Guest: Mariano Garcia-Valiño — engineer and healthcare founder (3 exits; now building his fourth)
Episode SummaryHealthcare 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.
Key Takeaways-
Cost crisis ≠ destiny: US costs outpace inflation; prevention and earlier intervention are the only scalable fix.
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Data > drama: EHRs + labs + pharmacy + wearables + behavioral/financial breadcrumbs create a far clearer risk picture than any single stream.
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AI's role today: Triage and risk flags—not final diagnoses. Models surface "high suspicion" and hand off to clinicians.
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Privacy is the moat: Strict consent, separation from employers/insurers, and legal walls keep PHI protected and trust intact.
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What signals matter: From basic blood panels and pharmacy gaps to face-scan metabolic cues—more signals = better precision.
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Why consumers care: Earlier answers, fewer nasty surprises, and lower lifetime spend. Prevention is the new ROI.
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Business model reality: Think subscription + outcomes, not one-off tests. The value is longitudinal.
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00:00 — Why AI in healthcare actually matters now
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00:34 — Meet Mariano: engineer → serial healthcare founder
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06:08 — The cost curve problem and why prevention is unavoidable
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07:44 — What this company really does: navigation + prediction, not diagnosis
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08:01 — Remote monitoring basics: from wearables to at-home capture
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09:13 — The messy truth: fragmented data, privacy laws, and integration
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12:19 — How data flows in (and why employers never see it)
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13:39 — Why financial/behavioral signals boost predictive power
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18:13 — What AI tells you: ranges, suspicions, and next clinical steps
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19:29 — The "why now" for consumers: earlier lifestyle change, lower costs
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21:10 — Roadmap & what has to be true for this to scale
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"We raise a flag, then route you to the right clinician. That's how AI actually saves money today."
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"If you stop filling your medication three months in, the model will catch it—and that's the moment to intervene."
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"Prediction beats reaction. Every time."
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Health plans & clinics: embed risk-flag APIs into care navigation and care-gap workflows.
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Employers: fund prevention programs without ever touching employee PHI—measure outcomes only.
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Startups: focus on data rights + consent UX; it's the difference between demo-ware and deployment.
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