The Definitive Guide to Synthetic Data for Healthcare & Life Sciences

Today’s healthcare industry generates massive amounts of data. Electronic health records (EHRs) document patients from birth up to their latest clinical visit. DNA microarrays and sequencing make it possible to understand the genetic factors associated with disease. Clinical trials generate three times as much data today as they did ten years ago. Despite all this data, barriers to using this data downstream remain.

Synthetic data helps healthcare institutions make the most of their data, while safeguarding sensitive information. It is generated by computer simulations or algorithms to resemble the patterns, distributions, and insights held in real-world data without exposing any of the private or sensitive information present in the original dataset.

In this guide, we explore: 

• The promise of synthetic data and its key benefits for healthcare and life sciences. 
• Fundamentals to synthetic data including how it works, where it comes from, and how to get started 
• Practical healthcare use cases of synthetic data, including real-world examples of Gretel, the leading synthetic data platform for generative AI.