A wrong output has real consequences here. Engineer for that.
AI features touching patient-adjacent data and decisions need evaluation, access control, and an audit trail built into the architecture — not a compliance badge applied after the fact. That's engineering work, and it's ours; certification stays yours.
What this looks like inside a healthtech engineering org.
These are the predictable result of AI features shipping into patient-adjacent workflows faster than the evaluation and governance infrastructure around them.
- A clinical- or triage-adjacent AI feature has no documented evaluation against known failure modes before shipping.
- PHI flows through a model or vector store without a clear access-control and audit boundary.
- A model update changed output behavior, and the team found out from a user complaint, not a test.
- No inventory exists of what AI touches patient data end to end.
- Clinical staff have stopped trusting an AI feature's output and started quietly working around it.
- A partner or payer asks for documentation of how the AI system is governed, and none exists in a reviewable form.
Where in the feature's life this shows up.
The risk isn't constant — it concentrates at specific points between a prototype and a payer's review.
01 · Prototype
Clinical- or triage-adjacent feature in testing
Evaluation against known failure modes gets skipped, since no patient sees it yet.
02 · Shipped to clinical workflows
Real patient-adjacent use
A model update changes output behavior and the team finds out from a complaint, not a test.
03 · Scaling
PHI flowing through more systems
No inventory tracks what touches patient data end to end, and access control gets inconsistent across systems.
04 · Under audit
A partner, payer, or OCR review asks for evidence
Section 1557's AI nondiscrimination duty has applied since May 2025 — most teams have nothing documented until asked.
Where this becomes engineering work.
Four disciplines cover most of what shows up in a healthtech AI feature stack. Not every account needs all four.
AI Reliability Engineering
The deliverable: a held-out test set and release gate checked against known clinical failure modes before ship.
AI Governance & Control
The deliverable: an inventory of every AI system that touches patient data, with an owner and approval gate.
AI Security Engineering
The deliverable: an access-control and audit boundary around any model or vector store that PHI flows through.
AI Systems Engineering
The deliverable: a regression suite that catches a behavior-changing model update before a patient or clinician does.
Not sure where the PHI access gap is? Run the AI Architecture Review.
Companies typically bring Crescent in when:
Capacity
You need specialist AI engineering capacity without building another team — project-based capacity, not staff augmentation.
Expertise
The project has crossed into an area where your existing team lacks specialized depth.
Speed
A production deadline is approaching and the internal path is too slow.
Critical project
Your core engineering team can't afford to divert months of capacity.
Independent review
You need an external technical second opinion before making an expensive decision.
Common questions.
Build the architecture your compliance team can actually certify.
Tell us where the gap is — an unevaluated feature, a PHI access boundary that doesn't exist, no inventory of what AI touches patient data. We'll tell you which discipline it maps to and whether it's a fit.
NDA available on request · Scoped engagements · No surprise fees
Not sure where the gap is? Run the AI Readiness Score — five minutes, no call required.