One-Liner
A data bridge connecting therapist clinical session data to disability insurance underwriters to improve risk pricing for the fastest-growing disability claim category — mental health conditions.
AI Thinking Process
Pet Insurance Vet Data Feed analogy applied to human health. Mental health provider data → disability insurance underwriters. Mental health = fastest-growing disability claim category. Data exists, buyer exists.
Consent barrier analysis: 42 CFR Part 2 (substance abuse), state mental health privacy laws. Patients NEVER consent to sharing mental health data with entities controlling their coverage. This is structural, not technical. G004 (structural adoption barrier): patient has identity-level resistance — sharing data threatens coverage.
KILLED: Fundamental consent barrier. No patient would consent to sharing mental health session data with disability insurer. Structural impossibility rooted in adversarial incentives between patient and insurer.
Tried: anonymized/aggregated population-level data. Failed: anonymized mental health trends available through public health datasets already. No middleware needed for data that's already public.
Kill Reason
Fundamental consent barrier: patients would never consent to sharing mental health session data with their disability insurer because doing so rationally threatens their coverage — insurers might deny future claims or raise premiums based on treatment history.
Risk Analysis
Risk analysis available for latest engine ideas.
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killed: Fundamental consent barrier: patients would never consent to sharing mental health session data with their disability insurer because doing so rationally threatens their coverage — insurers might deny future claims or raise premiums based on treatment history.
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