The paper addresses the significant challenges posed by the rapidly growing elderly population, which is projected to reach 2.1 billion by 2050, with nearly 50% of medical resources forecasted to be allocated to their care. Current issues in elderly healthcare include a high incidence of chronic diseases, weaker bodies, memory loss, and increased risk of falls, leading to higher demands for community and family healthcare services. Furthermore, many elderly individuals lack sufficient knowledge to manage their chronic conditions due to low clinic visiting rates and diagnostic accuracy, often encountering the “three long and one short” situation in traditional medical services (long registration, waiting, payment times, and short treatment time). Existing intelligent medical systems also face problems such as a lack of real-time interaction between institutions and patients, insufficient fusion of physical and information systems, low accuracy in crisis warnings, and inadequate continuous personal health management services throughout an elderly patient’s lifecycle.
To overcome these issues and enable real smart healthcare, the authors propose a novel cloud-based framework called CloudDTH. This framework is built upon the integration of Digital Twin (DT) technology with cloud computing, big data, and the Internet of Things (IoT).
Key aspects and contributions of the paper include:
- Introduction of Digital Twin Healthcare (DTH): DTH is defined as a new medical simulation approach that provides fast, accurate, and efficient medical services using DT technology with multi-science, multi-physics, and multi-scale models. It comprises three main parts: physical objects (e.g., patients, medical devices, external factors), virtual objects (digital models of these physical entities), and healthcare data that connects them. DTH aims to achieve real-time monitoring of health, prediction of medical equipment failures, and allows medical experts to determine symptoms and prescribe treatments remotely through virtual models, reducing risks and costs.
- Proposed CloudDTH Framework: CloudDTH is a generalized and extensible framework in a cloud environment designed for monitoring, diagnosing, and predicting health aspects of individuals, particularly the elderly, often using wearable medical devices. Its core objective is to achieve interaction and convergence between medical physical and virtual spaces. CloudDTH conceptualizes three main roles: resource providers (patients, medical institutions, third parties providing data/capabilities), platform operators (responsible for management and operation), and users (medical staff, researchers, elderly patients, informal caregivers). It offers both offline and online service modes.
- Layered Architecture: The CloudDTH system is presented with an eight-layer reference framework: Resource Layer, Perception Layer, Virtual Resource Layer, Middleware Layer, Service Layer, User Interface Layer, Application and User Layers, and Security System, complemented by a Standard System and Specification module. Each layer plays a specific role, from collecting healthcare resources and data (e.g., hardware/software resources, patient information, capabilities) to virtualizing resources, managing data and services, providing user-facing services, and ensuring overall system security and standardization.
- Key Enabling Technologies: The framework relies on six critical technological areas:
- Healthcare Resource Access (e.g., Health IoT, wearable devices, network transmission using M2M or WiFi).
- Healthcare Data Management and Analysis (e.g., data acquisition, multi-granular data cleaning, heterogeneous data fusion, data mining, and intelligence analysis).
- Healthcare Data Security (e.g., personal privacy, storage, payment security, and blockchain technologies).
- DTH Model Management (e.g., multidimensional model integration, reuse, verification, and optimization).
- Healthcare Service Management (e.g., equipment health management, quality monitoring, service search, and scheduling).
- Healthcare Information Fusion and Sharing (e.g., heterogeneous platform integration, business process integration, and cross-domain collaboration).
- Demonstrated Feasibility through Case Studies: The paper validates the feasibility of CloudDTH through application scenarios and a case study, focusing on the elderly patient’s medical lifecycle (monitoring, diagnosis, treatment, and evaluation). This includes:
- Crisis Early Warning and Real-Time Supervision: Demonstrated with real-time ECG monitoring, showing how the DTH model can analyze data, predict abnormal phenomena (like arrhythmia), and provide individualized medication reminders based on a patient’s physical and genetic condition. This function reflects the platform’s ability to support personalized medicine.
- Resource Scheduling and Optimization: Simulated the interaction of emergency and routine patients with hospital wards, demonstrating how the platform can optimize medical resource allocation (e.g., adjusting bed capacity) in response to external factors (like high temperatures causing an increase in emergency patients), thereby assisting decision-makers in hospital management and design.
In essence, the paper proposes CloudDTH as a comprehensive and intelligent solution for lifecycle-long personal health management for the elderly, bridging the gap between the medical physical and virtual worlds to deliver more accurate, fast, and personalized healthcare services. The authors highlight that while cloud computing, IoT, and mobile internet are widely used in healthcare, most research focuses on platform concentration and data monitoring, with fewer publications on real-time supervision or crisis warning for the elderly, making Digital Twin a valuable approach to solve the bottleneck of information-physical interaction and convergence.
APA Reference:
Liu, Y., Zhang, L., Yang, Y., Zhou, L., Ren, L., Wang, F., Liu, R., Pang, Z., & Deen, M. J. (2019). A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access, 7, 2909828–2909828. https://doi.org/10.1109/ACCESS.2019.2909828
