A study on requirement optimization of intelligent health care information system for rural elderly based on improved FKM-TOPSIS method

• Published in: Information services & use pp. 1 - 18
• Main Authors: Zhao, Xiangqin, Wang, Bin
• Format: Journal Article
• Language: English
• Published: 04.08.2023
• ISSN: 0167-5265; 1875-8789
• DOI: 10.3233/ISU-230198

Against the backdrop of China’s young and middle-aged population moving to the cities to work, older adults in rural areas of China are facing even more severe old-age and healthcare dilemmas. Under such circumstances, how to improve the functions of age-appropriate smart wearable products to meet the needs of older adults in rural areas for old age and health care has become a focal issue in Chinese society. In this paper, we change the traditional Fuzzy Kano Model (FKM) research method, quantify the research results, and use the superiority and inferiority coefficient formula to scientifically classify user needs after conducting research on different functions of smart wearable devices and obtaining different user needs: M denotes must-be demand; O denotes performance demand; A denotes attractive demand; and I denote indifferent demand. A theoretical multidimensional analysis of the generated data was performed to determine the requirements priority of the rural elderly. Then, using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach, compute the relative closeness of user satisfaction, i.e., the priority of functional needs. Finally, testing the product prototypes of “PC-based and mobile-based” information systems of smart wearable devices and identifying the most critical demand concentrated on emergency assistance for rural older adults. Such as 120 Call, Call Emergency Contacts, Positioning System, Blood Pressure Testing, Heartbeat Pulse Monitoring, etc. These functions can effectively reduce the health risks of older adults in critical emergencies, confirm the scientific character of user demand prioritization, establish the viability of the study, and provide new ideas for intelligent product-related research to continue.