4.6 Article

RiskScape: A Data Visualization and Aggregation Platform for Public Health Surveillance Using Routine Electronic Health Record Data

Journal

AMERICAN JOURNAL OF PUBLIC HEALTH
Volume 111, Issue 2, Pages 269-276

Publisher

AMER PUBLIC HEALTH ASSOC INC
DOI: 10.2105/AJPH.2020.305963

Keywords

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Funding

  1. Massachusetts Department of Public Health

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Automated analysis of EHR data is essential for public health surveillance, and RiskScape provides a user-friendly platform for data aggregation and visualization. It allows for near-real-time monitoring of chronic conditions and infectious diseases, with flexibility to incorporate new conditions like COVID-19. RiskScape plays a crucial role for the Massachusetts Department of Public Health in demonstrating need for funding and identifying inequitably burdened populations.
Automated analysis of electronic health record (EHR) data is a complementary tool for public health surveillance. Analyzing and presenting these data, however, demands new methods of data communication optimized to the detail, flexibility, and timeliness of EHR data. RiskScape is an open-source, interactive, Web-based, user-friendly data aggregation and visualization platform for public health surveillance using EHR data. RiskScape displays near-real-time surveillance data and enables clinical practices and health departments to review, analyze, map, and trend aggregate data on chronic conditions and infectious diseases. Data presentations include heat maps of prevalence by zip code, time series with statistics for trends, and care cascades for conditions such as HIV and HCV. The platform's flexibility enables it to be modified to incorporate new conditions quickly-such as COVID-19. The Massachusetts Department of Public Health (MDPH) uses RiskScape to monitor conditions of interest using data that are updated monthly from clinical practice groups that cover approximately 20% of the state population. RiskScape serves an essential role in demonstrating need and burden for MDPH's applications for funding, particularly through the identification of inequitably burdened populations.

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