AGENTIC SOCIO-TECHNICAL SYSTEMS FOR AGING DEMOGRAPHICS: INTEGRATING GRAPH NEURAL NETWORKS AND DEEP REINFORCEMENT LEARNING FOR RESILIENT HEALTHCARE LOGISTICS

Abstract

The fast-growing aging demographics in both developed and developing countries are exerting increasing stress on healthcare systems, especially those that experience high old-age dependency rates.Healthcare logistics models based on traditional methods tend to employ static and reactive decision-making strategies, which cannot efficiently respond to changes in healthcare demands, hence leading to delayed access to healthcare facilities, poor resource allocation, and growing healthcare accessibility gaps.In this paper, we explore how the use of an Agentic Artificial Intelligence (AI) system combining Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL) may be beneficial to healthcare logistics in aging populations.We present our research proposal on designing a socio-technical model for healthcare logistics, where elderly persons, healthcare organizations, caregivers, transportation services, and emergency responders are considered nodes connected through relationships.GNN is used to analyze intricate dependencies between the aforementioned components of healthcare communities, while DRL automatically learns optimal healthcare resource dispatching policies in dynamic environments.Our framework strives to reduce delays in healthcare assistance delivery, improve healthcare coverage, and increase the level of logistical robustness in situations when the environment becomes challenging for healthcare logistics providers.As demonstrated by simulation analysis, the designed hybrid artificial intelligence framework can increase responsiveness in health care relative to existing heuristically based scheduling techniques.Simulation results have shown lower latency in response, increased efficiency in resource allocation, and better scalability and adaptability under stress conditions.The results presented in the paper contribute to the creation of an intelligent health-care logistics system for aging populations.