Top Synthetic Data Use Cases for the HealthTech Industry

Generate representative synthetic data for development, testing, acceptance, and production environments.

• Delivers consistent, production-like data across all DTAP environments

• Enables early, safe testing (shift-left) without waiting for production access

• Covers a wide range of clinical workflows and edge cases

• Reduces test cycle time and catches issues earlier in the pipeline 

Improve operations through data-driven automation and experimentation.

• Enables faster, privacy-safe testing of new processes, technologies, services, and workflows

• Accelerate digital transformation by reducing manual test setup and provisioning

• Reduces delays from data access approvals or masking steps

   

Create safe, production-like data for demos, training, and education without exposing real PHI.

• Enables safe and realistic demonstrations that mirror real clinical workflows and patient journeys

• Customize datasets to match specific specialties, patient population, or edge cases

• Reduces risk of PHI exposure during public-facing demos, conferences, hackathons, etc. 

• Reduces demo setup delays by providing fresh, realistic datasets quicker 

Preparing & delivery high-quality, representative data to build, test, improve analytics and AI models.

• Generates diverse, privacy-preserving training data that reflects real-world behavior

• Reduces time and cost of data preparation and cleansing 
• Fills gaps in edge cases and underrepresented segments
• Enables faster model development and testing, reducing regulatory risk 

Enable secure, privacy-safe exchange of data across systems & stakeholders

• Enables safe, privacy-safe data sharing without using real production data
• Maintains data utility for AI, analytics, and testing while fully removing identifiable elements 
• Supports cross-team and partner collaboration without legal bottlenecks
• Helps build secure data ecosystems across internal and external stakeholders

Build new features faster using synthetic data that mirrors production behavior without exposing real data

• Enables early testing with realistic, privacy-safe patient data that mirrors production 

• Supports rapid iteration and CI/CD without waiting on approvals

• Simulates diverse workflows, business logic, and edge cases safely and at scale

• Reduces reliance on sensitive data, accelerating HealthTech development and QA cycles