The Insurability Problem in Healthcare AI
A Standards-Based Framework for Underwriting Risk Assessment
Depending on the facts and jurisdiction, a clinical AI failure may implicate product, professional, and enterprise liability. This research paper proposes a systematic way to organize underwriting diligence across those exposures.
The Standards-Proof Framework
The framework asks whether evidence produced under applicable medical-device and quality standards can inform underwriting diligence, then adds healthcare-specific validation and operational evidence. It does not assume every healthcare AI system is a medical device or that standards conformance determines coverage.
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 testing plus post-deployment operational records within a defined coverage boundary. A signed record can report the named control and recorded decision while supporting integrity and signer-attribution checks; it does not by itself prove that the control was effective or that the system was safe.
Four Illustrative Composite Cases
These scenarios are generalized teaching examples from the paper, not named customers, products, policies, or insurer decisions.
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. In a scoping review of 692 public FDA authorization documents from 1995 to 2023, 62 (9.0%) contained a prospective study for post-market surveillance; that is a reporting finding about the reviewed documents, not a current percentage of all authorized AI devices.
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.