AUTHOR=Li Xiaoqing , Tian Dan , Li Weihua , Hu Yabin , Dong Bin , Wang Hansong , Yuan Jiajun , Li Biru , Mei Hao , Tong Shilu , Zhao Liebin , Liu Shijian 

TITLE=Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial

JOURNAL=Frontiers in Pediatrics

VOLUME=10

YEAR=2022

URL=https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2022.929834

DOI=10.3389/fped.2022.929834

ISSN=2296-2360

ABSTRACT=<sec><title>Introduction</title><p>Complicated outpatient procedures are associated with excessive paperwork and long waiting times. We aimed to shorten queuing times and improve visiting satisfaction.</p></sec><sec><title>Methods</title><p>We developed an artificial intelligence (AI)-assisted program named <italic>Smart-doctor</italic>. A randomized controlled trial was conducted at Shanghai Children’s Medical Center. Participants were randomly divided into an AI-assisted and conventional group. <italic>Smart-doctor</italic> 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.</p></sec><sec><title>Results</title><p>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 (<italic>p</italic> &lt; 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, <italic>p</italic> &lt; 0.01), and total time (40.20 [IQR 26.40, 73.80] vs. 110.40 [IQR 68.40, 164.40] minutes, <italic>p</italic> &lt; 0.01). The overall satisfaction score was increased by 17.53% (<italic>p</italic> &lt; 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 (<italic>p</italic> &lt; 0.01), and missing the turn (<italic>p</italic> &lt; 0.01).</p></sec><sec><title>Conclusions</title><p>Using AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.</p></sec>