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Identifying Areas for Operational Improvement and Growth in IR Workflow Using Workflow Modeling, Simulation, and

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Summary

Discrete event simulation (DES) and optimization identified workflow bottlenecks in interventional radiology (IR). This approach improved patient care and staff efficiency by reallocating resources and extending operational hours.

Keywords:
Discrete event simulationInterventional radiology departmentOperational improvementOptimizationSimulationSimulation-based optimizationWhat-if scenario analysisWorkflow modeling

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Area of Science:

  • Medical Imaging and Radiology
  • Operations Research
  • Healthcare Management

Background:

  • Interventional Radiology (IR) departments face challenges in optimizing workflows due to patient arrival uncertainties, staff availability, and procedure time variability.
  • Traditional methods like Lean and Six Sigma address strategic workflow improvements but often fall short in tactical day-to-day operational efficiency.
  • Inefficient workflows can lead to delayed patient care, staff overtime, and reduced competitiveness.

Purpose of the Study:

  • To present an alternative approach using discrete event simulation (DES) and simulation-based optimization to address both tactical and strategic needs in IR workflow management.
  • To identify specific workflow bottlenecks and validate potential improvement strategies through simulation modeling.
  • To enhance patient care delivery and operational efficiency within an interventional radiology department.

Main Methods:

  • A comprehensive digital model of the patient workflow was developed using expert interviews and 192 days of electronic medical record (EMR) data.
  • Patient arrival patterns and process times were analyzed from 4393 individual patient appointments, modeling 196 unique procedures.
  • Dynamic staff schedules and rule-based procedure-room mapping were incorporated into the stochastic simulation model.

Main Results:

  • Stochastic simulations identified the computed tomography (CT) suite as a major workflow bottleneck during morning hours.
  • The study led to the reallocation of diagnostic CT scanner time blocks to the IR group, optimizing resource utilization.
  • Simulation-based optimization informed the extension of daily operating hours by 2.5 hours and the development of new staff schedules.

Conclusions:

  • The combination of workflow modeling, stochastic simulations, and optimization techniques is a viable and effective approach for identifying inefficiencies in interventional radiology.
  • This methodology allows for the discovery and validation of improvement options through 'what-if' scenario testing.
  • The implemented changes resulted in improved resource allocation, extended service availability, and optimized staff scheduling, enhancing overall departmental efficiency.