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The process of matching patients with suitable clinical trials is essentialfor advancing medical research and providing optimal care. However, currentapproaches face challenges such as data standardization, ethicalconsiderations, and a lack of interoperability between Electronic HealthRecords (EHRs) and clinical trial criteria. In this paper, we explore thepotential of large language models (LLMs) to address these challenges byleveraging their advanced natural language generation capabilities to improvecompatibility between EHRs and clinical trial descriptions. We propose aninnovative privacy-aware data augmentation approach for LLM-based patient-trialmatching (LLM-PTM), which balances the benefits of LLMs while ensuring thesecurity and confidentiality of sensitive patient data. Our experimentsdemonstrate a 7.32% average improvement in performance using the proposedLLM-PTM method, and the generalizability to new data is improved by 12.12%.Additionally, we present case studies to further illustrate the effectivenessof our approach and provide a deeper understanding of its underlyingprinciples.

Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching