The Insurability Problem in Healthcare AI
A Standards-Based Framework for Underwriting Risk Assessment
A single AI failure in a clinical setting can simultaneously generate product liability claims against the vendor, malpractice claims against the supervising physician, and enterprise liability claims against the health system. No existing insurance framework addresses this convergence. This paper proposes a systematic path forward.
The Standards-Proof Framework
Rather than waiting for actuarial data that won't stabilize for a decade, the framework repurposes international medical device standards that healthcare AI companies already implement for regulatory clearance, converting compliance work into underwriting evidence.
Foundation
Product Liability
Risk management quality across ISO 14971, IEC 62304, ISO 13485, and AI-specific extensions. Scores implementation depth, not binary compliance.
Healthcare-Specific Validation
Professional Liability
Validation by risk tier. Tier 1 (clinical decision support): prospective studies with subgroup stratification. Tier 2 (documentation, prior auth): harm-pathway-matched validation. Tier 3 (administrative): harm pathway analysis before accepting the classification.
Continuous Operational Assurance
All Domains (Runtime)
Pre-deployment adversarial stress testing plus post-deployment tamper-evident, inference-level monitoring of drift from baseline intent. Attestation that safety controls actually executed.
Four Case Studies
Patient-Facing Triage Chatbot
Vendor reported 91% appropriate routing. Stratified by time-sensitive conditions, the undertriage rate was 14%. Prompt injection overrode scope boundaries in 7% of attempts.
Prior Authorization AI
Classified as "administrative" but directly affected patient access to care. Assessment found 6% racial disparity in authorization rates, embedded in decades of training data.
AI-Assisted Colonoscopy
FDA 510(k) clearance based on academic centers. Deployment population was community practices. Only 9% of FDA-approved AI devices include prospective post-market surveillance.
Clinical Documentation AI
"95% accuracy" across 40,000 encounters/month. Stratified: ~120 notes/month contained hallucinated allergies, invented symptoms, or omitted findings, concentrated in the most complex patients.
Get the Complete White Paper
36 pages including four case studies, the complete Standards-Proof framework, parametric coverage mechanisms, and recommendations for insurers, AI companies, health systems, and clinicians.
Jennifer Shannon, MD
Chief Medical Officer, GLACIS Technologies
UW-trained psychiatrist. Previously helped develop the first FDA-authorized AI diagnostic device for autism at Cognoa.
Sarah Gebauer, MD
Validara Health
Healthcare AI risk assessment and clinical validation.