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Predicting Waiting Times for Medical Tasks in a Pediatric Hospital Using Machine Learning: Comprehensive,

Lin Lin Guo1, Rui Tang2, Jia Yang Wang2

  • 1Capital Center for Children's Health, Capital Medical University, No.2 Yabao Road, ChaoYang District, Beijing, 100020, China, 86 18380382165.

JMIR Medical Informatics
|September 30, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models can predict pediatric lab test waiting times, but struggle with radiology. Queue-related factors, like patient volume, are key predictors for optimizing hospital efficiency.

Keywords:
machine learningmedical taskspediatric hospitalqueuewaiting time

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Area of Science:

  • Healthcare Operations Research
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Pediatric hospitals in China face significant resource shortages and overcrowding.
  • Accurate waiting time prediction is crucial for optimizing operational efficiency and patient flow.

Purpose of the Study:

  • To develop and evaluate machine learning (ML) models for predicting waiting times for laboratory and radiology examinations in a pediatric hospital.
  • To identify key predictors influencing waiting times using queue theory principles.

Main Methods:

  • Retrospective analysis of 230,864 time-stamped records from a pediatric hospital information system (November 2024 - March 2025).
  • Extraction of queue-related and time-based features guided by queue theory.
  • Training and evaluation of linear regression and 8 ML models using cross-validation and bootstrap methods.
  • Assessment of model performance using Mean Absolute Error, Mean Square Error, Root Mean Square Error, and R²; feature importance analysis via Shapley additive explanations.

Main Results:

  • Median waiting time across all tasks was 4.817 minutes; radiology waiting times were generally longer than laboratory tests.
  • Tree-based ML models (Random Forest, CART) achieved high accuracy (R² 0.880-0.934) for predicting laboratory test waiting times.
  • ML models showed limited success in predicting radiology examination waiting times (R² 0.114-0.719).
  • Feature importance analysis highlighted queue-related predictors, particularly the number of queuing patients, as most significant.

Conclusions:

  • Task-specific ML models are essential for accurate waiting time prediction in diverse medical tasks.
  • Queue theory principles effectively guide the development of ML models for waiting time prediction.
  • Queue-related predictors are critical for improving the accuracy of waiting time predictions in pediatric healthcare settings.