SimpliMED: Automatic Simplification of Cardiology Discharge Reports Using Large Language Models

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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