Causal Methods Blog

Crash Course on Synthetic Data in Healthcare

ByAndy Wilson, Aimee Harrison

•2025-11-18T13:47:45.677Z

Abstract

Healthcare researchers face a fundamental tension between the need for robust datasets to advance medical knowledge and the imperative to protect patient privacy through regulations like HIPAA and GDPR. Synthetic data represents artificially generated records that preserve statistical properties from real data while containing no actual patient information. Synthetic data offers a solution for privacy-constrained research, methods development, and educational applications. This article introduces the privacy-utility-fidelity framework for evaluating synthetic data quality and demonstrates practical implementation through two contrasting case studies: a small rural women's health clinic sharing maternal outcomes data without compromising privacy or requiring extensive infrastructure, and an independent biostatistician developing educational materials with known ground truth for teaching causal inference methods. Through detailed R code examples using the synthpop package and comprehensive validation workflows, the article equips researchers with the conceptual foundations and practical tools to appropriately generate, evaluate, and deploy synthetic data in their work. Key takeaways emphasize that while synthetic data enables work otherwise impossible, it requires rigorous evaluation across all three dimensions, transparent documentation of trade-offs, and validation in real data for confirmatory research.