The role of data in HealthTechInnovation in HealthTech thrives on data. But for teams building HealthTech solutions, data access is one of the biggest bottlenecks.The challenge: Innovating under constraints Product and R&D teams are under pressure to deliver safer, smarter, and faster solutions that improve patient outcomes. But strict privacy regulations, fragmented datasets, and lengthy approval processes often slow them down, creating a gap between innovation and real-world impact.Why using real data is not an optionAccess to real data is tightly restricted. Even when possible, it introduces heavy compliance risks and slows development cycles. Alternatives like traditional approaches like masking or fabricating test data often fail to reflect clinical complexity, breaking workflows and limiting testing.The cost of limited data availabilityWhen teams can’t get the data they need, projects stall, testing coverage shrinks, and bugs slip into production. This doesn’t just delay releases, it directly impacts patient safety and trust, while driving up costs for HealthTech providers and vendors.How synthetic data adds value Synthetic data offers realistic, privacy-preserving datasets that mirror real patient records without exposing sensitive information. This enables HealthTech companies to accelerate development, validate new features safely, and unlock AI-driven insights, all while enhancing privacy.
Enhance patient data-privacyData MaskingTransform real sensitive data into privacy-preserving synthetic alternatives that preserve structure, relationships, and workflows. Preserve data utility for testing while eliminating risk of re-identification Accelerate release cycles by removing the need for lengthy manual anonymization steps Enhance compliance with HIPAA/GDPR while keeping datasets usable across environments Learn more
Automatically identify sensitive valuesPII ScannerAutomatically identify PII and PHI with our AI-powered PII Scanner. Instead of relying on slow, error-prone manual checks, you can automatically detect identifiers ensuring no sensitive value slips into development or test environments. Reduce time-consuming manual reviews of sensitive fields Detect identifiers consistently across structured and unstructured health data Prevent accidental exposure of PHI/PII in test or demo environments Learn more
Replace sensitive health values with representative valuesSynthetic Mock ValuesSubstitute PHI, PII, and other identifiers with realistic, representative mock values that reflect real data. Syntho automatically recommends the right synthetic replacement for each PHI/PII field, so HealthTech teams can: Preserve the utility of real data records Reduce manual data preparation workload Enhance privacy while enabling testing, training, and analytics Learn more
Maintain health data consistency across systemsConsistent Mapping & Referential Integrity HealthTech data typically flows through multiple systems from for example: EHRs, lab results, billing, and more. If those connections break, testing environments stop reflecting system behavior. Syntho preserves referential integrity across health datasets, enabling reliable end-to-end testing and accurate multi-table analytics. That means: Stable test environments for cross-system integrations Fewer mismatches that could delay releases More reliable automation pipelines Learn more
Reflect real-world scenariosRule-Based Synthetic DataApply specific business rules to generate synthetic data that reflects real-world workflows, behavior, and edge cases specific to your application. This ensures your test datasets are both statistically accurate and clinically relevant. Validate system behavior under high-risk scenarios (e.g., drug interactions, adverse events) Test complex product logic across diverse scenarios Reduce risk of failure in real-world environments Learn more