Research Article
Evaluation Model of Telemedicine Service Quality Based on Machine Sensing Vision
@ARTICLE{10.4108/eetpht.v8i3.669, author={Yingdong Cao and Hui Li and Zeqi Xie and Zhenti Cui and Loknath Sai Ambati}, title={Evaluation Model of Telemedicine Service Quality Based on Machine Sensing Vision}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={8}, number={3}, publisher={EAI}, journal_a={PHAT}, year={2022}, month={8}, keywords={Machine sensing vision technology, Language information assessment, Evaluation index system, Subjective and objective combination weighting method, Telemedicine service quality assessment}, doi={10.4108/eetpht.v8i3.669} }- Yingdong Cao
Hui Li
Zeqi Xie
Zhenti Cui
Loknath Sai Ambati
Year: 2022
Evaluation Model of Telemedicine Service Quality Based on Machine Sensing Vision
PHAT
EAI
DOI: 10.4108/eetpht.v8i3.669
Abstract
INTRODUCTION: At present, the common telemedicine service quality evaluation methods can not obtain the key evaluation indicators, which leads to the low accuracy and low user satisfaction. OBJECTIVES: This paper constructs a telemedicine service quality evaluation model based on machine vision technology. METHODS: Machine vision technology is used to obtain telemedicine service information, preliminarily select service quality assessment indicators, complete the selection of indicators, build a telemedicine service quality assessment indicator system, adopt subjective and objective combination method to calculate the weight of service quality assessment indicators, and combine matter element analysis method to build a telemedicine service quality assessment model. RESULTS: The experimental results show that the Cronhach a is higher than 0.7, the Barthel index is higher than 90, and the satisfaction of many users is more than 90%. CONCLUSION: The proposed method solves the problems existing in the current method and lays a foundation for the development of telemedicine service technology.
Copyright © 2022 Loknath Sai Ambati et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.