Safety Assessment Framework for AI. Twenty years of patient safety discipline, root cause analysis and failure mode analysis, applied to the AI systems entering clinical care.
AI vendors test their models in development. They do not assess how those models will fail in your hospital. The AI safety conversation is conducted in a technical vocabulary with no equivalent in the patient safety and quality management disciplines your organization already practices.
SAFE AI brings the two methodologies your quality, risk, and patient safety teams already use — root cause analysis and failure mode and effects analysis — to the AI systems being deployed into clinical workflows. The output is an inventory of identified failure modes, scored on severity, occurrence, and detection, tied to mitigation actions your existing governance structure can execute.
What separates this from a standard quality exercise is where the failure mode catalog begins — not from a blank sheet or vendor documentation, but from the Cure8 Signal Registry: a continuously curated body of peer-reviewed clinical AI literature maintained by research agents that actively scan publications, extract emerging patterns of failure and success, and build an evidence base well before client engagement begins. By the time we assess your environment, the literature foundation is already established.
The methodology carries two decades of patient safety practice. The evidence base powering it is continuously updated to keep pace with a rapidly evolving field.
SAFE AI runs two parallel analyses on every in-scope AI system.
Applied when a model degrades or produces an unexpected outcome in production. The failure rarely originates in the model itself.
A prospective catalog of how failure could occur — conducted before it does, grounded in evidence rather than assumption.
Illustrative excerpt from an ambient scribe assessment. Severity, occurrence, and detection scored 1 to 10. RPN equals S times O times D.
Sample only. Actual SAFE AI assessments score each failure mode against your specific deployment context, current controls, and monitoring infrastructure.
A complete catalog of identified failure modes for each in-scope AI system, scored on severity, occurrence, and detection, ranked by Risk Priority Number, and supported by evidence citations drawn from the clinical AI literature.
Remediation actions are mapped directly to your existing quality, risk, and IT governance structures. Each action is assigned to a responsible function, scoped to a realistic timeline, and written in the operational language your teams already use.
Recommended monitoring parameters are scoped to the failure modes carrying the highest RPN scores and the weakest existing detection, designed to integrate with the surveillance infrastructure already present in your environment.
A structured presentation prepared for your AI governance committee or board, covering the assessment methodology, prioritized findings, and recommended organizational posture — written for leadership decision-making, not technical review.
The methodology used to investigate medication errors, surgical complications, and diagnostic delays applies directly to model failures. The failure-and-recovery dynamics are structurally similar: a system performs as expected most of the time, fails under conditions that were not anticipated, and the question is whether the organization can detect, contain, and learn from the failure.
The vocabulary already embedded in your quality department's practice — root cause, contributing factor, severity, detection, RPN, mitigation, escalation — is the vocabulary SAFE AI uses. The framework integrates with existing governance structures rather than requiring new ones.
The same rigor extends to the evidence base. Research agents continuously monitor peer-reviewed clinical AI literature, identifying failure patterns and success conditions across deployment contexts. That ongoing curation ensures the failure mode catalog entering your assessment reflects current field findings — not a static list assembled at framework inception and never updated.
Clinical AI governance does not require a new safety language or a failure taxonomy assembled in isolation from clinical evidence. It requires the existing patient safety discipline, applied with rigor, and grounded in a literature base that keeps pace with the systems being deployed today.
Begin with a two-week SAFE AI assessment of your ambient documentation deployment. The literature baseline is already in place — the engagement is focused entirely on your environment, your controls, and your risk posture.
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