Reimagining the IR Workflow for a Better Work-Life Balance

Area of Science:

• Medical Imaging
• Radiology Workflow Optimization
• Healthcare Management

Background:

• Interventional radiology practices face challenges with scheduling efficiency and physician work-life balance.
• Current workflows may not optimally account for case variability and individual physician performance.

Purpose of the Study:

• To assess the impact of workflow modifications on operational metrics in an academic interventional radiology setting.
• To evaluate the effectiveness of an adaptive learning system for predicting interventional radiology case lengths.

Main Methods:

• Implementation of workflow changes: separation of inpatient/outpatient services, earlier start times, and an adaptive learning system for case length prediction.
• Assessment of key performance indicators: procedural volume, on-time starts, prediction accuracy, and room shutdown times.
• Pre- and post-intervention data analysis to quantify improvements.

Main Results:

• Significant improvements observed in the accuracy of first case start times.
• Enhanced accuracy in predicting procedure block times and last case encounter ending times.
• Positive trends in overall procedural volume and room utilization are suggested.

Conclusions:

• Workflow redesign, including service separation and predictive analytics, can substantially improve interventional radiology operational efficiency.
• Interventional radiologists can achieve greater schedule control, enhanced work hour utilization, and better work-life balance through optimized workflows.
• Adaptive learning systems offer a promising tool for personalized case length prediction in interventional radiology.