Leveraging Large Language Models to Enhance Emotional Intelligence Training in Anesthesiology.

Area of Science:

• Anesthesiology
• Medical Education
• Artificial Intelligence

Background:

• Emotional intelligence (EI) is crucial for anesthesiologists in high-stakes perioperative communication.
• Stressors like fatigue and heavy workloads can impair EI during critical patient care.
• Structured training is necessary to strengthen EI in clinical practice.

Purpose of the Study:

• To explore the potential of large language models (LLMs) as a novel tool for enhancing emotional intelligence training in anesthesiology.
• To investigate how LLMs can be utilized to create tailored simulation scenarios for improving clinician communication skills.

Main Methods:

• LLMs were utilized to generate diverse, on-demand simulation scenarios.
• Scenarios were designed to be fine-tuned and validated by experts for pedagogical use.
• LLMs facilitated the creation of tailored training modules for emotionally charged conversations.

Main Results:

• LLMs demonstrated the capability to generate empathetic responses suitable for training.
• The technology allows for the creation of customizable simulation scenarios for various teaching methods.
• LLMs can provide accessible supplemental tools for bolstering clinicians' EI skills.

Conclusions:

• Large language models present an innovative and accessible method for supplementing emotional intelligence training in anesthesiology.
• LLM-generated simulations can effectively prepare clinicians for challenging interpersonal interactions.
• While not replacing human-led training, LLMs offer a valuable adjunct for enhancing communication and EI in perioperative settings with expert oversight.