NSAG · Module M11 · Healthcare & Clinical
Medical Technology & Evidence Standards
Standalone deployment retired
An algorithm used for 200 million patients was producing systematic racial bias. The governance infrastructure to detect it did not exist. Seven years later, most US hospitals still don't have that governance infrastructure.
What this address is
This hostname served a standalone copy of NSAG module M11. That copy was retired on 15 August 2026, and the page you are reading replaced it. The deployment stays online so that links already published against it keep resolving, and so that anyone arriving here is sent to the material that is still maintained.
The module's current scope, its evidence base, and its release status are published on the NSAG hub at nsag-site.vercel.app/m11. Where this page and the hub disagree, the hub is correct.
What the module examines
Obermeyer et al. (2019) published in Science the defining case study in clinical AI governance: a commercial algorithm used for approximately 200 million US patients to identify high-risk candidates for care management used healthcare cost as a proxy for health need. Because structural barriers reduce Black patients' healthcare spending relative to white patients with equivalent illness, the algorithm systematically underestimated Black patients' needs. The verbatim governance finding: remedying the disparity would raise Black patients receiving extra help from 17.7% to 46.5%. The ASTP/ONC Data Brief No. 80 (Chang et al., 2025) documents that 71% of US hospitals now use EHR-integrated predictive AI while fewer than half evaluate all models for demographic bias.
M11 sits in the Healthcare & Clinical group of the framework.
What the assessment measured
The module organised a structured self-assessment across six governance dimensions:
- 1AI Tool Inventory & Transparency
- 2Independent Evidence Evaluation
- 3Demographic Impact Assessment
- 4Human Oversight & Override Protocols
- 5Patient Disclosure Standards
- 6Post-Adoption Monitoring & Suspension Standards
Each dimension was described against tiers running from early stage up to the fully implemented tier the framework calls PIONEERING, with observable criteria written for each level, so that an institution could locate its own arrangements rather than receive a score. It was a self-assessment framework for institutional reflection, and never a validated instrument, an audit, an accreditation, or a compliance determination.
Who it was written for
Health system CMOs and CIOs · Clinical AI governance committees · Healthcare compliance officers · EHR implementation teams · Any healthcare institution using predictive AI in clinical decisions
And any patient whose care is shaped by an algorithm they will never see.
Why the standalone deployment was retired
The fifteen modules were first published as fifteen separate deployments. Scope, evidence, and release status then had to be maintained in fifteen places, and they drifted apart. The hub now holds one canonical page per module, and these fifteen addresses point at it.
Assessment collection is paused across all fifteen modules. The published operations matrix records the same position for every one of them: the canonical route is reachable, collection is paused, and advisory work is delivered by a person rather than by automated scoring. This page is a static record. It carries no forms and collects nothing.