A Smart-Mutual Decentralized System for Long-Term Care

Existing caretakers of long-term care are assigned constrainedly and randomly to taking care of older people, which could lead to issues of shortage of manpower and poor human quality, especially the proportion of older people increases year after year to let long-term care become more and more important. In addition, due to different backgrounds, inadequate caregivers may cause older people to suffer from spiritual alienation under the current system. Most of the existing studies present a centralized architecture, but even if technology elements are incorporated, such as cloud center services or expert systems, it is still impossible to solve the above-mentioned challenges. This study moves past the centralized architecture and attempts to use the decentralized architecture with Artificial Intelligence and Blockchain technology to refine the model of providing comprehensive care for older people. Using the proposed mapping mutual clustering algorithm in this study, the positions of caregivers and older people can be changed at any time based on the four main background elements: risk level, physiology, medical record, and demography. In addition, this study uses the proposed long-term care decentralized architecture algorithm to solve the stability of care records with transparency to achieve the effect of continuous tracking. Based on previous records, it can also dynamically change the new matching mode. The main contribution of this research is the proposal of an innovative solution to the problem of mental alienation, insufficient manpower, and the privacy issue. In addition, this study evaluates the proposed method through practical experiments. The corporation features have been offered and evaluated with user perceptions by a one-sample t-test; the proposed algorithm to the research model also has been compared with not putting it into the model through ANOVA analysis to get that all hypotheses are supported. The results reveal a high level of accuracy of the proposed mutual algorithm forecasting and positive user perceptions from the post-study questionnaire. As an emerging research topic, this study undoubtedly provides an important research basis for scholars and experts who are interested in continued related research in the future.