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  1. Home
  3. Reducing Endoscopic Procedure Backlog By Improving Efficiency: A Predictive Model And Machine Learning-based Scheduling Approach.
  1. Home
  3. Reducing Endoscopic Procedure Backlog By Improving Efficiency: A Predictive Model And Machine Learning-based Scheduling Approach.

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Reducing endoscopic procedure backlog by improving efficiency: a predictive model and machine learning-based

Tu-San Pham1, Héloïse Gachet2, Waleed Aljohani3

  • 1Polytechnique Montreal, Montréal, QC, H3T 1J4, Canada.

Journal of the Canadian Association of Gastroenterology
|April 30, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new machine learning scheduling tool can reduce patient wait times for endoscopic procedures. This AI-driven system improves resource utilization and patient throughput, helping to clear backlogs caused by the COVID-19 pandemic.

Keywords:
healthcaremachine learningpredictive modelscheduling

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Area of Science:

  • Medical Informatics
  • Operations Research
  • Artificial Intelligence

Background:

  • The COVID-19 pandemic significantly reduced endoscopic procedure volumes, creating substantial patient backlogs.
  • Delays in endoscopic investigations impact patient care and require efficient solutions.

Purpose of the Study:

  • To develop a machine learning-based scheduling tool for endoscopic procedures.
  • To enhance resource utilization, system efficiency, and patient throughput.
  • To mitigate procedural delays and address patient backlogs.

Main Methods:

  • Phase 1: Applied machine learning (XGBoost regression) to predict procedure duration using patient and environmental data.
  • Phase 2: Developed a scheduling module utilizing a greedy heuristic and Mixed Integer Programming (MIP) for resource optimization.

Main Results:

  • XGBoost regression model achieved a mean absolute error of 5.67 minutes for duration prediction.
  • MIP simulations increased patient scheduling by 5.9% and reduced mean waiting time from 19.5 to 17.3 days for 1,000 patients.
  • Real-world data simulations showed MIP scheduling 8 more patients than the baseline, with improved resource utilization.

Conclusions:

  • A machine learning scheduling tool shows significant potential for improving endoscopic procedure backlogs.
  • Clinical validation is required to confirm the tool's effectiveness in real-world settings.
  • Future work includes prospective data collection and integration into clinical workflows for practical utility.