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Healthcare Analytics on Encrypted Patient Data

Run cohort research, risk models, and care coordination on encrypted patient data across sites — without a new data-use agreement each time.

Cross-site research doesn’t need a data-sharing agreement measured in months

Two hospitals running a joint post-marketing study. A health system pooling claims and clinical data for a value-based care contract. A multi-site research network trying to replicate a finding before it publishes. In every case, the researchers can specify exactly which fields they need. What actually stops them is everything downstream of that request: a new data-use agreement, a fresh de-identification pass, and a privacy review that treats every collaboration like the first one anyone’s ever done.

What changes

Blind Insight runs cohort queries, risk models, and encrypted-record lookups directly on ciphertext — the analyst, the model, and Blind Insight itself see only encrypted fields. Small-cohort results are suppressed automatically (k=11), so no query can return a group small enough to re-identify someone. BlindML trained a Naive Bayes model on 1,000,000 encrypted records — six features, one target — in 50 seconds; the comparable fully homomorphic encryption workload takes 4 to 24 hours. Every layer runs on FIPS-compliant AES-GCM-256, and Blind Insight is SOC 2 Type II and HIPAA compliant at every tier — see pricing.

That’s zero trust enforced by cryptography, not policy. Centralized key custody — the model every health-tech vendor backs into — breaks the trust assumption. Blind Insight doesn’t have your keys, so a breach on our end exposes ciphertext and keyed hashes, never a patient.

Where this shows up

  • Cross-site health information exchange. Multiple hospitals and research institutions run cohort identification and analysis on encrypted records, without producing a fresh plaintext extract for every partner.
  • Care coordination and patient support. A care coordinator or support rep gets field-level, time-bound access to a chart for the interaction they’re on, not the rest of their shift, and it revokes the moment the call ends — healthcare’s own recent breaches are the reason this isn’t hypothetical.
  • The ARPA-H network. Blind Insight is a spoke in ARPA-H’s Investor Catalyst Hub, a nationwide network accelerating the commercialization of biomedical solutions.

Why anonymization alone doesn’t solve this

Standard de-identification strips the fields researchers need to match a patient across two institutions’ systems — the exact join key cross-site research depends on. The EDPB’s 2025 pseudonymization guidance, built on the Breyer and Deloitte rulings, turns on one question: does the recipient of shared data actually hold the means to re-identify someone, or just the data itself? We’ve covered what that distinction means for cross-border data sharing. Blind Insight’s architecture is built for that exact distinction — a query result’s recipient never holds a re-identification key, because the key never left your side. Fully homomorphic encryption solves a related theoretical problem but not the practical one — see why most teams don’t need it and how the alternatives compare.

From demo to production in weeks. Book a demo and we’ll walk the HIPAA minimum-necessary mapping against your schema, then start in the sandbox — nothing to deploy, synthetic data in your shape, live in 72 hours. Or try it on your own schema today.