Li Xiaoqing, Tian Dan, Li Weihua, Dong Bin, Wang Hansong, Yuan Jiajun, Li Biru, Shi Lei, Lin Xulin, Zhao Liebin, Liu Shijian
School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Child Health Advocacy Institute, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, 1678 Dongfang Road, Shanghai, 200127, China.
BMC Health Serv Res. 2021 Mar 17;21(1):237. doi: 10.1186/s12913-021-06248-z.
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.
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.
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).
Using XIAO YI can significantly reduce the waiting time of patients, and thus, improve the outpatient service process of hospitals.
许多研究表明，患者满意度与等待时间显著负相关。一个设计良好的医疗系统不应让患者长时间等待预约和会诊。然而，在中国，患者等待时间较长，而在诊室进行诊断和治疗的实际时间相对较短。
我们开发了一个人工智能（AI）辅助模块并将其命名为“小医”。它可以帮助门诊患者根据主诉自动预约影像检查或实验室检查。这样，门诊患者在就诊前就可以进行检查或检测。采用传统方式就诊的患者被分配到传统组，使用“小医”的患者被分配到AI辅助组。我们于2019年8月1日至2020年1月31日进行了一项回顾性队列研究。采用倾向得分匹配法平衡两组之间的混杂因素。等待时间定义为从挂号到准备实验室检查或影像检查的时间。总费用包括挂号费、检查费、检验费和药费。我们使用Wilcoxon秩和检验比较时间和费用的差异。双侧统计显著性水平设定为0.05。
共纳入12342例就诊病例，其中传统组6171例，AI辅助组6171例。AI辅助组的中位等待时间为0.38（四分位间距：0.20，1.33）小时，而传统组为1.97（0.76，3.48）小时（p<0.05）。AI辅助组的总费用为335.97（四分位间距：244.80，437.60）元人民币，传统组为364.58（249.70，497.76）元人民币（p<0.05）。
使用“小医”可以显著缩短患者等待时间，从而改善医院的门诊服务流程。
