Adaptive Health Intervention for Older Adults Using Causal AI on Streaming Social and Behavioral Data

Abstract

Healthcare infrastructures worldwide face growing pressure to provide individualized and forward-thinking care for aging populations. Traditional healthcare models frequently fail to meet the needs of people dealing with long-term illnesses or physical movement limitations. This research examines how real-time social and behavioral data streams, enhanced by causal AI, can revolutionize adaptive health interventions. Causal AI utilizes wearable sensors, environmental monitors, and social media activity to identify the root causes of health changes, leading to early and tailored interventions. Personalized support systems powered by AI dynamically respond to changing health conditions and contextual factors, delivering medication reminders as well as assistance with mental health engagement and social connectivity. The study demonstrates that a comprehensive, data-driven approach facilitates the implementation of a 4P medicine framework, which improves the early detection of diseases while supporting personalized treatment planning and enhancing quality of life through predictive and participatory medical practices.