Abstract
In top tertiary hospitals situated in big cities in China, long registration wait times, payment and short treatment times are common problems due to the considerable number of patients. For this reason, self-service terminals are introduced by hospitals to shorten waiting time and improve patients’ medical experience. In order to improve the quality of outpatient service reasonably and effectively, this paper used the qualitative and quantitative methods to study the interface of self-service terminal. The research combined Kansei Engineering theory with Service Design theory to make the perceptual evaluation of self-service terminal interface and improve the service content. Firstly, the perceptual vocabularies were collected from the literature of medical self-service system research, and the interface samples were collected from the self-service terminals used by public hospitals in Beijing, Shanghai and other big cities. Secondly, through the discussion of the expert group, the words with no obvious characterization were deleted, then the questionnaire method was used to screen out the high-frequency words and representative interfaces. Thirdly, the vocabularies and samples were integrated into a perceptual evaluation scale of the interface of self-service terminals. The research used factor analysis method to process the collected data from the questionnaire survey and summarized the significant perceptual vocabularies. Finally, combining with the field research of the hospitals, this paper discussed and analyzed the interface elements of the self-service system, and summarized an improvement strategy. It is to improve the readability of the interface, optimize the logic of interface interaction and strengthen the interface guidance.
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Shi, Z., Shang, H., Wang, C., Wang, Z., Zhou, M. (2020). Research on Hospital Self-service Machine Interaction Design Based on Kansei Engineering. In: Di Bucchianico, G., Shin, C., Shim, S., Fukuda, S., Montagna, G., Carvalho, C. (eds) Advances in Industrial Design. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1202. Springer, Cham. https://doi.org/10.1007/978-3-030-51194-4_88
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DOI: https://doi.org/10.1007/978-3-030-51194-4_88
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