AI-Driven Workflow Optimization in Emergency Radiology: Reducing Reporting Time for Trauma Cases

Abstract

Background:
Timely radiology reporting is crucial for trauma management in emergency departments (EDs), directly influencing patient outcomes. However, traditional workflows often result in delayed turnaround times, especially for critical cases.

Purpose:
To evaluate the impact of an artificial intelligence (AI)-driven workflow optimization platform on reporting turnaround time (TAT) for trauma CT scans in a tertiary care ED.

Methods:
A two-phase prospective audit was conducted, analyzing 100 trauma CT cases (50 pre- and 50 post-intervention). The primary outcome was mean reporting time. Secondary outcomes included the proportion of critical cases reported within 30 and 60 minutes.

Results:
After AI implementation, mean reporting time decreased from 87.0±27.3 to 35.5±13.7 minutes (p<0.001). The proportion of critical cases reported within 30 minutes increased from 18.2% to 74.1% (p<0.001), and within 60 minutes from 34.6% to 92.6% (p<0.001). Compliance with best-practice timeframes improved dramatically.

Conclusion:
AI-driven workflow optimization significantly reduces reporting turnaround times and increases compliance for critical trauma cases in emergency radiology. These findings support broader adoption of AI solutions to improve patient care in EDs.