Digital Twins for Cloud-Integrated Healthcare 5.0 20-35

Abstract

This study explores the potential of digital twin technology in the context of healthcare management, with a focus on improving system efficiency, predictive analytics, and operational decision-making. A digital twin framework is formulated and enhanced using a Genetic Algorithm (GA), which is employed to optimize healthcare resource allocation, patient flow, and treatment planning. The framework integrates real-time data from patient monitoring devices, hospital operations, and cloud-based platforms to generate dynamic, virtual replicas of critical healthcare entities. The GA is utilized to iteratively evolve optimal configurations and decision paths within the digital twin model, ensuring adaptive responses to changing clinical and logistical demands. Through simulation and analysis, the study demonstrates the viability of the proposed GA-driven digital twin approach in advancing the goals of healthcare 5.0 enabling a more connected, intelligent, and patient-centric healthcare ecosystem.