Digital Twin Technology in Telehealth: Real-Time Patient Simulation for Predictive Diagnosis and Treatment Planning

Abstract

Digital Twin (DT) technology has emerged as a transformative innovation in telehealth, enabling real-time simulation of patient physiology to support predictive diagnosis, continuous monitoring, and personalized treatment planning. Unlike conventional telemedicine systems that rely solely on acquired physiological data, digital twins integrate multimodal inputs—including vital signs, imaging data, historical records, genetic profiles, lifestyle parameters, and environmental data—into dynamic, virtual patient models. These models continuously adapt to reflect a patient’s biological state and provide clinicians with predictive insights unavailable through traditional monitoring. This research examines the architecture, computational frameworks, clinical applications, and implementation barriers of digital twin technology in telehealth. The paper presents a methodological framework for developing real-time digital twins, accompanied by a case study involving cardiac patients. Data analysis tables evaluate the predictive accuracy of DT-based simulation models relative to standard clinical monitoring. Findings highlight that digital twins enhance risk stratification accuracy, optimize treatment decisions, and reduce clinical uncertainty. The study concludes with recommendations for scaling digital twin ecosystems through interoperable standards, cloud-edge integration, AI-driven modeling, and ethical governance frameworks.