Information Governance Framework for AI-Generated Synthetic Patient Data in Healthcare Research: Balancing Utility, Privacy and Algorithmic Bias Mitigation

Abstract

This study develops and validates an information governance framework for AI-generated synthetic patient data in healthcare research, balancing data utility, privacy, and algorithmic bias mitigation. The introduction positions the framework as a response to stringent privacy regulations and data scarcity, integrating dynamic consent, differential privacy, and fairness-aware synthetic data generation. The literature review synthesizes theoretical foundations and identifies gaps in standardized validation and integrated governance. The methodology employs a design science approach, utilizing public datasets such as MIMIC-III, advanced generative models (GANs, VAEs, diffusion models), and blockchain-based consent systems. Validation metrics assess fidelity, utility, and bias reduction. Findings demonstrate substantial improvements in consent efficiency, data utility, and demographic fairness, though challenges persist regarding interface complexity and scalability. The conclusion affirms enhanced patient control, privacy, and equity, recommending adaptive interfaces, federated learning, and real-world pilots. The framework offers a scalable, policy-aligned solution for secure and equitable healthcare AI, supporting evidence-based innovation while safeguarding patient rights.