AI for Simulation Operations

Healthcare simulation centers collect extensive operational, educational, and experiential data, yet much of this information is often underutilized beyond accreditation reporting. The authors introduce the CORE Analytics Framework-Compliance, Operations, Results, and Experience-as a structured approach to transform routine simulation data into actionable operational intelligence. Rather than focusing on learner-facing pedagogical applications of artificial intelligence (AI), this framework targets administrative and logistical functions that influence educational quality and sustainability. By integrating correlation analysis, predictive analytics, and AI-assisted data synthesis, the CORE framework helps simulation leaders identify inefficiencies, anticipate resource needs, and support continuous improvement. A case example illustrates how scalable implementation can improve outcomes and optimize resource utilization, positioning operational intelligence as foundational to educational excellence in healthcare simulation.

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Publication Details

Journal
Simulation in Healthcare The Journal of the Society for Simulation in Healthcare
Published
2026-04-03
DOI
https://doi.org/10.1097/sih.0000000000000932
Primary Topic
Simulation-Based Education in Healthcare
Type
article
Field-Weighted Citation Impact
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article

AI for Simulation Operations

Alaina Herrington, Tonya Rutherford-Hemming
Simulation in Healthcare The Journal of the Society for Simulation in Healthcare
Simulation-Based Education in Healthcare
article

AI for Simulation Operations

Alaina Herrington, Tonya Rutherford-Hemming
article en

Abstract

Healthcare simulation centers collect extensive operational, educational, and experiential data, yet much of this information is often underutilized beyond accreditation reporting. The authors introduce the CORE Analytics Framework-Compliance, Operations, Results, and Experience-as a structured approach to transform routine simulation data into actionable operational intelligence. Rather than focusing on learner-facing pedagogical applications of artificial intelligence (AI), this framework targets administrative and logistical functions that influence educational quality and sustainability. By integrating correlation analysis, predictive analytics, and AI-assisted data synthesis, the CORE framework helps simulation leaders identify inefficiencies, anticipate resource needs, and support continuous improvement. A case example illustrates how scalable implementation can improve outcomes and optimize resource utilization, positioning operational intelligence as foundational to educational excellence in healthcare simulation.

Simulation in Healthcare The Journal of the Society for Simulation in Healthcare
East Carolina University (US), Mississippi State University (US)
Openalex Percentile: Top 22%
Simulation-Based Education in Healthcare
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AI for Simulation Operations — Alaina Herrington, Tonya Rutherford-Hemming · Simulation in Healthcare The Journal of the Society for Simulation in Healthcare (2026) | TGRS Research Map | TGRS