Development of an Open-Source Data-Driven Health Equity Analytics Platform

Healthcare organizations need comprehensive tools that integrate clinical, socioeconomic, and environmental data to better understand and address health inequities. Existing solutions are often costly, lack customization, or are limited in handling complex, multi-source data, hindering research and intervention efforts.

A large healthcare institution seeking to enhance its health disparities research through advanced data visualization, statistical modeling, and geospatial analysis capabilities.

• Develop a scalable, open-source analytics platform that integrates clinical, social, and environmental data sources.
• Enable detailed exploration of health outcomes influenced by social determinants using advanced visualizations and statistical models.
• Improve the platform's performance and usability to facilitate widespread adoption among healthcare researchers and practitioners.
• Expand geospatial analysis capabilities to incorporate detailed location-based variables relevant to health disparities.
• Implement machine learning modules to support predictive analytics and patient outcome assessments.

• Integration of multi-source data including clinical, demographic, social, and environmental data following standardized data structures.
• Self-service data exploration and visualization with customizable dashboards and interfaces.
• Implementation of statistical models, including logistic regression, to analyze health outcomes across different social determinants.
• Advanced geospatial mapping visualizations incorporating variables like Zip5 and FIPS codes.
• Support for machine learning algorithms to enhance predictive analytics and patient segmentation.
• Deployment on Linux servers with secure handling of sensitive health data.
• Role-based access control to ensure data security and compliance.

• Clinical data repositories aligned with health data standards
• Geospatial data services for mapping and location analytics
• Machine learning algorithms and libraries for predictive modeling

• Scalable architecture to support increasing data volume and user load
• Application responsiveness supporting real-time data exploration
• Secure handling of sensitive health information with compliance to data privacy regulations
• Open-source platform to facilitate customization and cost-effective deployment

The platform is expected to significantly improve researchers' ability to identify and analyze health inequities, leading to more targeted interventions. By enabling widespread, cost-effective access to advanced analytics, the project aims to foster innovative health disparities research, increase stakeholder engagement, and support efforts to reduce health disparities across diverse populations.