Regulatory Operations
The regulatory evidence graph: beyond document management
A connected evidence model can reveal gaps, accelerate change assessment, and make regulatory conclusions reviewable.
Regulated product development produces thousands of documents, data points, decisions, and obligations. Traditional repositories preserve those files. They do not consistently preserve the relationships that give the files regulatory meaning.
What an evidence graph connects
A useful graph links product claims, intended use, requirements, hazards, controls, specifications, tests, results, labeling, submissions, commitments, markets, and lifecycle changes.
Why relationships matter
When a claim changes, teams need to know which risks, tests, reports, labels, and market authorizations may be affected. When a test fails, they need to see every conclusion that depended on it.
A repository answers where the file is. An evidence graph answers what it supports and what depends on it.
Start with one decision chain
Begin with a high-value chain such as claim-to-risk-to-test-to-submission. Establish controlled identifiers, relationship types, source records, ownership, and review status.
Keep humans accountable
AI can propose relationships and highlight missing links. Qualified professionals must review consequential interpretations and approve regulatory decisions.
Further regulatory context
Read medical device regulatory definitions and authoritative references. Explore the workflows eeRegOS supports.
Editorial note: This content is educational and does not constitute legal or regulatory advice. Product-specific decisions should be reviewed by qualified professionals using current authoritative sources.