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.

中文翻译：

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COVID-19 TestNorm：将COVID-19测试名称标准化为LOINC代码的工具

利用电子健康记录（EHR）中常规临床实践数据的大型观察数据网络，是研究冠状病毒病2019（COVID-19）的关键资源。对于跨机构在COVID-19研究中二次使用EHR而言，数据规范化是一个关键挑战。在这项研究中，我们解决了自动化COVID-19诊断测试正常化的挑战，COVID-19诊断测试是关键数据元素，但在临床实施后已发布了受控术语。我们开发了一个简单但有效的基于规则的工具，称为COVID-19 TestNorm，可自动将本地COVID-19测试名称标准化为标准LOINC（逻辑观察标识符名称和代码）代码。COVID-19 TestNorm是使用从8个医疗保健系统收集的568个测试名称开发和评估的。我们的结果表明，在独立的测试装置上，它可以达到97.4％的精度。COVID-19 TestNorm可以作为开发人员的开源软件包，也可以作为最终用户的在线Web应用程序（https://clamp.uth.edu/covid/loinc.php）。我们认为，这将是支持将EHR二次用于COVID-19研究的有用工具。