Local LLM Pipeline Achieves Strong Performance on Medical Data Extraction Task
A research team from sebis developed a two-stage, fully local large language model pipeline using MedGemma-27B to automatically fill Case Report Forms from unstructured electronic health record notes. The system separates binary presence classification from value extraction and uses few-shot in-context learning without external API calls or model fine-tuning. The approach achieved a macro-F1 score of 0.55, placing second among locally-hosted open-source submissions at the CL4Health 2026 shared task, suggesting privacy-preserving pipelines can approach the performance of proprietary models.
Researchers at sebis presented a clinical NLP pipeline designed to address the challenge of extracting structured information from unstructured electronic health record (EHR) notes while preserving patient data privacy. The system uses MedGemma-27B, a domain-adapted language model, deployed entirely on-premise without reliance on external APIs or cloud services. Its two-stage architecture first classifies whether a clinical item is present in a note, then extracts its value, enforcing strict grounding in textual evidence to reduce hallucination. Item-specific few-shot in-context learning is used to guide the model without requiring fine-tuning. The pipeline was evaluated on the CRF Filling 2026 English test track, part of the CL4Health workshop at LREC 2026, achieving a macro-F1 of 0.55 and finishing second among open-source, locally-hosted submissions. The authors argue this demonstrates that data-sovereign, on-premise LLM deployments can achieve near-competitive performance compared to proprietary frontier models, offering a practical framework for privacy-sensitive clinical settings.
What's missing
The paper does not report the macro-F1 scores of the top-performing proprietary or frontier model submissions, making it difficult to quantify the actual performance gap between the local pipeline and state-of-the-art alternatives.
What different sources said
- arXiv cs.CLCenter
sebis at CRF Filling 2026: A Two-Stage Local LLM Pipeline for Medical CRF Filling
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