Sectors / Healthcare
Patient-data governance that flows into clinical AI, with consent end to end.
Clinical and operational AI rely on some of the most sensitive data there is. aerais keeps consent and provenance attached as data moves toward a model.
Why it matters
Why governing data and AI together matters in healthcare
Hospitals, health systems and life sciences organizations are adopting AI for imaging, documentation, triage and operations. Each use depends on patient data collected for a specific purpose under specific consent.
Governance has to follow that data. If consent, de-identification status and provenance are lost between the record system and the model, the organization cannot show that a clinical AI tool was built or used appropriately.
Typical questions
Five questions we hear in this sector
- Which patient data sets feed which AI tools, and under what consent or legal basis?
- Is de-identification verified before data reaches a model, and how is it evidenced?
- What happens to a model when a patient withdraws consent?
- Which clinical AI tools, including vendor features, are in use today?
- Who is accountable for monitoring a deployed tool after clinical sign-off?
Chain of custody
How the chain applies
Data plane controls here are classification of identifiable and special category data, consent and purpose tracking, lineage from record systems into research and model pipelines, and quality checks on labels and coding.
These seam loops tend to matter most:
Right to be forgotten across data and models
Withdrawal and deletion requests have to reach derived data sets and models, not only the record system.
Data-to-AI lineage
Clinical review needs to know which data and model version produced a recommendation.
Contaminated-data blast radius
If a source data set is found to be mislabeled or mishandled, the affected models and uses must be identifiable quickly.
Entry points
Where engagements usually begin
In healthcare, a first engagement often centers on consent, classification and lineage on patient data, followed by an inventory and oversight model for clinical and vendor AI. Most organizations start from data governance and bring in AI governance as the chain extends. A fixed-scope diagnostic is the usual first step; see engagements for how that is structured.
Regulatory context
Regulatory context, at a high level
Health privacy law applies to patient data (for example HIPAA in the US, PHIPA in Ontario, and GDPR rules on special category data in Europe). Software used for clinical purposes may be subject to medical device rules, and the EU AI Act treats many health uses as high risk. ISO/IEC 42001 and the NIST AI RMF provide management references.
This is general context only. Requirements vary by jurisdiction and change over time, so confirm current requirements with counsel and regulators. Alignment with a standard is not certification.
Diagnostic
What a diagnostic would look at here
- Where patient data enters analytics and AI pipelines
- Consent and purpose records and how they travel with the data
- Inventory of clinical, operational and vendor AI tools
- De-identification practice and how it is verified
- Post-deployment monitoring ownership for clinical AI
Each item is examined against the chain of custody: whether the control exists, whether it runs inside the systems rather than on paper, and whether it leaves evidence a reviewer can follow. Nothing is assumed from documents alone, and findings are written for executives and boards as well as for practitioners.
The output is a current-state maturity view, risk exposure and a prioritized roadmap. Request a diagnostic to scope it for your organization.
FAQ
Frequently asked questions
Does aerais work with identifiable patient data?
Engagements are designed to avoid handling it. We work with metadata, catalogs, controls and evidence, and agree access boundaries with your privacy office before any work starts.
How does consent reach an AI model?
Through purpose and consent attributes that travel with the data and are checked at pipeline and release points. A withdrawal then has a defined path to derived data and models.
Is clinical safety part of the scope?
Clinical validation and safety sign-off remain with your clinical governance bodies. aerais governs the data and the lifecycle controls around them.
Contact
Make your data and AI provable.
Start with a diagnostic: current-state maturity, risk exposure and a prioritized roadmap. We aim to reply within two business days.