Generating Readable Spanish Clinical Narratives from Structured Electronic Health Records with Multi-Agent LLMs

Resumen

Electronic Health Records (EHRs) provide longitudinal, interoperable digital histories of patient medical events across healthcare settings. However, the vast quantity of structured data and its tabular format often complicate the synthesis of a clear clinical overview. The primary objective of this work is to generate coherent Spanish clinical narratives that summarize patient trajectories, improving interpretability for clinicians and integration into decision workflows. We propose a multi-agent architecture where specialized Large Language Model (LLM) agents transform distinct clinical structured variables into a unified narrative. We compare three models with varying domain and language specialization: MedGemma-27B, Marmoka-es+it, and LLaMA 3.1. Performance is assessed through a dual evaluation framework combining expert manual annotation with an LLM-as-a-judge approach. Our results highlight the critical role of domain and language adaptation in clinical narrative generation while addressing the lack of specialized resources for Spanish clinical narratives.