RAG outperforms long-context prompting for clinical EHR reasoning

A new research paper evaluates retrieval-augmented generation (RAG) against long-context prompting for clinical reasoning tasks using electronic health records (EHRs). The study found that RAG was more token-efficient and performed better on tasks like extracting imaging procedures and reconstructing antibiotic timelines, even with fewer than 8K tokens. While RAG showed clear gains in these areas, the diagnosis generation task remained challenging across all tested methods and models, indicating limitations due to documentation variability. The findings suggest RAG is a competitive and efficient approach for clinical tasks involving large EHR datasets, even as models improve their long-context handling capabilities. AI

IMPACT RAG remains a competitive and efficient approach for clinical tasks over large amounts of EHR, even as newer models become capable of handling increasingly longer amounts of text.

RANK_REASON Research paper evaluating LLM methods for clinical reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Clinical Reasoning
• DeepSeek V3.1
• Electronic Health Records
• GPT-5.4-mini
• Long-Context Input
• Mistral Medium 3
• Retrieval-Augmented Generation
• Skatje Myers

• paper
• other

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