scholarly journals Development of a COVID-19 Application Ontology for the ACT Network

JAMIA Open ◽  
2021 ◽  
Author(s):  
Shyam Visweswaran ◽  
Malarkodi J Samayamuthu ◽  
Michele Morris ◽  
Griffin M Weber ◽  
Douglas MacFadden ◽  
...  
Keyword(s):  
Laboratory Tests ◽  
Research Community ◽  
Health Centers ◽  
Academic Health Centers ◽  
Data Networks ◽  
Health Records ◽  
Course Of Illness ◽  
Application Ontology ◽  
Value Sets ◽  
Data Elements

Abstract Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

scholarly journals Development of a COVID-19 Application Ontology for the ACT Network

2021 ◽  
Author(s):  
Shyam Visweswaran ◽  
Malarkodi J Samayamuthu ◽  
Michele Morris ◽  
Griffin M Weber ◽  
Douglas MacFadden ◽  
...  
Keyword(s):  
Laboratory Tests ◽  
Research Community ◽  
Health Centers ◽  
Academic Health Centers ◽  
Data Networks ◽  
Health Records ◽  
Course Of Illness ◽  
Application Ontology ◽  
Value Sets ◽  
Data Elements

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

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

2020 ◽  
Vol 27 (9) ◽  
pp. 1437-1442 ◽  
Author(s):  
Xiao Dong ◽  
Jianfu Li ◽  
Ekin Soysal ◽  
Jiang Bian ◽  
Scott L DuVall ◽  
...  
Keyword(s):  
Web Application ◽  
Data Networks ◽  
Clinical Implementation ◽  
Health Records ◽  
Critical Data ◽  
Secondary Use ◽  
Independent Test ◽  
Data Elements ◽  
Online Web ◽  
Critical Resources

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.

scholarly journals Novel coronavirus, novel faculty development programs: rapid transition to eLearning during the pandemic

2020 ◽  
Vol 48 (5) ◽  
pp. 446-449 ◽  
Author(s):  
Zuber D. Mulla ◽  
Valerie Osland-Paton ◽  
Marco A. Rodriguez ◽  
Eduardo Vazquez ◽  
Sanja Kupesic Plavsic
Keyword(s):  
Online Education ◽  
Faculty Development ◽  
El Paso ◽  
Health Centers ◽  
Academic Health Centers ◽  
The Novel ◽  
Development Programs ◽  
Rapid Transition ◽  
Texas Tech University ◽  
Novel Coronavirus

AbstractThe novel coronavirus disease 2019 (COVID-19) has caused a rapid and massive transition to online education. We describe the response of our Office of Faculty Development at Texas Tech University Health Sciences Center El Paso (TTUHSC EP) to this unprecedented challenge during and after this post-pandemic crisis. The initiatives for emergency transition to eLearning and faculty development described in this paper may serve as a model for other academic health centers, schools, colleges and universities.

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