Connecting the Dots: From Neighborhood Intelligence to Smart Healthcare Cities Building a Scalable Framework for Global Health Data and AI-Driven Access

Abstract

Healthcare inequity has evolved from a problem of medical capacity to a crisis of information visibility. This paper presents a scalable conceptual framework for a Global Health Intelligence Network (GHIN), an AI-driven, geospatially enabled model designed to connect global healthcare data and improve equitable access to care. Building on the author’s foundational work in LocalBlox (neighborhood intelligence), IdMap (global POI data infrastructure), and The Smart Healthcare City (Arefin, 2023), GHIN proposes an integrated data ecosystem that links healthcare facilities, professionals, and communities through intelligent mapping and analytics.
The framework operates across three functional layers: a spatial data infrastructure for real-time facility and resource mapping; an AI analytics layer for predictive modeling, demand forecasting, and service optimization; and a human interface layer (VirtualDoctorX) that connects patients to verified local or virtual healthcare options. Through this model, GHIN transforms fragmented health data into actionable intelligence — enabling governments, NGOs, and global health agencies to identify access gaps, optimize interventions, and strengthen system resilience.
By fusing AI, spatial intelligence, and ethical data governance, this work envisions a future where every person, regardless of geography, is visible within a unified global healthcare map. The study concludes that connected data ecosystems can catalyze equitable, data-informed global health transformation.