The Digital Analytic Patient Reviewer (DAPR) for COVID-19 Data Mart Validation (preprint)

Objective: To provide high-quality data for COVID-19 research, we validated COVID-19 clinical indicators and 22 associated computed phenotypes, which were derived by machine learning algorithms, in the Mass General Brigham (MGB) COVID-19 Data Mart. Materials and Methods: Fifteen reviewers performed a manual chart review for 150 COVID-19 positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered the Digital Analytic Patient Reviewer (DAPR). DAPR is a web-based chart review tool that integrates patient notes and provides note search functionalities and a patient-specific summary view linked with relevant notes. Within DAPR, we developed a COVID-19 validation task-oriented view and information extraction logic, enabled fast access to data, and considered privacy and security issues. Results: The concepts for COVID-19 positive cohort, COVID-19 index date, COVID-19 related admission, and the admission date were shown to have high values in all evaluation metrics. For phenotypes, the overall specificities, PPVs, and NPVs were high. However, sensitivities were relatively low. Based on these results, we removed 3 phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes towards using DAPR for chart review. They assessed the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed. Discussion and Conclusion: DAPR's patient summary view accelerated the validation process. We are in the process of automating the workflow to use DAPR for chart reviews. Moreover, we will extend its use case to other domains.

Development of a COVID-19 Application Ontology for the ACT Network (preprint)

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.