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An agent-based modelling tool (ABMT) for scheduling diagnostic imaging machines.
Brendan Eagen1, Richard Caron, Walid Abdul-Kader
1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Summary
This study introduces an Agent-Based Modelling Tool (ABMT) for optimizing patient scheduling in diagnostic imaging. The ABMT effectively identified an unexpected rise in patient arrivals, proving its value for facility management.
Area of Science:
- Operations Research
- Health Informatics
- Computer Science
Background:
- Diagnostic imaging facilities face challenges in patient scheduling due to variable patient flow and resource availability.
- Efficient scheduling is crucial for minimizing wait times and maximizing resource utilization.
Purpose of the Study:
- To develop and validate an Agent-Based Modelling Tool (ABMT) for patient scheduling at diagnostic imaging facilities.
- To assess the ABMT's capability in managing complex scheduling scenarios, including multiple imaging devices and patient classes.
Main Methods:
- Development of an Agent-Based Modelling Tool (ABMT) incorporating patient classes, imaging devices, and server availability.
- Testing the ABMT using real-world data and expert input from a Canadian hospital.
Main Results:
- The ABMT successfully simulated patient scheduling processes within a diagnostic imaging setting.
- The tool identified a previously undetected increase in patient arrivals, demonstrating its sensitivity and utility.
Conclusions:
- The Agent-Based Modelling Tool (ABMT) is an effective management tool for diagnostic imaging facilities.
- The ABMT can enhance operational efficiency by detecting and responding to changes in patient demand.
