Technology and design decisions behind the Cascade Protocol
Deep dives into the technology and design decisions behind the Cascade Protocol. Written for AI/ML developers, healthcare engineers, and compliance-conscious architects building the next generation of health applications.
AI agents working with health data typically receive raw JSON with no schema context, proprietary API formats, or flat CSVs. None of these formats are self-describing. This post explains why RDF/OWL — the foundation of the Cascade Protocol — solves the semantic grounding problem for health AI, with a concrete comparison of how the same medication record looks in ad-hoc JSON, FHIR R4, and Cascade Turtle.
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When an AI agent writes "your blood pressure trend is improving," how do you know which readings it used? Were they from a medical-grade device or a consumer app? Without provenance, you cannot answer these questions. This post introduces Cascade's five provenance classes — from ClinicalGenerated to AIGenerated — and explains how the W3C PROV-O model turns data lineage into a compliance primitive for HIPAA and the 21st Century Cures Act.
The common pattern: you build a health app, you add AI features, you send user health data to an AI API. You have just created a massive HIPAA compliance risk. Who signed the BAA? Where is the data stored? Does the AI company use it for training? This post makes the case that local-first architecture — where the AI agent runs on the user's device and zero bytes of health data leave the machine — is the only genuinely responsible approach.
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