expandev

Patient data, governed by design. Not by hope.

For healthcare and life-sciences teams building AI-assisted software under HIPAA and FDA scrutiny.

The regulatory frame

HIPAA, FDA, and AI management.

HIPAA

Protected health information (PHI) handling, access control, and audit requirements.

FDA SaMD & Good ML Practice

Documented design history and change control for Software as a Medical Device.

ISO/IEC 42001 & the EU AI Act

AI management and high-risk obligations for clinical-adjacent systems.

How governed development maps to that frame

Design history, continuously assembled.

PHI handling

Declared explicitly in the asset model, not buried in generated code where no auditor can find it.

Design history file

Versioned requirements, architecture, and approvals form one continuous, exportable record.

Human oversight

Every AI output is reviewed and approved by a named clinician or engineer.

Regulatory scenario walkthrough

The patient-intake feature.

Illustrative scenario — not a customer case.

A digital-health team builds an AI-assisted patient-intake feature. In Architecture, the PHI data store and its access layer are declared as explicit assets with handling rules. Development generates the intake flow, and the Quality Gate rejects a logging call that would write PHI to an unsecured sink. The Analyzer records the rejection, the corrective fix, and the engineer who approved it.

When an FDA reviewer or a HIPAA auditor later requests the design history of the intake feature, the team exports the artifact trail — requirement, asset map, generated code, approvals — as a design history file that was assembled continuously, not reconstructed under deadline.

See expandev applied to your stack.

Tell us about your team and we'll tailor a walkthrough to your stack, your governance needs, and the way you ship.

Patient data, governed by design. Not by hope. · Expandev