AI for Simulation Operations: A Framework for Administrative and Logistical Excellence

Area of Science:

• Healthcare Simulation
• Educational Data Analytics
• Operational Intelligence

Background:

• Healthcare simulation centers generate vast amounts of data, often underutilized beyond accreditation.
• Current data analysis typically focuses on learner outcomes, neglecting operational aspects.
• There is a need for structured methods to leverage simulation data for administrative improvement.

Purpose of the Study:

• Introduce the CORE Analytics Framework (Compliance, Operations, Results, Experience).
• Demonstrate how to transform routine simulation data into actionable operational intelligence.
• Target administrative and logistical functions impacting educational quality and sustainability.

Main Methods:

• Developed the CORE Analytics Framework.
• Integrated correlation analysis, predictive analytics, and AI-assisted data synthesis.
• Applied the framework to a case example for implementation illustration.

Main Results:

• The CORE framework facilitates identification of operational inefficiencies.
• Predictive analytics aid in anticipating resource needs.
• AI-assisted synthesis supports continuous quality improvement in simulation centers.

Conclusions:

• Operational intelligence derived from the CORE framework is foundational to educational excellence.
• Scalable implementation of the CORE framework optimizes resource utilization and improves outcomes.
• The framework shifts focus from solely pedagogical applications to administrative and operational enhancement.