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

doi:10.1093/jamia/ocaa145

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

J Am Med Inform Assoc. 2020 Jul 1;27(9):1437-1442. doi: 10.1093/jamia/ocaa145.

Authors

Xiao Dong¹, Jianfu Li¹, Ekin Soysal¹, Jiang Bian², Scott L DuVall^{3

4}, Elizabeth Hanchrow^{5

6}, Hongfang Liu⁷, Kristine E Lynch^{3

4}, Michael Matheny^{5

6}, Karthik Natarajan^{8

9}, Lucila Ohno-Machado^{10

11}, Serguei Pakhomov¹², Ruth Madeleine Reeves^{5

6}, Amy M Sitapati^{10

13}, Swapna Abhyankar¹⁴, Theresa Cullen¹⁴, Jami Deckard¹⁴, Xiaoqian Jiang¹, Robert Murphy¹, Hua Xu¹

Affiliations

¹ School of Biomedical Informatics, University of Texas, Houston, Texas, USA.
² Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
³ VA Informatics and Computing Infrastructure, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.
⁴ Department of Internal Medicine Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, Utah, USA.
⁵ Tennessee Valley Healthcare System, Veterans Affairs Medical Center, Nashville, Tennessee, USA.
⁶ Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
⁷ Division of Digital Health Sciences, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA.
⁸ Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, New York, USA.
⁹ Medical Informatics Services, NewYork-Presbyterian Hospital, New York, New York, USA.
¹⁰ Department of Biomedical Informatics, UCSD Health, University of California, San Diego, La Jolla, California, USA.
¹¹ Division of Health Services Research and Development, Veterans Administration San Diego Healthcare System, La Jolla, California, USA.
¹² Department of Pharmaceutical Care and Health Systems, College of Pharmacy, University of Minnesota, Minneapolis, Minnesota, USA.
¹³ Division of General Internal Medicine, Department of Medicine, University of California, San Diego, La Jolla, California, USA.
¹⁴ LOINC and Health Data Standards, Regenstrief Institute, Indianapolis, Indiana, USA.

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: COVID-19; COVID-19 TestNorm; LOINC; natural language processing; testing name normalization.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

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

Abstract

Publication types

MeSH terms

Grants and funding