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Clinical AI GovernanceSepsis early-warning

Sepsis early-warning
In-house · v2.3 · ED + inpatient · Deterioration prediction · deployed 2025-10-20
critical Active

Performance against the locked baseline

baseline 0.91 · floor at −10% · locked 2026-08-20
QuarterAUROCDecaySens/SpecParity gapVerdictAttested by
2026-Q2 0.887 2.53% — — clean [email protected]
2026-08-20
2026-Q1 0.893 1.87% — — clean [email protected]
2026-08-20
2025-Q4 0.905 0.55% — — clean [email protected]
2026-08-20

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.

ActionSigned byRationaleSignature
approve deployment
2025-10-08
 [email protected]
CMIO
 Local validation on 12 months of retrospective ED data; nursing workflow review complete; rollback plan documented. 799dc5edfd38…

The fifteen-control checklist

12/15

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

compliance mapped — Sepsis early-warning
[email protected] · 2025-10-14T03:00 UTC · chai CHAI-TR-04 → in_progress
compliance mapped — Sepsis early-warning
[email protected] · 2025-10-13T13:00 UTC · nist-ai-rmf MEASURE-2.6 → implemented
compliance mapped — Sepsis early-warning
[email protected] · 2025-10-12T22:00 UTC · ruaih-2026 RUAIH-MON-02 → implemented
compliance mapped — Sepsis early-warning
[email protected] · 2025-10-12T13:00 UTC · ruaih-2026 RUAIH-GOV-01 → implemented
drift attested — Sepsis early-warning
[email protected] · 2025-10-11T09:00 UTC · 2026-Q2: AUROC 0.887 vs baseline 0.91, decay 2.53%
drift attested — Sepsis early-warning
[email protected] · 2025-10-10T13:00 UTC · 2026-Q1: AUROC 0.893 vs baseline 0.91, decay 1.87%
drift attested — Sepsis early-warning
[email protected] · 2025-10-10T06:00 UTC · 2025-Q4: AUROC 0.905 vs baseline 0.91, decay 0.55%
active — Sepsis early-warning
[email protected] · 2025-10-09T20:00 UTC · under_review → active: approve_deployment signed by [email protected] as cmio
approve deployment — Sepsis early-warning
[email protected] · 2025-10-09T00:00 UTC · signed as cmio, sig 799dc5edfd389ac437f74f9b453528f3738df70f00c583cc59290a86441dae75: Loca
under review — Sepsis early-warning
[email protected] · 2025-10-08T14:00 UTC · proposed → under_review: baseline locked, awaiting deployment sign-off
baseline locked — Sepsis early-warning
[email protected] · 2025-10-07T12:00 UTC · AUROC 0.91, threshold 10%, evaluated 2025-09-30
model registered — Sepsis early-warning
[email protected] · 2025-10-07T08:00 UTC · Sepsis early-warning (In-house) — critical risk, ED + inpatient