Train on healthcare data without compromising privacy
Foundation model labs and AI research organizations need high-quality healthcare data to build models that understand clinical language, medical imaging, and patient context. Integral makes that data available — de-identified, certified, and optimized to preserve the signals your models need.
Higher-Utility Training Data
Safe Harbor strips the 18 HIPAA identifiers regardless of context — destroying clinical nuance your models depend on. Expert Determination lets you keep geographic context, temporal relationships, clinical dates, and other signals that make healthcare data valuable for training, while certifying that re-identification risk stays below defensible thresholds.
Synthetic Replacement for Clinical Text
Redacted text leaves gaps that degrade model training. Integral's "hiding in plain sight" approach replaces real PHI entities with synthetic ones — names, dates, locations — so your training corpus reads naturally. Models learn from realistic clinical language, making it extremely difficult for an attacker to distinguish synthetic entities from real ones.
Self-Hosted Deployment
Healthcare data often can't leave your infrastructure. Integral deploys inside your VPC, on-prem data center, or air-gapped environment as container images — same core engine, no data leaving your network. Process terabytes of clinical notes and medical imaging without a single record crossing your boundary.
Why Expert Determination matters for AI
The de-identification method you choose directly impacts the quality of your training data.
Safe Harbor: The Blunt Instrument
- × All dates generalized to year only — temporal signals lost
- × Geographic data truncated to 3-digit zip at most
- × Ages over 89 grouped into a single bucket
- × No linkage tokens — cross-dataset joins impossible
- × Redacted text leaves [REDACTED] gaps that degrade model quality
Expert Determination: The Surgical Approach
- ✓ Clinical dates preserved when the use case requires it
- ✓ Geographic precision retained where analytically valuable
- ✓ Age ranges configurable to the needs of the dataset
- ✓ Linkage tokens retained for cross-dataset analysis
- ✓ Synthetic replacement keeps clinical text natural and model-ready
Configurable Remediation Trade-Offs
Training a model on clinical notes? Preserve clinical dates and suppress demographics. Building a geographic health model? Keep location precision and generalize other fields. Integral's remediation adapts to what your models need — not the other way around.
Alternative Privacy Models
Beyond standard de-identification, Integral supports differential privacy, k-map analysis, generalization, truncation, and feature engineering. The privacy model is selected to match your data characteristics and research requirements, maximizing utility while maintaining certified privacy guarantees.
Certified and Audit-Ready
Every dataset processed through Integral receives a signed Expert Determination certification — a PDF opinion backed by qualified statistical experts that documents the privacy model, remediation strategy, and risk justification. Ready for your legal team, data partners, and regulatory reviews.