Cortivus SAFE AI applies the patient safety discipline you already trust, root cause analysis and failure mode analysis, to the AI systems entering your hospital.
Three structural problems that most hospitals are not yet equipped to
detect.
You do not have access to the training data, drift telemetry, or update history of the AI vendors operating inside your clinical workflows. The vendor knows when their model degrades. You do not.
The AI safety conversation is happening in technical vocabulary. Your safety and quality departments speak RCA, FMEA, SBAR, etc. Those frameworks are not yet being applied to the AI being deployed into care.
New/emerging laws and regulatory guidance may require what most hospitals cannot yet produce: a documented AI inventory, drift monitoring, inference logging, and a defensible governance trail.
Safety Assessment Framework for AI
Built for operators based on proven clinical safety and quality methodologies. Root cause analysis for AI failures. Failure mode and effects analysis for assessing potential weaknesses in your workflows as it intersects AI. Outputs your leadership committees will recognize on first read.
Learn the FrameworkEvery engagement applies the SAFE AI framework. Scope and depth vary.
Two-week SAFE AI assessment of your ambient documentation deployment. The fastest way to see the framework applied to a system already in your environment.
A defensible baseline of every AI system in clinical use across your hospital, with risk tier and governance gap analysis. Annual refresh cycle available.
Deeper engagement for hospitals with multiple clinical AI tools in production. Establishes ongoing monitoring controls and a board-ready governance trail.
Independent review of AI vendor proposals, contracts, and clinical-safety claims. SAFE AI scoring of training data disclosure, drift monitoring, audit access, and indemnification language before you sign.
If the data underneath your AI cannot be trusted, the outputs cannot be audited. That is a patient safety question, not an IT problem.
AI is being deployed faster than IT operational discipline can absorb it. The gap between adoption and governance is where unmanaged risk lives.
Half of GenAI projects do not survive the path to production. The bottleneck is governance, not the model. In healthcare, that discipline has a name your quality team already knows.
If you have AI in clinical use and you cannot point to a documented drift monitoring practice, an inference logging trail, or an RPN-scored inventory of failure modes, we should talk.
servingyou@cortivus.com
Two-week SAFE AI assessment of your ambient documentation deployment.
Thirty-minute call to scope what a SAFE AI engagement would look like in your environment.