Frontiers in Pediatrics (Aug 2022)

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

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

DOI
https://doi.org/10.3389/fped.2022.929834
Journal volume & issue
Vol. 10

Abstract

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

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