Evaluating large language models on hospital health data for automated emergency triage

Large language models (LLMs) have a significant potential in healthcare due to their ability to process unstructured text from electronic health records (EHRs) and to generate knowledge with few or no training. In this study, we investigate the effectiveness of LLMs for clinical decision support, specifically in the context of emergency department triage, where the volume of textual data is minimal compared to other scenarios such as making a clinical diagnosis.

We benchmark LLMs with traditional machine learning (ML) approaches using the Emergency Severity Index (ESI) as the gold standard criteria of triage. The benchmark includes general purpose, specialised, and fine-tuned LLMs. All models are prompted to predict ESI score from a EHRs. We use a balanced subset (n = 1000) from MIMIC-IV-ED, a large database containing records of admissions to the emergency department of Beth Israel Deaconess Medical Center.

Our findings show that the best-performing models have an average F1-score below 0.60. Also, while zero-shot and fine-tuned LLMs can outperform standard ML models, their performance is surpassed by ML models augmented with features derived from LLMs or knowledge graphs.

LLMs show value for clinical decision support in scenarios with limited textual data, such as emergency department triage. The study advocates for integrating LLM knowledge representation to improve existing ML models rather than using LLMs in isolation, suggesting this as a more promising approach to enhance the accuracy of automated triage systems.