Generative Adversarial Networks for Synthetic Electronic Health Record Data Generation and Privacy Preservation in Healthcare Systems

Abstract

The adoption of electronic health records (EHRs) has revolutionized healthcare data management, enabling large-scale clinical analytics, precision medicine, and population health monitoring. However, concerns regarding patient privacy, regulatory compliance, and data scarcity hinder the use of EHRs for research and model development. Generative Adversarial Networks (GANs), a class of deep learning models capable of producing realistic synthetic data, offer a compelling solution to these challenges. This study investigates the design, implementation, and evaluation of GAN-based frameworks for generating synthetic EHR data while preserving patient privacy and supporting downstream machine learning applications. Leveraging multimodal healthcare datasets, including structured clinical codes, laboratory values, and temporal treatment sequences, we demonstrate that GANs can synthesize high-fidelity data that accurately mimics real-world distributions. Furthermore, we explore privacy-preserving strategies, including differential privacy and adversarial regularization, to mitigate disclosure risks. Experimental results indicate that GAN-generated synthetic EHRs maintain statistical properties and predictive utility comparable to real datasets, enabling robust model training without compromising sensitive patient information. This research advances the integration of AI-driven synthetic data generation in healthcare, providing scalable solutions for secure data sharing, research reproducibility, and privacy-preserving analytics.