Patient Before Payer

The systems meant to fix medicine are being trained on the records of the people medicine already serves, and the communities most likely to need them are the least likely to be in the data. One speaker turns real-world health data into evidence regulators will accept and led a global COVID response with the CDC. The other bootstrapped a billing company to 400 doctors and built the AI that catches the insurance denial before it happens. One works at the level of the evidence, the other at the level of the claim.

Who this is for

Clinicians, founders, health system leaders, and anyone who has watched a family member fight a denial.

On the table

• What does it take to build a clinical model that passes for everyone and not only the well insured?
• When AI sits between the patient and the payer, whose side is it on, and who decides?
• What would health infrastructure look like if it were built for the neighborhoods, and the countries of origin, the system has never reached?

You'll leave with

• Where AI in medicine is genuinely earning trust, and where it is failing quietly
• The regulatory and reimbursement realities a health startup has to build around
• Where the next serious company gets built