Li Xiaoqing, Tian Dan, Li Weihua, Hu Yabin, Dong Bin, Wang Hansong, Yuan Jiajun, Li Biru, Mei Hao, Tong Shilu, Zhao Liebin, Liu Shijian
School of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China.
School of Public Health, Shanghai Jiao Tong University, Shanghai, China.
Front Pediatr. 2022 Aug 10;10:929834. doi: 10.3389/fped.2022.929834. eCollection 2022.
Complicated outpatient procedures are associated with excessive paperwork and long waiting times. We aimed to shorten queuing times and improve visiting satisfaction.
We developed an artificial intelligence (AI)-assisted program named . A randomized controlled trial was conducted at Shanghai Children's Medical Center. Participants were randomly divided into an AI-assisted and conventional group. was used as a medical assistant in the AI-assisted group. At the end of the visit, an e-medical satisfaction questionnaire was asked to be done. The primary outcome was the queuing time, while secondary outcomes included the consulting time, test time, total time, and satisfaction score. Wilcoxon rank sum test, multiple linear regression and ordinal regression were also used.
We enrolled 740 eligible patients (114 withdrew, response rate: 84.59%). The median queuing time was 8.78 (interquartile range [IQR] 3.97,33.88) minutes for the AI-assisted group versus 21.81 (IQR 6.66,73.10) minutes for the conventional group ( < 0.01), and the AI-assisted group had a shorter consulting time (0.35 [IQR 0.18, 0.99] vs. 2.68 [IQR 1.82, 3.80] minutes, < 0.01), and total time (40.20 [IQR 26.40, 73.80] vs. 110.40 [IQR 68.40, 164.40] minutes, < 0.01). The overall satisfaction score was increased by 17.53% ( < 0.01) in the AI-assisted group. In addition, multiple linear regression and ordinal regression showed that the queuing time and satisfaction were mainly affected by group ( < 0.01), and missing the turn ( < 0.01).
Using AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.
复杂的门诊手术伴随着大量的文书工作和漫长的等待时间。我们旨在缩短排队时间并提高就诊满意度。
我们开发了一个名为 的人工智能辅助程序。在上海儿童医学中心进行了一项随机对照试验。参与者被随机分为人工智能辅助组和传统组。 在人工智能辅助组中用作医疗助手。就诊结束时，要求完成一份电子医疗满意度问卷。主要结果是排队时间，次要结果包括咨询时间、检查时间、总时间和满意度得分。还使用了Wilcoxon秩和检验、多元线性回归和有序回归。
我们招募了740名符合条件的患者（114名退出，响应率：84.59%）。人工智能辅助组的中位排队时间为8.78（四分位间距[IQR]3.97，33.88）分钟，而传统组为21.81（IQR 6.66，73.10）分钟（<0.01），并且人工智能辅助组的咨询时间更短（0.35[IQR 0.18，0.99]对2.68[IQR 1.82，3.80]分钟，<0.01），总时间也更短（40.20[IQR 26.40，73.80]对110.40[IQR 68.40，164.40]分钟，<0.01）。人工智能辅助组的总体满意度得分提高了17.53%（<0.01）。此外，多元线性回归和有序回归表明，排队时间和满意度主要受分组（<0.01）和错过轮次（<0.01）的影响。
使用人工智能简化门诊服务流程可以缩短患者的排队时间并提高就诊满意度。
