Who We Work With?
ML Assurance & Evaluation Companies
​We provide the clinical depth healthcare evaluation requires: documented failure patterns mapped to AI types, domain-specific test scenarios, and clinical expertise that's expensive to build in-house.
We can integrate our failure pattern database into your evaluation frameworks, co-develop healthcare-specific assessments, or provide clinical review for your client engagements.
Healthcare AI Vendors
We provide systematic evaluation against documented failure patterns, giving you prioritized clinical considerations tailored to your AI type with evidence citations and clear acceptance criteria.
How it works:
Tell us about your AI
Complete a brief intake covering what it does, its clinical domain, and deployment context.
Get your tailored evaluation
We filter our database to the patterns relevant to your system, providing you with prioritized considerations alongside evidence and clear criteria.
Build with confidence
Address gaps during development and use the documentation for FDA submissions, procurement committees, and internal evaluation.
The result: gaps identified early when they're inexpensive to fix, documentation you can use with regulators and procurement committees, and confidence that you've tested for what matters.
Patient Safety & Risk Organizations
We provide systematic frameworks to evaluate AI products with clinical evidence. Our approach helps you identify AI-related patterns in existing incident data and assess vendor claims against documented failure modes.
Whether you're a health system evaluating products, an insurer making coverage decisions, or a patient safety organization monitoring technology risks, we help you ask the right questions and interpret the answers.
Why Standard ML Validation Misses Real-Life Clinical Failures
Healthcare AI often fails in deployment because validation ignores real clinical use. This paper explains why and what to test instead.
What We've Built
We’ve built a comprehensive, evidence-based framework grounded in documented patient harm, real workflows, and frontline clinical reality—so risks are identified early, not after deployment.
100+
Clinical Failure Patterns
How AI systems actually cause problems in clinical settings: alert fatigue, automation bias, sensor failures, atypical presentation misses, escalation gaps, and more.
300+
System Vulnerabilities
Why AI fails in real clinical environments through the organizational, workflow, and human factors that technical testing misses.
240+
Applicability Mappings
Which patterns apply to which AI types, where one failure pattern generates dozens of specific test scenarios.
Documented Cases with Citations
Every entry traces to FDA MAUDE, AHRQ, or peer-reviewed literature.
The Problem
The Gap Between Lab Performance and Clinical Reality
Healthcare AI systems excel in controlled testing, but clinical environments present different challenges. The questions that seem obvious in hindsight only become visible when you know what to test for.
What happens when alerts pile up and critical warnings get buried? How does a system behave when sensor data degrades but still looks plausible? What if a diagnostic tool performs well on average but misses atypical presentations?
These aren't hypothetical scenarios but documented failures from systems that passed technical evaluation yet encountered problems in real clinical settings.
The challenge isn't accuracy, but knowing how to evaluate for clinical reality.
The Guide
A Systematic Approach to Clinical AI Evaluation
Validara Health was founded to build what should have existed: a systematic catalog of how clinical AI fails in real-world deployment. Our methodology draws from FDA adverse event reports, peer-reviewed literature, AHRQ patient safety data, and clinical deployment experience.
The result is a database of documented failure patterns mapped to specific AI types and clinical contexts. Every test scenario we create traces back to real patient outcomes rather than hypothetical risk.
About Us
Validara Health started with a simple realization: most healthcare AI failures aren’t mysterious. They’re predictable if you know where to look.
We’ve studied hundreds of real cases where clinical AI caused patient harm, disrupted workflows, or triggered regulatory issues. The same patterns show up again and again. And most could have been caught earlier with the right clinical perspective at the table.
We bring a clinical lens that turns “we think this is safe” into “here’s the evidence.”
Our work is grounded in real-world incidents, peer-reviewed research, and bedside experience.
Services
Clinical AI Readiness Assessment
For healthcare AI vendors preparing for deployment or regulatory submission
A systematic evaluation of your AI system against documented clinical failure patterns. You receive prioritized risk considerations tailored to your AI type, with evidence citations and clear acceptance criteria.
What you get:
Tailored failure pattern analysis for your specific AI type and clinical domain
Prioritized clinical considerations with evidence from documented harm cases
Clear acceptance criteria for each identified risk
Documentation suitable for FDA submissions and procurement processes
Systematic Evaluation
Move from hoping your AI is safe to knowing you've addressed documented failure patterns before deployment.
Deep Engagement
Custom clinical expertise for pilot planning, safety surveillance, and regulatory preparation.
Consulting & Red Team Evaluation
For teams needing deeper engagement, pilot safety planning, or ongoing clinical expertise
Custom engagements for pre-deployment safety evaluation, pilot study design, safety surveillance frameworks, or regulatory preparation.
Example projects:
Pre-pilot safety surveillance design
Clinical failure mode workshops with product teams
Regulatory submission support with clinical evidence
Safety monitoring framework development
Structure: Project-based or ongoing advisory
Investment: Custom scoped
Clinical AI Readiness Assessment
For ML assurance companies, patient safety organizations, and risk assessment teams
We provide the clinical domain expertise your healthcare evaluations require:
What you get:
Failure pattern database integration
Co-development of healthcare-specific assessment frameworks
Clinical review services for your client engagements
Custom framework development for AI safety surveillance
Structure: Partnership models tailored to your organization
Clinical Domain Expertise
Enhance your healthcare AI evaluations with documented failure patterns and clinical judgment.
From Hoping to Knowing
BEFORE:
"We believe our AI is safe based on accuracy metrics."
AFTER:
"We've tested for the failure modes that actually harm patients in clinical deployment."
BEFORE:
"We're hoping the pilot goes smoothly."
AFTER:
"We've addressed documented failure patterns before going live."
BEFORE:
"We're not sure what clinical reviewers will ask."
AFTER:
"We can answer the hard questions with evidence."
These failure modes are discoverable before deployment. The teams that catch them early build better products and move faster.