Skyer: a novel benchmark for evaluating the effectiveness of large language models in emergency department triage

Emergency department (ED) overcrowding causes diagnostic challenges, prolonged wait times, and impairs appropriate triage, often due to human error and fatigue. Large language models can assist ED staff in triage, improving patient care by mitigating these problems.

We designed an evaluation method (Skyer benchmark) to assess fifteen large language models, including DeepSeek-R1 (70B, 7B), ChatGPT versions (4, 4.5-preview), Gemini iterations (1.5-pro, 2.0-Pro-experimental, 2.5_03-25, 2.5_05-06), Mistral-7B, Llama-3.3, Gemma models (2-27b-it, 3-12b-it, 3-27b-it), Qwen-2.5, and Phi-4-14B. We assessed the performance of models in ED triage by using 55 realistic clinical pediatric scenarios. The main objective was to evaluate models' triage performance using a weighting system that accounted for the impacts of over-triage and under-triage, in addition to simple accuracy. A secondary objective was to assess models' consistency, by repeating tests across scenarios three times.

Our findings indicate that only two models, ChatGPT-4.5-preview and Gemini-2.5_05-06, demonstrated superior and reliable triage performance. ChatGPT-4.5-preview (77% accuracy, mean weight 377.5 out of 550) and Gemini-2.5_05-06 (74% accuracy, mean weight 365/550) significantly outperformed human-triage-experts accuracy (64% accuracy, mean weight 253.5/550) and other models. This difference was statistically significant (p-value < 0.05), with an extremely large effect size (Cohen's D = 2.18 and 1.98). Furthermore, they demonstrated sufficient reliability due to acceptable consistency in their triage performance (85% and 82%).

Our work establishes Skyer as an evaluation approach of large language models. Skyer selected the best-performing models, which demonstrated significantly higher triage performance than the human experts and showed consistent results. These large language models can streamline the delivery of quality healthcare in overcrowded EDs. Despite promising outcomes, we identified limitations prohibiting these large language models as replacement of human experts. Instead, we demonstrate their potential for a substantial role in assisting the staff in overcrowded EDs.