Author:
Li Xiaoqing,Tian Dan,Li Weihua,Dong Bin,Wang Hansong,Yuan Jiajun,Li Biru,Shi Lei,Lin Xulin,Zhao Liebin,Liu Shijian
Abstract
Abstract
Background
Many studies suggest that patient satisfaction is significantly negatively correlated with the waiting time. A well-designed healthcare system should not keep patients waiting too long for an appointment and consultation. However, in China, patients spend notable time waiting, and the actual time spent on diagnosis and treatment in the consulting room is comparatively less.
Methods
We developed an artificial intelligence (AI)-assisted module and name it XIAO YI. It could help outpatients automatically order imaging examinations or laboratory tests based on their chief complaints. Thus, outpatients could get examined or tested before they went to see the doctor. People who saw the doctor in the traditional way were allocated to the conventional group, and those who used XIAO YI were assigned to the AI-assisted group. We conducted a retrospective cohort study from August 1, 2019 to January 31, 2020. Propensity score matching was used to balance the confounding factor between the two groups. And waiting time was defined as the time from registration to preparation for laboratory tests or imaging examinations. The total cost included the registration fee, test fee, examination fee, and drug fee. We used Wilcoxon rank-sum test to compare the differences in time and cost. The statistical significance level was set at 0.05 for two sides.
Results
Twelve thousand and three hundred forty-two visits were recruited, consisting of 6171 visits in the conventional group and 6171 visits in the AI-assisted group. The median waiting time was 0.38 (interquartile range: 0.20, 1.33) hours for the AI-assisted group compared with 1.97 (0.76, 3.48) hours for the conventional group (p < 0.05). The total cost was 335.97 (interquartile range: 244.80, 437.60) CNY (Chinese Yuan) for the AI-assisted group and 364.58 (249.70, 497.76) CNY for the conventional group (p < 0.05).
Conclusions
Using XIAO YI can significantly reduce the waiting time of patients, and thus, improve the outpatient service process of hospitals.
Funder
National Science Foundation of China
Shanghai Municipal Commission of Health and Family Planning
Medical and Engineering Cooperation Project of Shanghai Jiao Tong University
Science and Technology Innovation-Biomedical Supporting Program of Shanghai Science and Technology Committee
Chinese Translational Medicine Basic Instructure Project
Program for Artificial Intelligence Innovation and Development of Shanghai Municipal Commission of Economy and Informatization
Construction of Alliance Based on Artificial Intelligence for Pediatric Common Diseases
Publisher
Springer Science and Business Media LLC
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