Publications

Below is a growing list of our publications exploring how large language models can support emergency department teams.

• EDSim: An Agentic Simulator for Emergency Department Operations
  npj Digital Medicine – Submitted
  Authors: Jiajun Wu, Hutton Ledingham, Zirui Wang, Braden Teitge, Alexander Burn, Oussama Ouadihi, Mohamad Ghattas, Darin Vicaldo, Sergiu Cociuba, Megan Harmon, Tanvir Chowdhury, Zack Marshall, Tyler Williamson, Tracie Risling, Eddy Lang, Jiayu Zhou, Jessalyn Holodinsky, Steve Drew
  Introduces an agentic emergency department simulator that combines LLM-driven clinician and patient behavior with data-driven operational modeling for workflow experiments.

• Fine-tuned large language models guided by physician feedback can improve patient chart summarization for emergency departments
  ICEM 2025 – International Conference on Emergency Medicine – Conference Presentation
  Authors: Jiajun Wu, Hanzhe Wei, Braden Teitge, Jessalyn Holodinsky, Kyle Exner, Steve Drew
  Demonstrates how clinician-in-the-loop fine-tuning boosts the quality and utility of ED chart summaries.

• Small Language Models for Emergency Departments Decision Support: A Benchmark Study
  Accepted to 2025 IEEE International Conference on Autonomous and Trusted Computing (ATC 2025) – Conference Paper
  Authors: Zirui Wang, Jiajun Wu, Braden Teitge, Jessalyn Holodinsky, Steve Drew
  Evaluates compact models for ED decision-support tasks, highlighting trade-offs between accuracy, latency, and deployability.

• Dual-stage and Lightweight Patient Chart Summarization for Emergency Physicians
  Accepted at IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT) 2025 – Conference Paper
  Authors: Jiajun Wu, Swaleh Zaidi, Braden Teitge, Henry Leung, Jiayu Zhou, Jessalyn Holodinsky, Steve Drew
  Introduces a two-step summarization pipeline that balances comprehension with speed for frontline clinicians.