Outperforming frontier models on emergency department chart generation

A company developed a model to convert emergency department (ED) conversations into structured clinical charts, addressing the complexity and real-time demands of emergency medicine documentation. The model operates in two stages: transforming ED transcripts into structured JSON with 16 sections and merging this with physical exam templates to create the final chart. It outperforms existing models like gemini-2.5-pro in both accuracy and speed, achieving 84.8% accuracy in chart generation and 67.2% in summarization while running 6-8 times faster. The model successfully handles complex routing logic, conditional inclusion, template modification, and high-risk diagnosis detection, areas where general-purpose language models typically fail. By using iterative supervised fine-tuning (iSFT) and creating comprehensive evaluation frameworks with Lumina, the model ensures high precision and robustness. The pipeline was simplified, reducing latency significantly by consolidating multiple prompts and replacing pattern-matching tasks with deterministic code. This model processes tens of thousands of emergency department notes weekly, with an expected increase in usage, demonstrating its scalability and potential impact in emergency settings.