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Building an Adaptable Pediatric Intensive Care Unit Simulation Portfolio: Advancing Efficiency, Flexibility, and

Daniel Loeb1,2, Kelly Ely3, Kelly Collins2

  • 1From the University of Cincinnati College of Medicine, Cincinnati, Ohio.

Pediatric Quality & Safety
|May 15, 2026
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Summary
This summary is machine-generated.

A new pediatric intensive care unit (PICU) simulation program increased training frequency by 128% and reduced cancellations by 25%. This scalable model enhances team performance and patient safety through predictive analytics and multidisciplinary collaboration.

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Area of Science:

  • Medical Education
  • Healthcare Quality Improvement
  • Simulation Technology

Background:

  • Simulation-based education is crucial for enhancing patient safety and team performance in high-acuity settings like pediatric intensive care units (PICUs).
  • Implementing scalable and dynamic simulation programs that adapt to changing clinical demands remains a significant challenge for many units.
  • Existing simulation models often lack the flexibility to accommodate fluctuations in patient volume, clinical acuity, and staffing.

Purpose of the Study:

  • To develop and implement a comprehensive PICU simulation portfolio.
  • To integrate predictive analytics, multidisciplinary collaboration, and real-time clinical data into simulation training.
  • To increase the frequency of simulations and training exposures within the PICU.

Main Methods:

  • A quality improvement initiative utilized a specific, measurable, achievable, relevant, and time-bound (SMART) aim and a key driver diagram.
  • Iterative plan-do-study-act (PDSA) cycles informed interventions, including standardized simulation types and a decision tree for real-time analytics.
  • Key interventions involved multidisciplinary team integration, resource centralization, and process streamlining for simulation setup.

Main Results:

  • Annual simulations increased by 128%, engaging 1,256 staff members.
  • Simulation cancellations decreased by 25%, indicating improved program reliability.
  • Efficiency gains were observed with a 58% reduction in setup time and a 64% reduction in takedown time; no safety events were reported.

Conclusions:

  • The developed PICU simulation training model is scalable and adaptable, demonstrating significant improvements in efficiency and training capacity.
  • The integration of predictive analytics and multidisciplinary collaboration enhances simulation program effectiveness.
  • This innovative framework offers a promising model for other clinical settings seeking to improve simulation-based education and patient care outcomes.