Guest: Mariano Garcia-Valiño — engineer and healthcare founder (3 exits; now building his fourth)
Healthcare is burning cash and patience. Mariano lays out a blunt playbook: aggregate real-world signals (labs, pharmacy fills, wearables—even spending patterns that hint at adherence), run AI to flag risk early, and route people to care before conditions explode in cost. No sci-fi. No diagnosis claims. Just practical prediction, consent-driven data, and measurable outcomes.
Cost crisis ≠ destiny: US costs outpace inflation; prevention and earlier intervention are the only scalable fix.
Data > drama: EHRs + labs + pharmacy + wearables + behavioral/financial breadcrumbs create a far clearer risk picture than any single stream.
AI's role today: Triage and risk flags—not final diagnoses. Models surface "high suspicion" and hand off to clinicians.
Privacy is the moat: Strict consent, separation from employers/insurers, and legal walls keep PHI protected and trust intact.
What signals matter: From basic blood panels and pharmacy gaps to face-scan metabolic cues—more signals = better precision.
Why consumers care: Earlier answers, fewer nasty surprises, and lower lifetime spend. Prevention is the new ROI.
Business model reality: Think subscription + outcomes, not one-off tests. The value is longitudinal.
00:00 — Why AI in healthcare actually matters now
00:34 — Meet Mariano: engineer → serial healthcare founder
06:08 — The cost curve problem and why prevention is unavoidable
07:44 — What this company really does: navigation + prediction, not diagnosis
08:01 — Remote monitoring basics: from wearables to at-home capture
09:13 — The messy truth: fragmented data, privacy laws, and integration
12:19 — How data flows in (and why employers never see it)
13:39 — Why financial/behavioral signals boost predictive power
18:13 — What AI tells you: ranges, suspicions, and next clinical steps
19:29 — The "why now" for consumers: earlier lifestyle change, lower costs
21:10 — Roadmap & what has to be true for this to scale
"We raise a flag, then route you to the right clinician. That's how AI actually saves money today."
"If you stop filling your medication three months in, the model will catch it—and that's the moment to intervene."
"Prediction beats reaction. Every time."
Health plans & clinics: embed risk-flag APIs into care navigation and care-gap workflows.
Employers: fund prevention programs without ever touching employee PHI—measure outcomes only.
Startups: focus on data rights + consent UX; it's the difference between demo-ware and deployment.
If you're building AI for real people—not hype
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