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

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 journal › Article › peer-review

12 Scopus citations

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 language	English (US)
Pages (from-to)	1437-1442
Number of pages	6
Journal	Journal of the American Medical Informatics Association
Volume	27
Issue number	9
DOIs	https://doi.org/10.1093/jamia/ocaa145
State	Published - Sep 1 2020

Bibliographical note

Publisher Copyright:
© The Author(s) 2020.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

COVID-19
COVID-19 TestNorm
LOINC
Natural language processing
Testing name normalization

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10.1093/jamia/ocaa145

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Dong, X., Li, J., Soysal, E., Bian, J., DuVall, S. L., Hanchrow, E., Liu, H., Lynch, K. E., Matheny, M., Natarajan, K., Ohno-Machado, L., Pakhomov, S., Reeves, R. M., Sitapati, A. M., Abhyankar, S., Cullen, T., Deckard, J., Jiang, X., Murphy, R., & Xu, H. (2020). COVID-19 testnorm: A tool to normalize COVID-19 testing names to LOINC codes. Journal of the American Medical Informatics Association, 27(9), 1437-1442. https://doi.org/10.1093/jamia/ocaa145

Dong, X, Li, J, Soysal, E, Bian, J, DuVall, SL, Hanchrow, E, Liu, H, Lynch, KE, Matheny, M, Natarajan, K, Ohno-Machado, L, Pakhomov, S, Reeves, RM, Sitapati, AM, Abhyankar, S, Cullen, T, Deckard, J, Jiang, X, Murphy, R & Xu, H 2020, 'COVID-19 testnorm: A tool to normalize COVID-19 testing names to LOINC codes', Journal of the American Medical Informatics Association, vol. 27, no. 9, pp. 1437-1442. https://doi.org/10.1093/jamia/ocaa145

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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.",

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

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AU - Bian, Jiang

AU - DuVall, Scott L.

AU - Hanchrow, Elizabeth

AU - Liu, Hongfang

AU - Lynch, Kristine E.

AU - Matheny, Michael

AU - Natarajan, Karthik

AU - Ohno-Machado, Lucila

AU - Pakhomov, Serguei

AU - Reeves, Ruth Madeleine

AU - Sitapati, Amy M.

AU - Abhyankar, Swapna

AU - Cullen, Theresa

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AU - Jiang, Xiaoqian

AU - Murphy, Robert

AU - Xu, Hua

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N2 - 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.

AB - 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.

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

Abstract

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