Access to patient data is the slowest step in health-tech development. Synthetic data — statistically realistic but artificial records — lets teams build, test, and share without exposing real people.
Strong use cases
Development and QA environments, vendor demos, load testing, education, and augmenting rare-condition classes in training sets.
Honest limitations
Synthetic data inherits the biases of its source and can miss rare correlations that matter clinically. Regulatory-grade validation still requires real, governed data.
Doing it right
Measure privacy risk (re-identification testing) and utility (does a model trained on synthetic transfer to real?) rather than trusting vendor claims. Datastrel implements synthetic-data pipelines and the evaluation harnesses that keep them honest.