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Abhyankar, Swapna

https://orcid.org/0000-0002-6475-5344

Mayo Clinic

Regenstrief Institute

Citations 1

COVID-19 TestNorm: A tool to normalize COVID-19 testing names to LOINC codes
Dong, Xiao , Li, Jianfu , Soysal, Ekin , Bian, Jiang , DuVall, Scott L. , Hanchrow, Elizabeth, Lynch, Kristine E. , Matheny, Michael E. , Ohno‐Machado, Lucila , Pakhomov, Serguei , Reeves, Ruth , Sitapati, Amy M. , Abhyankar, Swapna , Deckard, Jami, Murphy, Robert P. , Xu, Hua , Bian, Jiang , Matheny, Michael , Reeves, Ruth M , Liu, Hongfang , Natarajan, Karthik , Cullen, Theresa , Jiang, Xiaoqian , Murphy, Robert

Journal of the American Medical Informatics Association | June 17, 2020 | Volume: 27 | Issue: 9 | Pages: 1437-1442

10.1093/jamia/ocaa145 Get It

AbstractLarge 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.