AI reaches the bedside and the pharmacy counter. AVAAS certifies it without touching a single patient record.
Clinical decision support, sepsis alerts, utilization management, prior authorization, and pharmacy automation now shape care for millions of patients. AVAAS evaluates the model, the governance, and the artifacts, not patient data, so verification never creates a new privacy exposure.
The decision point in clinical and pharmacy AI
The AI acts when it flags or fails to flag a deteriorating patient, scores who receives care management, recommends ending a rehabilitation stay, or gates a prescription behind an automated prior authorization. Each of those is a clinical consequence produced by a model most institutions have never independently tested.
A model that quietly underperforms in your population is invisible in the vendor's brochure and obvious in an independent evaluation.
What keeps clinical AI exposed
The alert that does not fire
An external validation of a widely deployed sepsis prediction model, published in JAMA Internal Medicine in 2021, found it identified only about a third of sepsis cases while generating heavy alert burden. Local behavior is not the brochure.
Utilization AI under litigation
Litigation alleges an algorithm was used to cut off rehabilitation coverage for elderly patients, with roughly nine in ten appealed denials reversed. The insurer disputes the claims. The pattern is the exposure.
The proxy was the flaw
Research in Science in 2019 showed a widely used care-management algorithm scored Black patients as healthier than equally sick white patients because it used spending as a proxy for need.
Evidence built for clinical governance
Does the model perform in your population?
In-environment evaluation measures behavior on your cases, with synthetic or de-identified data where appropriate, and reports the gap between claimed and observed performance.
Does it treat patient groups equitably?
Structured testing probes for the proxy failures documented in the literature, with causal attribution that shows why a disparity appears, not just that it does.
Can you satisfy compliance without new privacy risk?
The engagement is designed so no protected health information is accessed, and the output is documented, third-party evidence of conformity to a published standard at the decision point.
Health systems, payers, PBMs, and digital health vendors all deploy models that decide for patients. Certification gives clinical governance committees the independent answer they currently take on faith.
Related AVAAS coverage: Healthcare · California healthcare AI · Insurance.
Check your exposure before you certify
AI Risk Mirror
See documented AI failures in your sector, each with sources and how AVAAS catches the same pattern.
Open tool →AI Vendor Liability Screener
Find out who is liable when a third-party AI vendor drives a decision that harms someone.
Open tool →AI Assurance Gap Analyzer
See the gap between your current AI governance and what a defensible standard requires.
Open tool →Verify the model before it reaches the bedside.
Tell us where AI touches clinical or pharmacy decisions in your organization, and we will scope a no-PHI evaluation that your governance committee can act on.
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