Use of a large language model (LLM) for ambulance dispatch and triage.

Introduction: Large language models (LLMs) have grown in popularity in recent months and have demonstrated advanced clinical reasoning ability. Given the need to prioritize the sickest patients requesting emergency medical services (EMS), we attempted to identify if an LLM could accurately triage ambulance requests using real-world data from a major metropolitan area.

Methods: An LLM (ChatGPT 4o Mini, Open AI, San Francisco, CA, USA) with no prior task-specific training was given real ambulance requests from a major metropolitan city in the United States.

Changing the Culture to Improve CCF: An Improvement Project.

Objectives: After identifying chest compression fraction (CCF) as a key area for improvement, our Emergency Medical Services (EMS) agency aimed to improve our baseline monthly median CCF from 81.5% to 90% or more in paramedic-attended medical cardiac arrests by December 2023. The CCF is a process measure that, if improved, has been shown to increase likelihood of survival from cardiac arrest.

Infant out-of-hospital cardiac arrest during nights and weekends.

Background: A growing body of evidence suggests outcomes for cardiac arrest in adults are worse during nights and weekends when compared with daytime and weekdays. Similar research has not yet been carried out in the infant setting.

Methods: We examined the National Emergency Medical Services Information System (NEMSIS), a database containing millions of emergency medical services (EMS) runs in the United States.