Evidence map›Paper›PMID 36034568›Full record

ArticleFrontiers in pediatrics2022

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

Xiaoqing Li, Dan Tian, Weihua Li, Yabin Hu, Bin Dong, Hansong Wang, Jiajun Yuan, Biru Li, Hao Mei, Shilu Tong and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Xiaoqing LiSchool of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China.
Dan TianDivision of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Weihua LiDivision of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yabin HuSchool of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China.
Bin DongDivision of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Hansong WangDivision of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiajun YuanDivision of Hospital Management, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Biru LiDepartment of Pediatric Internal Medicine, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Hao MeiSchool of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China.
Shilu TongSchool of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China.
Liebin ZhaoShanghai Engineering Research Center of Intelligence Pediatrics (SERCIP), Shanghai, China.
Shijian LiuSchool of Medicine, Shanghai Children's Medical Center, Child Health Advocacy Institute, Shanghai Jiao Tong University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 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 ( Conclusions: Using AI to simplify the outpatient service procedure can shorten the queuing time of patients and improve visit satisfaction.

Indexed as

artificial intelligencequeueing timerandomized controlled trialsatisfactionwaiting time

Identifiers

PMID36034568
PMCPMC9399636

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.