A De-ID Before AI architecture that guarantees no identifiable patient data ever reaches the AI processing layer. Built for healthcare organizations that need clinical intelligence without compromising patient privacy.
Three core pillars that work together to deliver clinical intelligence while keeping patient identity fully protected.
Transforms raw clinical inputs into actionable insights using multimodal AI models trained on anonymized data.
Our De-ID Before AI architecture ensures no identifiable patient data ever reaches the AI processing layer.
Azure Health Data Services (AHDS) FHIR API acts as the single source of truth for all clinical data.
Raw clinical data enters, gets de-identified and processed, and emerges as structured FHIR resources - with no PHI ever crossing the AI boundary.
Raw clinical video capturing patient movement, behavior, and physical assessments across any specialty.
MP4 FormatStandardized physician assessment forms, structured clinical evaluations, and diagnostic drawings.
High-Res ImagePhysician observations and patient metadata providing clinical context for AI analysis.
Unstructured TextStores discrete clinical scores - assessment results, functional metrics, and physician evaluation outcomes - all linked to an opaque token, never a patient name.
Groups related observations into a single assessment session for streamlined clinical review.
Secure links to de-identified media stored in Azure Blob Storage with immutable versioning.
Clinician SPA and Patient PWA consume FHIR data via RBAC with Recharts trendlines and masked video playback.
Four distinct layers that enforce the zero-PHI principle at every boundary, from clinical capture through to authorized clinical consumption.
Critical checkpoint: De-identification and tokenization occur before any data touches AI components. PHI stripped, identity vaulted.
Multimodal model processes anonymized physician assessment inputs for precise clinical scoring across any specialty, with no patient identifiers present.
Stores Observation scores, DiagnosticReport groupings, and DocumentReference media links - all keyed to opaque tokens.
Clinician SPA (Desktop) and Patient PWA (Tablet) consume FHIR API via Role-Based Access Control for secure re-identification only where authorized.
"We strip identity first, then score movement - privacy by default."
Names, MRNs, and DOBs are stripped from raw input before any processing begins.
Encrypted mapping table linking Token to Identity, stored separately with AES-256 and restricted RBAC access.
Face blurring applied to video assessments; PHI and identifying information removed from physician assessment documents.
Azure AI Video Indexer and Azure AI Foundry Multimodal process only anonymized inputs.
Every HIPAA technical safeguard is backed by a concrete Azure control - not just a policy statement.
Assign a unique name and number for identifying and tracking user identity across all sessions.
Electronic procedures that terminate an electronic session after a predetermined time of inactivity.
Hardware and software mechanisms to record and examine activity in information systems containing PHI.
Policies and procedures to protect EPHI from improper alteration or destruction in an unauthorized manner.
Guard against unauthorized access to EPHI being transmitted over electronic communications networks.
Mechanism to encrypt and decrypt PHI stored in any electronic medium whenever deemed appropriate.
Our architecture guarantees that sensitive patient identity is separated from clinical data before it ever reaches the AI processing layer.