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Li X, Tian D, Li W, Hu Y, Dong B, Wang H, Yuan J, Li B, Mei H, Tong S, Zhao L, Liu S. Using artificial intelligence to reduce queuing time and improve satisfaction in pediatric outpatient service: A randomized clinical trial. Front Pediatr 2022; 10:929834. [PMID: 36034568 PMCID: PMC9399636 DOI: 10.3389/fped.2022.929834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION Complicated outpatient procedures are associated with excessive paperwork and long waiting times. We aimed to shorten queuing times and improve visiting satisfaction. METHODS We 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. RESULTS 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 (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). CONCLUSIONS Using AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.
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Affiliation(s)
- Xiaoqing Li
- 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
| | - Dan Tian
- Division of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, Shanghai, China.,Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, China
| | - Weihua Li
- Division of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, Shanghai, China.,Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, China
| | - Yabin Hu
- School of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Dong
- Division of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, Shanghai, China.,Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, China
| | - Hansong Wang
- Division of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, Shanghai, China.,Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, China
| | - Jiajun Yuan
- Division of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Pediatric AI Clinical Application and Research Center, Shanghai Children's Medical Center, Shanghai, China.,Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, China
| | - Biru Li
- Department of Pediatric Internal Medicine, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hao Mei
- School of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China.,Department of Data Science, School of Population Health, University of Mississippi Medical Center, Jackson, MS, United States
| | - Shilu Tong
- School of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China.,School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia
| | - Liebin Zhao
- Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, China
| | - Shijian Liu
- 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
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