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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.

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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.