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Artificial intelligence-assisted reduction in patients’ waiting time for outpatient process: a retrospective cohort study

机译:人工智能辅助减少患者的门诊过程等待时间:回顾性队列研究

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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.
机译:许多研究表明,患者满意度与等待时间显着呈负相关。精心设计的医疗保健系统不应让患者等待时间,以便预约和咨询。然而,在中国,患者在诊所在诊断和治疗中花费的实际时间比较少。我们开发了一个人工智能(AI)译文模块,并将其命名为小义。它可以帮助门诊,根据其首席投诉自动订购成像考试或实验室测试。因此,在他们去看医生之前可以检查门诊病人。以传统方式看到医生的人被分配给传统群体,那些使用萧义的人被分配给AI辅助集团。我们在2019年8月1日至2020年1月31日进行了一项回顾性的队列研究。倾向得分匹配用于平衡两组之间的混杂因素。等候时间被定义为从注册准备实验室测试或成像检查的时间。总成本包括注册费,测试费,考试费和药费。我们使用了Wilcoxon Rank-Sum测试来比较时间和成本的差异。统计显着性水平为双方设定为0.05。招募了12千和三百四十二次访问,由6171次访问传统集团的访问,并在AI辅助组中访问6171次访问。中位等待时间为0.38(间环范围:0.20,1.33)小时为AI辅助组,与常规组的1.97(0.76,3.48)小时(P?& 0.05)。总成本为335.97(四分位数:244.80,437.60)为AI辅助组和364.58(249.70,497.76)CNY为常规组(P?& 0.05)。使用小义可以显着减少患者的等待时间,从而提高医院的门诊服务过程。

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