@inproceedings{molino-pinar-etal-2026-simplimed,
    title = "{S}impli{MED}: Automatic Simplification of Cardiology Discharge Reports Using Large Language Models",
    author = "Molino-Pi{\~n}ar, Lucas  and
      D{\'i}az Galiano, Manuel Carlos  and
      Mart{\'i}n-Valdivia, Mar{\'i}a-Teresa  and
      Urbano-Moral, Jose Angel  and
      Sola-Garcia, Elena",
    editor = "Gupta, Deepak  and
      Thompson, Paul  and
      Ananiadou, Sophia  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/cawl-year/2026.cl4health-1.7/",
    doi = "10.63317/53snqjnk47xn",
    pages = "72--81",
    abstract = "Medical discharge reports frequently contain highly technical language that creates significant communication barriers between healthcare professionals and patients, potentially compromising treatment adherence and post-discharge care quality. In this paper, we present SimpliMED, a modular system designed to automatically simplify cardiology discharge reports using Large Language Models (LLMs) and advanced Natural Language Processing techniques (NLP). Our architecture integrates section-based preprocessing with specialized prompts, explicit handling of medical abbreviations, and therapeutic explanations of medications to enhance accessibility. We evaluate our system using a corpus of 307 anonymized cardiology discharge reports from a Spanish medical center. For abbreviation detection, our fine-tuned Small Language Model (SLM) achieves an F1-score of 0.90, significantly outperforming regex-based approaches (F1: 0.67). For medication recognition, we achieve F1-scores of 0.91 for commercial names and 0.70 for active principles. We also contribute a therapeutic dictionary containing 14,611 medications with patient-friendly explanations extracted from the Spanish Agency of Medicines. Expert evaluation by two cardiologists yields an overall quality score of 75{\%}, with highest performance for admission reason (91{\%}) and current illness (75{\%}) sections. While results demonstrate the potential of LLM-based medical text simplification for Spanish clinical language, we identify areas requiring further development before clinical deployment."
}