Abstract: This article focuses on matching stroke rehabilitation services, and patient needs through the interconnection between patient demand and rehabilitation service capabilities. A solution is proposed based on the KJ, fuzzy AHP, and QFD methods to address this problem. Specifically, the KJ method categorizes user needs, and the fuzzy AHP method calculates weights and rankings. Furthermore, rehabilitation service capability indicators are developed, and the QFD method is applied to match customer needs with rehabilitation service capability indicators. The service indicator value is constructed through mapping relationships, and the rehabilitation service capability value is obtained by adding up the results. The best matching scheme is predicted by comparing rehabilitation service capability values of service alternatives. The success of the model has been proven by examining the case. It has helped patients and service organizations find suitable caregivers. The research results illustrate that the proposed model can effectively address the problem of stroke rehabilitation services and patient needs matching and has practical value and potential applications. Therefore, this research is significant in enhancing the quality of stroke rehabilitation services and patient satisfaction and provides a reference value for future studies of similar issues.

Hua Wei, Ding-Bang Luh, Yue Sun, Xiao-Hong Mo and Yu-Hao Shen, “The Model of Stroke Rehabilitation Service and User Demand Matching” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140740

@article{Wei2023,
title = {The Model of Stroke Rehabilitation Service and User Demand Matching},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140740},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140740},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Hua Wei and Ding-Bang Luh and Yue Sun and Xiao-Hong Mo and Yu-Hao Shen}
}