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

Abstract

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. Requests were batched into groups of four, and the LLM was prompted to identify which of the four patients should be prioritized. The same groupings of four requests were then shown to a panel of experienced critical care paramedics who voted on which patient should be prioritized.

Results: Across 98 groupings of four ambulance requests (392 total requests), the LLM agreed with the paramedic panel in most cases (76.5 %, n = 75). In groupings where the paramedic panel was unanimous in their decision (n = 48), the LLM agreed with the unanimous panel in 93.8 % of groupings (n = 45).

Conclusions: Our preliminary analysis indicates LLMs may have the potential to become a useful tool for triage and resource allocation in emergency care settings, especially in cases where there is consensus among subject matter experts. Further research is needed to better understand and clarify how they may best be of service.