Tech E&O for healthcare AI
Healthcare AI creates questions about faulty output, reliance, sensitive data, delegated action and allocation of responsibility. A Tech E&O or cyber product can be explored with the authorised insurer; coverage depends on actual wording and facts. This page is an educational product discussion, not an existing Glacis policy. STABLE
Loss-path questions
Section titled “Loss-path questions”| Workflow | Examine |
|---|---|
| Clinical documentation | Omissions, inaccurate records, review and reliance |
| Decision support | Intended use, output quality, clinical judgement and escalation |
| Utilisation review | Decision authority, fairness, human review and appeal |
| Operational agents | Identity, tool permissions, approvals, transactions and effects |
| Sensitive-data processing | Access, disclosure, retention, security and response |
Separate the vendor’s services from the clinician’s or provider’s duties and the insurer’s claims practices. Read-only access does not prevent human reliance on generated advice. A signed record is not evidence of clinical correctness by itself. STABLE
The historical Law360 litigation examples concern allegations and procedural snapshots, not proven covered losses. Their current status is VERIFY in AI claims litigation.
Underwriting and evidence
Section titled “Underwriting and evidence”Define the insured activity, limits, exclusions, retroactive/claims terms and duties with the insurer. Consider data quality, human oversight, permitted actions, control effectiveness and capture gaps. Test what runtime evidence adds to the existing assessment. STABLE
A proposed schedule credit, admitted/E&S route, delegated authority, profit share or coverage condition requires appropriate legal, actuarial and insurer review. Receipts do not establish that any has been agreed. VERIFY all specific commercial and regulatory details.
→ AI insurance regulation · Healthcare workflows
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