Outperforming frontier models on emergency medicine documentation

A company specializing in converting emergency department (ED) conversations into structured clinical charts faced the challenge of creating a model that could manage the complexity of emergency medicine documentation in real-time. This task required transforming ED transcripts into structured JSON and then merging this with physical exam templates to produce accurate charts. The project involved developing a model that exceeded the performance of existing models by achieving higher accuracy and speed, specifically outperforming the gemini-2.5-pro model. To achieve these results, the team implemented a simplified two-stage pipeline and used iterative SFT (iSFT) training with dense feedback, allowing the model to handle high-risk diagnoses and make precise edits to documentation templates. The model's success was bolstered by comprehensive evaluation frameworks and a focus on multi-stage robustness, enabling it to process tens of thousands of ED notes weekly and expected to expand further. This innovative approach ensures both speed and accuracy in emergency documentation, addressing the critical need for precise application of clinical rules and semantic understanding in regulated medical environments.