Exploring the Use of a Large Language Model in Simulation Debriefing: An Observational Simulation-Based Pilot Study.

Journal: Simulation in healthcare : journal of the Society for Simulation in Healthcare
Published Date: May 13, 2025

Abstract

INTRODUCTION: Facilitating debriefings in simulation is a complex task with high task load. The increasing availability of generative artificial intelligence (AI) offers an opportunity to support facilitators. We explored simulation facilitation and debriefing strategies using a large language model (LLM) to decrease facilitators' task load and allow for a more comprehensive debrief.

Authors

  • Eury Hong
    From the Department of Pediatrics (E.H., B.D.), Yale University School of Medicine, New Haven, CT; Department of Pediatrics (S.K.), Sections of Safety, Advocacy and Healing and General Pediatrics, Yale University School of Medicine, New Haven, CT; Department of Pediatrics (M.A., S.A., T.M.W., I.T.G.), Section of Emergency Medicine, Yale University School of Medicine, New Haven, CT; Department of Emergency Medicine (M.A., T.M.W.), Yale University School of Medicine, New Haven, CT; Yale University School of Medicine (M.R., A.S.R.), New Haven, CT; Department of Pediatrics (R.H., L.J.), Section of Neonatal-Perinatal Medicine, Yale University School of Medicine, New Haven, CT; Department of Anesthesiology, Critical Care and Pain Medicine (T.A.W.), Boston Children's Hospital, Boston, MA; and Harvard Medical School (T.A.W.), Boston, MA.
  • Sundes Kazmir
  • Benjamin Dylik
  • Marc Auerbach
  • Matteo Rosati
  • Sofia Athanasopoulou
  • Russell Himmelstein
  • Travis M Whitfill
  • Lindsay Johnston
  • Traci A Wolbrink
  • Arielle Shibi Rosen
  • Isabel T Gross

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