AI-enabled population health surveillance using multi-source healthcare data to predict public health system strain across U.S. Regions

Population health surveillance is increasingly critical for anticipating systemic stress within healthcare systems, particularly as the United States faces rising demands from chronic diseases, aging populations, and emerging public health threats. Artificial intelligence (AI)–enabled surveillance frameworks provide a transformative approach to monitoring and forecasting healthcare system strain by integrating diverse and high-volume data sources. At a broad level, AI-driven population health analytics leverage multi-source healthcare data including electronic health records, insurance claims, pharmacy utilization data, environmental indicators, wearable device outputs, and social determinants of health to generate comprehensive insights into evolving health patterns across communities. Through advanced machine learning and deep learning models, these systems can identify latent correlations among clinical indicators, demographic characteristics, and regional health behaviors that may signal impending public health pressures. At a more targeted level, AI models such as gradient boosting, recurrent neural networks, and ensemble learning techniques enable predictive estimation of hospital admissions, emergency department demand, and healthcare workforce requirements across different U.S. regions. Integrating geospatial analytics and real-time epidemiological monitoring further enhances the ability of public health agencies to detect early signals of healthcare system strain and implement proactive mitigation strategies. Consequently, AI-enabled population health surveillance offers a scalable and data-driven framework for improving public health preparedness, optimizing healthcare resource distribution, and strengthening resilience across regional healthcare infrastructures in the United States.