Development of an Internet of Health Things (IoHT) Data Acquisition System and Intelligent Risk Assessment System for Elderly Care Customization
Student thesis: Doctoral Thesis
Related Research Unit(s)
In light of the phenomenon of the aging population globally, long-term care services, for example residential care and home care, are important in providing nursing care and treatments to the elderly in the community. Under the policies of long-term care services, the elderly, generally speaking, is required to have adequate medical care and healthcare, offered by a number of healthcare organizations, such as hospitals, mental health centers and rehabilitation centers. Currently, data management for the elderly is relatively centralized in government so as to establish individual electronic medical records (eHRs) and protected health information (PHI), without decision support functionalities. The elderly patients’ medical and healthcare records, created by public and private organizations, cannot be effectively interconnected to support healthcare decisions. Apart from the aforementioned documentation and record systems, the utilization of emerging technologies in the elderly care service sector is relatively low, and existing healthcare decisions are expert-intensive and independent. It is difficult to ensure the objectivity and consistency of the design and implementation of care plans among the elderly. In view of the above situations, the community and healthcare industry are eager to develop a reliable, secure and comprehensive system to provide the elderly with adequate healthcare services and health monitoring. In this paper, an Internet of Healthcare Things (IoHT)-based data acquisition system (IoHT-DAS) is proposed, which provides a structured framework, aimed at integrating IoHT devices to generate a one-stop solution which reports on elderly people’s healthcare status. The elderly can be effectively linked into the integrated IoHT-DAS system, using various sensing and data collection technologies. The proposed system is deemed to be the foundation for the interconnection of medical and healthcare data, for example vital signs of the elderly, to maximize the efficiency of long-term care, whereby existing data systems, including eHRs and PHI, can be securely linked to obtain a full medical and healthcare record of each elderly person. By making use of the collected data, an intelligent risk assessment system (IRAS) evaluates the generic risks and fall prediction of the elderly who live in residential care facilities, through the adoption of adaptive neuro-fuzzy inference systems (ANFIS), which are linked to the historical risk profiles and elderly patients’ data. Thus, integrated indexes identifying the fall and overall risk can be quantified to support decisions on care plan customization, which are made by utilizing the rule-based expert system. The decision-making rules and knowledge collected from nursing officers are retained in the rule repository of the rule-based expert system, therefore, the objectivity and consistency of healthcare decisions among the group of elderly persons can be guaranteed. Subsequently, the elderly’s care plans can therefore be intelligently updated and revised, according to their health deterioration and risk levels. To validate the aforementioned proposed system, a case study in a nursing home in Hong Kong was investigated, whereby the implementation roadmap and stakeholders’ roles were organized to support a generalization of the entire system implementation. Using IoHT-DAS and IRAS, the care plans of the elderly could be customized to satisfy not only patients’ needs, but also stakeholders’ expectations. It was found that the deployment of the proposed systems in the elderly care facilities was feasible and would have a positive impact on long-term care operations and management. Consequently, this study contributes to long-term care management through the enhancement of service quality in the community, by exploiting state-of-the-art technologies and methods. The power of the technologies is unlocked in this project to transform the long-term care sector and to assist the decision support functionalities, so as to standardize the long-term care services, achieving the desired service specifications and quality. Moreover, the research synergy between IoHT-DAS and IRAS is explored in this project, which can create a wide range of research opportunities to obtain applied research values, so as to contribute to our community and society in a practical and applicable manner.