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

5 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

Funding Information:
This project is partially supported by National Center for Advancing Translational Sciences grant nos. UL1TR003167 (UTHealth CTSA, HX, and RM) and 5U01TR002062 (HL, XJ, SP, HX, and KN); National Cancer Institute grant no. U24 CA194215 (HX and HL); National Institute of Health grant nos. R01AG066749 (XJ), R01GM114612 (XJ) and U01TR002062 (XJ); Veterans Affairs Health Services Research RES 13-457 (MM); Cancer Prevention and Research Institute of Texas grant nos. RP170668 (HX), RR180012 (XJ) and RP160015 (XD); the National Science Foundation RAPID grant no. 2027790 (XJ); and Gordon and Betty Moore Foundation grant no. 9639 (LO-M and HX). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Cancer Prevention and Research Institute of Texas, the Department of Veterans Affairs, or the U.S. government.

Publisher Copyright:
© The Author(s) 2020.

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

@article{42b6b454b34d454f905c7e7fc1e919dd,

title = "COVID-19 testnorm: A tool to normalize COVID-19 testing names to LOINC codes",

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",

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",

note = "Funding Information: This project is partially supported by National Center for Advancing Translational Sciences grant nos. UL1TR003167 (UTHealth CTSA, HX, and RM) and 5U01TR002062 (HL, XJ, SP, HX, and KN); National Cancer Institute grant no. U24 CA194215 (HX and HL); National Institute of Health grant nos. R01AG066749 (XJ), R01GM114612 (XJ) and U01TR002062 (XJ); Veterans Affairs Health Services Research RES 13-457 (MM); Cancer Prevention and Research Institute of Texas grant nos. RP170668 (HX), RR180012 (XJ) and RP160015 (XD); the National Science Foundation RAPID grant no. 2027790 (XJ); and Gordon and Betty Moore Foundation grant no. 9639 (LO-M and HX). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Cancer Prevention and Research Institute of Texas, the Department of Veterans Affairs, or the U.S. government. Publisher Copyright: {\textcopyright} The Author(s) 2020.",

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doi = "10.1093/jamia/ocaa145",

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AU - Dong, Xiao

AU - Li, Jianfu

AU - Soysal, Ekin

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

AU - Deckard, Jami

AU - Jiang, Xiaoqian

AU - Murphy, Robert

AU - Xu, Hua

N1 - Funding Information: This project is partially supported by National Center for Advancing Translational Sciences grant nos. UL1TR003167 (UTHealth CTSA, HX, and RM) and 5U01TR002062 (HL, XJ, SP, HX, and KN); National Cancer Institute grant no. U24 CA194215 (HX and HL); National Institute of Health grant nos. R01AG066749 (XJ), R01GM114612 (XJ) and U01TR002062 (XJ); Veterans Affairs Health Services Research RES 13-457 (MM); Cancer Prevention and Research Institute of Texas grant nos. RP170668 (HX), RR180012 (XJ) and RP160015 (XD); the National Science Foundation RAPID grant no. 2027790 (XJ); and Gordon and Betty Moore Foundation grant no. 9639 (LO-M and HX). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Cancer Prevention and Research Institute of Texas, the Department of Veterans Affairs, or the U.S. government. Publisher Copyright: © The Author(s) 2020.

PY - 2020/9/1

Y1 - 2020/9/1

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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