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An Interactive Information Visualization System for Temporal Queries in a Large-scale COVID-19 EHR Dataset
Yan Huang1,2, Shiqiang Tao1,2, Wei-Chun Chou1,2
1McGovern Medical School.
Abstract:
COVID-SPHERE is a self-service web application designed to advance clinical research informatics by facilitating secondary use of Electronic Health Records (EHR) for COVID-19 research. The system employs a flexible EHR concept framework that defines hierarchical concepts and ontologies, enabling clinical researchers to build complex temporal queries through an intuitive, single-click interface without requiring database expertise. Our method dynamically generates MongoDB queries in real-time and offers interactive, faceted visualizations to analyze longitudinal patient activities and integrated health records, supporting both individual patient analysis and population-level research. Hosted on a server managing over 5 TB of data encompassing 30 billion health records spanning 15 years from more than 8.8 million patients, this work demonstrated its generalizability by supporting multiple published research studies investigating various COVID related research topics on epidemiology, treatment outcomes, and long-term sequelae since November 2020. By simplifying the cohort discovery process, COVID-SPHERE reduces the informatics barriers between researchers and EHR data, enhancing the efficiency of clinical and translational research while promoting data-driven insights for COVID-19 surveillance and intervention. Its architecture is applicable to other large-scale clinical research data warehouses, offering a model for future public health informatics systems.
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