This Health City white paper presents a case study demonstrating that synthetic data can replicate real-world health data analytics while preserving patient privacy. Using deep learning, a complex dataset of 300,000 patients was synthesized to match original patterns in mortality and hospitalization models. Results showed minimal identity disclosure risk (0.0019) and comparable analytical outcomes. The study confirms synthetic data’s potential for broader data access, safe AI development, virtual clinical trials, and training future health data scientists, all while bypassing regulatory constraints.

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