The Killer Diligence Question for AI Health Startups

Due-diligence question: "What's your regulatory pathway and reimbursement strategy for the next 18 months, and what concrete milestones or evidence do you have to prove you're on track?" This forces founders to articulate not just product value, but how they'll actually get paid and stay compliant in real markets.
Why it matters: At JPM 2026, investors care less about hype and more about clear plans that tie AI innovation to real clinical use and payer acceptance. If a startup can't articulate specific FDA/CE pathways or early payer engagement, it often de-prioritizes them in favor of teams who have that mapped out.

Clear fault lines protect patients and partners when mistakes occur. Contracts should state who pays for harm, who defends claims, and when caps apply. The product’s regulatory class, intended use, and warnings shape the duty of care.

Professional and product liability insurance should be current, with limits sized to real risk. An incident playbook should cover notice, root cause, rollback, and patient outreach. Ask for sample terms, insurance proof, and the full incident response plan today.

Return on investment must be proven with sound methods, not stories. Studies should define baselines, adjust for case mix, and report confidence ranges. Clinical impact may include fewer readmissions, shorter stays, or faster diagnosis, with clear units.

Financial impact may include lower cost per case, staff time saved, or revenue lift, with audit trails. Independent reviews and customer references should confirm results in varied settings. Request the economic model, raw outcome tables, and contacts for reference calls now.

Real clinical value depends on smooth, secure links to the EHR in use today. The product should read and write through standard APIs, support single sign-on, and record every action. Workflows should launch in context, place orders or notes when allowed, and respect user roles.

Code maps should match local terms so alerts and results land in the right place. Reliability plans should cover downtime, version changes, and rollback without lost data. Ask for a live, end-to-end demo in your EHR test system with clear success checks now.

Bias must be measured across meaningful patient groups before and after launch. Key measures include error rates, how well predictions match reality, and gaps across race, sex, age, and language. Results should be shown for overlapping groups, not just one trait at a time.

Mitigation plans should cover data balance, threshold tuning, and human review for hard cases. Ongoing checks should track drift and trigger safe rollbacks when harm risk grows. Request subgroup reports, mitigation plans, and live monitoring dashboards now.

Every health data point should be traceable to a lawful source with clear consent. The consent terms should name training, testing, and real-world use, not just research. Patients need a simple way to opt out, and withdrawals should be applied across all copies and uses.

De-identification and linkage methods should be explained, with audits that show low re-identification risk. Cross-border transfers, minors’ data, and later reuse should be covered by policy and contracts. Ask to review consent templates, data origin records, and audit logs today.