AI-Powered Predictive Analytics for Secure Healthcare Systems and Intelligent Resource Scaling with Adaptive Workload Management

Abstract

Healthcare systems increasingly depend on cloud-native platforms, connected medical devices, electronic health records, and data-intensive clinical applications that require secure, reliable, and scalable computing infrastructure. Conventional resource allocation mechanisms often respond reactively to workload changes, resulting in resource underutilization, performance degradation, increased operational costs, and potential service disruptions. This research proposes an AI-powered predictive analytics framework that integrates healthcare workload forecasting, security intelligence, and adaptive resource scaling to improve the resilience and efficiency of healthcare information systems. The framework uses machine learning models to analyze historical and real-time workload indicators, including request rates, processing latency, resource utilization, application demand, and service activity. Predictive models estimate future workload patterns and initiate proactive scaling decisions before resource saturation occurs. Security analytics are incorporated to identify anomalous access behavior, unusual traffic patterns, and potential cyber threats while maintaining healthcare data protection requirements. An adaptive workload management mechanism dynamically distributes computing resources across cloud and distributed environments according to predicted demand and risk conditions. The proposed approach emphasizes explainability, continuous monitoring, automated decision support, and policy-aware resource management. The framework can support improved service availability, resource utilization, response time, scalability, and security for modern healthcare systems while providing a foundation for intelligent and resilient healthcare infrastructure management.