Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital

Xiaoqing Li; Weiyu Liu; Weiming Kong; Wenqing Zhao; Hansong Wang; Dan Tian; Jiali Jiao; Zhangsheng Yu; Shijian Liu

doi:10.21037/tp-23-58

Prediction of outpatient waiting time: using machine learning in a tertiary children's hospital

Transl Pediatr. 2023 Nov 28;12(11):2030-2043. doi: 10.21037/tp-23-58. Epub 2023 Nov 23.

Authors

Xiaoqing Li^#^{1

2}, Weiyu Liu^#³, Weiming Kong³, Wenqing Zhao⁴, Hansong Wang⁵, Dan Tian⁵, Jiali Jiao³, Zhangsheng Yu^{3

6

7

8}, Shijian Liu^{1

2}

Affiliations

¹ Hainan Branch, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Sanya, China.
² School of Public Health, Shanghai Jiao Tong University, Shanghai, China.
³ Center for Biomedical Data Science, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
⁴ Division of Information Department, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
⁵ Division of Hospital Management, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
⁶ Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
⁷ SJTU-Yale Joint Center for Biostatistics and Data Science, Shanghai Jiao Tong University, Shanghai, China.
⁸ Clinical Research Institute, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.

^# Contributed equally.

Abstract

Background: Accurately predicting waiting time for patients is crucial for effective hospital management. The present study examined the prediction of outpatient waiting time in a Chinese pediatric hospital through the use of machine learning algorithms. If patients are informed about their waiting time in advance, they can make more informed decisions and better plan their visit on the day of admission.

Methods: First, a novel classification method for the outpatient clinic in the Chinese pediatric hospital was proposed, which was based on medical knowledge and statistical analysis. Subsequently, four machine learning algorithms [linear regression (LR), random forest (RF), gradient boosting decision tree (GBDT), and K-nearest neighbor (KNN)] were used to construct prediction models of the waiting time of patients in four department categories.

Results: The three machine learning algorithms outperformed LR in the four department categories. The optimal model for Internal Medicine Department I was the RF model, with a mean absolute error (MAE) of 5.03 minutes, which was 47.60% lower than that of the LR model. The optimal model for the other three categories was the GBDT model. The MAE of the GBDT model was decreased by 28.26%, 35.86%, and 33.10%, respectively compared to that of the LR model.

Conclusions: Machine learning can predict the outpatient waiting time of pediatric hospitals well and ease patient anxiety when waiting in line without medical appointments. This study offers key insights into enhancing healthcare services and reaffirms the dedication of Chinese pediatric hospitals to providing efficient and patient-centric care.

Keywords: Waiting time; artificial intelligence (AI); machine learning; pediatric.