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Clinical AI GovernanceNo-show prediction

No-show prediction
In-house · v0.9 · Ambulatory · Schedule optimisation
low Under review

Performance against the locked baseline

baseline 0.86 · floor at −15% · locked 2026-08-20
No attestations against the current baseline yet. The timeline appears after the first quarterly attestation.

Quarterly attestation

The number, on the record, once a quarter. Breach the threshold on an active model and suspension is automatic — continuation is what needs a signature.

Lock a baseline

Insert-only: a new lock supersedes the old on the record, never edits it. The threshold you choose is the automatic suspension floor.

Lock an equity threshold and a quarterly parity gap above it suspends the model automatically — the same enforcement as decay. The headline metric holding while a subgroup slides is exactly the failure AUROC alone cannot see.

Accountable sign-offs

status changes need an org admin

You sign as who you are signed in as — email from the session, your stated role, your rationale, and a hash anchored in the audit chain. Deployment, suspension, reactivation and retirement require an organisation administrator.

No sign-offs yet.

The fifteen-control checklist

0/15

The Institute's published RUAIH / CHAI / NIST crosswalk, as a working checklist for this model. Change a status and it saves — every change lands in the audit chain. 'Not started' is the honest default; the binder shows the gaps.

RUAIH 1 — Governance
The AI governance committee
CHAI: Organizational structure · NIST: GOVERN · evidence: AI governance committee charter
The organizational AI policy
CHAI: Organizational AI policy · NIST: GOVERN · evidence: Organizational AI policy
The AI inventory and product registry
CHAI: Responsible AI lifecycle management · NIST: MAP · evidence: AI use case registry
Board and executive reporting
CHAI: Organizational resources · NIST: GOVERN · evidence: Board reporting pack
Resourcing the governance programme
CHAI: Organizational resources · NIST: GOVERN · evidence: Programme resourcing plan
RUAIH 2 — Effective data management
Data governance and minimum necessary
CHAI: Responsible data management and use · NIST: MAP · evidence: Data use agreement and access policy
Data security controls for AI systems
CHAI: Responsible data management and use · NIST: GOVERN · evidence: Access control and audit log review record
RUAIH 3 — Risk and bias reduction
The risk-tiering method
CHAI: Risk and impact assessments · NIST: MAP · evidence: Intake and risk-tiering procedure
Bias and equity assessment
CHAI: Risk and impact assessments · NIST: MEASURE · evidence: Local bias assessment
Third-party and vendor due diligence
CHAI: Third-party management · NIST: GOVERN · evidence: Vendor AI disclosure request and completed questionnaire
RUAIH 4 — Monitoring, evaluating and validating safety performance, effectiveness and responsible use
Local validation before deployment
CHAI: Responsible AI lifecycle management · NIST: MEASURE · evidence: Local validation study protocol and result
Post-deployment monitoring
CHAI: Responsible AI lifecycle management · NIST: MEASURE · evidence: Monitoring plan with named thresholds
AI safety event reporting
CHAI: Responsible AI lifecycle management · NIST: MANAGE · evidence: AI safety event reporting route
RUAIH 5 — Transparency, education and training
Patient disclosure and consent
CHAI: Education, training and feedback · NIST: GOVERN · evidence: Patient disclosure standard
Role-specific workforce training
CHAI: Education, training and feedback · NIST: GOVERN · evidence: Training curriculum and completion records

Survey binder

The full governance record as one download — assembled live, with the audit chain's verification verdict inside. This is the document you hand the surveyor.

Export survey binder

Audit trail — this model

under review — No-show prediction
[email protected] · 2025-11-01T18:00 UTC · proposed → under_review: baseline locked, awaiting deployment sign-off
baseline locked — No-show prediction
[email protected] · 2025-10-31T19:00 UTC · AUROC 0.86, threshold 15%, evaluated 2026-07-20
model registered — No-show prediction
[email protected] · 2025-10-31T00:00 UTC · No-show prediction (In-house) — low risk, Ambulatory