Generating Reliable Synthetic Clinical Discharge Summaries for Medical Text Analysis

Abstract

Clinical text is an important part of healthcare systems because it is used to store and manage patient information in documents such as discharge summaries, doctor notes, and diagnostic reports. Among these documents, discharge summaries are especially important because they provide a brief overview of a patient’s diagnosis, treatment procedures, medications, and follow-up instructions after hospitalization. These summaries are also useful for healthcare research and medical data analysis. However, strict privacy regulations and hospital policies restrict access to real clinical records, making it difficult for researchers to collect large datasets for developing and testing machine learning models in healthcare. To address this issue, this study proposes a framework for generating and validating synthetic clinical discharge summaries using transformer-based biomedical language models. Initially, the clinical text is preprocessed using cleaning, formatting, and tokenization techniques to improve consistency and readability. Biomedical language models such as BioBERT, RoBERTa, and DistilBERT are then used to generate contextual embeddings and capture semantic relationships within medical text. In addition, semantic similarity analysis, entailment-based validation, and faithfulness evaluation are applied to verify the consistency and reliability of the generated summaries while preserving patient privacy and maintaining clinical relevance.