publication Date: 2020-08-13
• Subjects: COVID-19 -- natural language processing -- testing name normalization -- LOINC -- COVID-19 TestNorm; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa145
• Languages: English
• ISSNs: 1067-5027

Abstract

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.