A Community-Driven Digital Twin Living Lab Framework to Improve the Efficiency and Reliability of Health and Elderly Care Services

Efficient, accessible, and resilient health and elderly care systems are crucial for citizen well-being. Hong Kong and other advanced aging societies face unprecedented challenges in providing quality health and elderly care services due to rapidly aging populations, increasing prevalence of chronic health conditions, and growing aging-in-place demands with escalating costs. While innovative technologies like gerontechnology, artificial intelligence (AI), AgeTech, digital twin, and robotics offer tremendous potential to address these challenges, they raise significant concerns about acceptance, adaptability, safety, and usability among end-users. “Living lab” provides collaborative and flexible environments for testing these technologies and co-developing practical digital health and elderly care solutions.Developing living labs with enhanced ambient and operational intelligence, however, faces considerable challenges, including difficulties in digitalizing age-friendly built environments and heterogeneous physical entities, integrating complex health and elderly care information systems, monitoring human behaviors and their interactions with smart devices and AI models, and operating networked hybrid lab workplaces. Therefore, a flexible framework driving stakeholders and citizens towards co-designing and co-operating digital health living labs could open new pathways to deliver quality services to address the healthcare and “silver tsunami” challenges locally and globally.This research aims to develop a comprehensive community-driven facility management framework for digital health living labs to assist and scale up smart health and elderly care services delivery. The framework comprises: a systematic digital twin model for digitalizing living lab environments, devices, facilities, end-users, systems, and healthcare processes; a service-oriented mechanism for sharing and exchanging digital twin data across smart devices and applications, and AI-powered systems; an AI and ambient intelligence enabled model to analyze interaction patterns and predict anomalies, safety risks, and reliability issues; (iv) a community-driven approach to co-developing and co-operating living labs; and a cloud-native prototype system demonstrating how to implement the proposed framework.The research methodology encompasses interviews, case studies, and focus group meetings to identify obstacles and requirements for operating digital health living labs, alongside feasible strategies for deploying AI and innovative technologies. A comprehensive software reference architecture will be designed to facilitate the implementation of the framework, which is followed by validation through case studies.The novelty of this investigation lies in integrating digital facility management practices, community-driven open innovation approaches, and the digital twin middleware developed by our team for co-managing digital health labs. The findings will lay a solid foundation for developing smart, accessible, and resilient health and elderly care ecosystems.