Digital Twin Technology for Hospital Infrastructure: Integrating 5G Private Network Design, Coverage Analysis, and Lifecycle Automation in Large Healthcare Facilities

Abstract

The convergence of Digital Twin (DT) technology and fifth-generation (5G) private network architecture presents a transformative paradigm for the design, deployment, and operational management of wireless network infrastructure in large healthcare facilities. Hospital campuses represent uniquely challenging deployment environments: electromagnetically hostile, geometrically complex, operationally continuous, and subject to stringent regulatory constraints governing both patient safety and data privacy. Traditional wireless network planning methods, manual site surveys, empirical path loss models, and iterative post-installation rework are systematically inadequate for these environments, producing persistent coverage failures and extended commissioning timelines that concentrate their most damaging consequences in the highest-acuity zones of the hospital. This article presents a unified architectural framework integrating multi-modal digital twin construction, machine learning-enhanced RF simulation, microservices-based deployment architecture, and operational sustainability strategies specifically designed for hospital-scale 5G private network deployment. The framework encompasses five interdependent technical components: Building Information Modeling (BIM) and LiDAR-based DT construction; ML-corrected RF accuracy optimization deploying Gaussian Process Regression, convolutional neural networks, reinforcement learning, Graph Neural Networks, and federated learning; a seven-service microservices platform with Kubernetes orchestration; caching and computation efficiency strategies reducing end-to-end simulation latency; and carbon-aware energy management aligned with sustainable cloud computing frameworks. Author's primary research projections indicate that DT-enabled planning reduces total cost of ownership by USD 3-6 million on a standard 600-bed hospital deployment and reduces commissioning timelines by 15-30% relative to traditional methods.