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Applying queueing theory to evaluate wait-time-savings of triage algorithms
Yee Lam Elim Thompson1, Gary M Levine1, Weijie Chen1
1The U.S. Food and Drug Administration, White Oak, MD USA.
Queueing Systems
|October 25, 2024
Summary
Computer-aided triage (CADt) software can reduce patient wait times in radiology. This study uses queueing theory to quantify wait-time savings, showing CADt is most effective in busy, understaffed settings.
Area of Science:
- Medical Imaging and Informatics
- Artificial Intelligence in Healthcare
- Operations Research
Background:
- Artificial intelligence (AI) shows promise in healthcare, with computer-aided triage and notification (CADt) software designed to prioritize urgent radiological cases.
- While CADt deployment improves patient outcomes, quantitative methods for evaluating its impact on wait times are lacking.
Purpose of the Study:
- To quantitatively evaluate the wait-time savings achieved by deploying CADt software in radiology workflows.
- To develop and validate a methodology for assessing the performance of AI-driven prioritization tools.
Main Methods:
- Applied queueing theory to model radiology workflows with and without CADt implementation.
- Calculated average patient image waiting times under various AI performance, radiologist reading speeds, and image arrival rates.
- Developed a simulation tool to verify theoretical results and provide confidence intervals for performance metrics.
Main Results:
- Quantitatively demonstrated wait-time reductions attributable to CADt deployment.
- Found CADt to be most effective in high-volume, resource-limited (e.g., short-staffed) radiology reading environments.
- Simulation results aligned with theoretical predictions, confirming the methodology's validity.
Conclusions:
- Queueing theory provides a robust framework for evaluating the time-saving benefits of CADt systems.
- CADt is a valuable tool for optimizing radiology workflows, particularly in settings facing high demand and limited staffing.
- The presented evaluation methodology is adaptable for assessing other AI prioritization algorithms in various service industries.
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