  Recent advances in deep generative models have greatly expanded the potential
to create realistic synthetic health datasets. These synthetic datasets aim to
preserve the characteristics, patterns, and overall scientific conclusions
derived from sensitive health datasets without disclosing patient identity or
sensitive information. Thus, synthetic data can facilitate safe data sharing
that supports a range of initiatives including the development of new
predictive models, advanced health IT platforms, and general project ideation
and hypothesis development. However, many questions and challenges remain,
including how to consistently evaluate a synthetic dataset's similarity and
predictive utility in comparison to the original real dataset and risk to
privacy when shared. Additional regulatory and governance issues have not been
widely addressed. In this primer, we map the state of synthetic health data,
including generation and evaluation methods and tools, existing examples of
deployment, the regulatory and ethical landscape, access and governance
options, and opportunities for further development.
