@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T SimpliMED: Automatic Simplification of Cardiology Discharge Reports Using Large Language Models
%A Molino-Piñar, Lucas
%A Díaz Galiano, Manuel Carlos
%A Martín-Valdivia, María-Teresa
%A Urbano-Moral, Jose Angel
%A Sola-Garcia, Elena
%Y Gupta, Deepak
%Y Thompson, Paul
%Y Ananiadou, Sophia
%Y Demner-Fushman, Dina
%S Proceedings of the Third Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F molino-pinar-etal-2026-simplimed
%X 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.
%R 10.63317/53snqjnk47xn
%U https://aclanthology.org/2026.cl4health-1.7/
%U https://doi.org/10.63317/53snqjnk47xn
%P 72-81
Markdown (Informal)
[SimpliMED: Automatic Simplification of Cardiology Discharge Reports Using Large Language Models](https://aclanthology.org/2026.cl4health-1.7/) (Molino-Piñar et al., CL4Health 2026)
ACL