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Value for money in self-regulated procedural simulation.

Alexandre Lafleur1,2, Gabriel Demchuk1, Marie-Laurence Tremblay3

  • 1Vice-décanat à la pédagogie et au développement professionnel continu, Faculty of Medicine, Université Laval, Québec, Canada.

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Summary
This summary is machine-generated.

Determining optimal simulator-to-participant ratios is key for cost-effective medical simulation training. This study provides a replicable method to calculate these ratios, improving resource allocation and purchase decisions for simulation programs.

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Area of Science:

  • Medical Education
  • Simulation-Based Learning
  • Health Professions Education

Background:

  • Self-regulated procedural simulation allows learners to practice skills on various simulators.
  • Current needs assessments lack the predictive power for cost-effective simulation resource planning.
  • Establishing simulator-to-participant ratios is crucial for informed purchasing decisions.

Purpose of the Study:

  • To develop a method for calculating optimal simulator-to-participant ratios for self-regulated procedural simulation.
  • To inform cost-effective planning and resource allocation in simulation-based training programs.

Main Methods:

  • Designed 90-minute self-regulated procedural simulation sessions for internal medicine residents.
  • Phase 1 involved 51 participants using 22 simulators across various procedures.
  • Calculated minimal simulator numbers based on participant usage time to inform Phase 2 resource planning.

Main Results:

  • Optimal simulator ratios per 10 participants were determined for specific procedures, e.g., 9.2:3.7 for central venous catheterization.
  • Phase 2, with reduced simulators, saw a significant increase in simulator usage rate from 35.5% to 76.6%.
  • Total simulator usage time was maintained at approximately 80.4 minutes.

Conclusions:

  • A replicable method for cost-effective simulation planning based on simulator usage measurement is presented.
  • Simulator-to-participant ratios derived from this method can guide purchasing decisions.
  • This approach supports resource optimization for simulation programs.