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Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation.

Ivo Baltruschat1,2,3,4, Leonhard Steinmeister5, Hannes Nickisch6

  • 1Institute for Biomedical Imaging (E5), Hamburg University of Technology, Hamburg, Germany. ivo-matteo.baltruschat@tuhh.de.

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Summary
This summary is machine-generated.

Artificial intelligence (AI) can optimize radiology workflows by prioritizing urgent chest X-rays (CXRs), significantly reducing average report turnaround times (RTATs) for critical findings. Implementing a maximum waiting time threshold further mitigates risks associated with AI false negatives.

Keywords:
Artificial intelligenceRadiographyWaiting listsWorkflow

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Area of Science:

  • Radiology Workflow Optimization
  • Artificial Intelligence in Medical Imaging
  • Clinical Decision Support Systems

Background:

  • Current radiology workflows often use a first-in, first-out (FIFO) system, which may delay critical findings.
  • Artificial intelligence (AI) offers potential for intelligent worklist prioritization to improve efficiency.
  • False negative predictions by AI could lead to dangerously long report turnaround times (RTATs).

Purpose of the Study:

  • To evaluate AI-driven smart worklist prioritization for optimizing radiology workflows.
  • To assess the impact of AI on reducing RTATs for critical findings in chest radiographs (CXRs).
  • To investigate methods for mitigating risks of prolonged RTATs due to AI false negatives.

Main Methods:

  • Development of a realistic clinical workflow simulation framework using hospital-specific data.
  • Comparison of standard FIFO worklist processing with AI-based urgency prioritization.
  • Implementation of an 'upper limit' for maximum waiting time to assign highest urgency.

Main Results:

  • AI prioritization significantly reduced average RTAT for critical findings compared to FIFO (e.g., pneumothorax: 35.6 min vs. 80.1 min).
  • Maximum RTAT increased in some cases without an upper limit, highlighting the risk of false negatives.
  • The 'upper limit' strategy substantially reduced maximum RTATs across all critical finding classes.

Conclusions:

  • AI-powered smart worklist prioritization effectively reduces average RTAT for critical CXR findings.
  • An upper limit on waiting time is crucial to prevent excessively long RTATs caused by AI false negatives.
  • AI can achieve near-optimal performance, comparable to perfect classification, in reducing critical finding RTATs.