iCAT: AI-Driven GIS Platform for Analyzing Health Disparities
SUMMARY
The Intelligent Catchment Analysis Tool (iCAT) enhances health outcomes and resource allocation in underserved communities by employing artificial intelligence and geographic information system analytics to identify and target health disparities through real-time, culturally tailored data visualization and interpretation
The Unmet Need: Insufficient tools for precise identification and targeting of health disparities in underserved populations
- Health disparities in underserved, low-income communities contribute significantly to preventable morbidity and mortality, including elevated cancer incidence and mortality; current methods often lack integration of multifactorial data and culturally sensitive engagement, limiting effective intervention targeting and resource distribution.
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The rise of data-driven healthcare and the geographic information system (GIS) industry combined with advances in artificial intelligence and machine learning presents an opportunity to synthesize complex health, demographic, socioeconomic, and environmental data, generating actionable insights to better address community-specific health challenges.
The Proposed Solution: AI- and machine learning-driven GIS platform providing real-time, multilingual, and culturally adapted health disparity analysis and educational tools
- The faculty inventor iCAT- an interactive, cloud-based geographic information system integrated with advanced AI/ML analytics, natural language processing, and multimedia delivery that analyzes health-related geospatial data to identify disparities and inform resource allocation in underserved areas; it uniquely offers multilingual, voice-interactive user interfaces and culturally tailored content beyond direct translation. The platform incorporates statistical rigor through multivariate modeling and confounder adjustment to enhance data reliability and supports a range of analytic methods including gradient boosting and neural networks. Currently validated within a five-county cancer catchment area comprising approximately eight million individuals, iCAT is scalable to various diseases and regions with ongoing enhancements planned to broaden data sources and improve data verification.
ADVANTAGES
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Real-time, comprehensive health disparity analytics
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Multilingual, voice-interactive user interface
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Culturally tailored and personalized educational content
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Advanced statistical and AI-driven data validation
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Integration of diverse demographic, environmental, and healthcare data
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Cloud-based scalability across conditions and geographies
APPLICATIONS
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Identification of cancer burden and disparities within regional catchment areas
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Support for community health organizations in underserved populations
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Resource allocation and intervention planning for healthcare policymakers