Practical AI in Healthcare

Amy Price survived a car accident that left her with a broken neck, severe brain injury, and $4 million in medical bills. She was told she'd need to be institutionalized. Instead, she earned a DPhil at Oxford and became Editor-in-Chief of the Journal of Participatory Medicine. In this episode, Amy sits down with Leon to discuss why patients belong inside the AI design process, what it really means to have a "knowledgeable human who cares" in the loop, and why healthcare AI is an unfinished system worth building on, not a broken one worth scrapping. She also shares how she uses AI tools for her own health decisions and what she's learned about closing the patient AI literacy gap.

Shashi Shankar spent nearly a decade at Genentech before a family cancer journey and a broken data landscape pushed him to build something different. His company Novellia works directly with patients — not data brokers — to collect and consolidate health records across multiple providers using SMART on FHIR. The result: longitudinal, patient-authorized real-world data that fills the gaps left by claims databases, single-site EMRs, and health information exchanges. We explore why previous PHR companies failed, how AI catches clinical data errors that humans miss, and whether Big Tech should be trusted with patient data.

When Adam Blum was diagnosed with follicular lymphoma, he tried over a dozen commercial trial matchers. None returned actual matches. So the serial AI entrepreneur built CancerBot, a free precision-matching service that assesses 100% of eligibility criteria — not the five surface-level attributes most matchers use. On this episode, Blum explains the Prompt Workbench (where biomedical experts refine extraction prompts to above 90% accuracy), how conjunctive normal form makes complex eligibility logic tractable, and why "best trial" means something different for every patient. A masterclass in AI scaffolding for healthcare.

For 37 years, Charlie Harp heard the same thing from healthcare organizations: "Our data quality is fine." They were right — for billing and scheduling. But AI changed the equation. Harp, founder of Clinical Architecture, built the PIQI framework to measure patient data quality across four dimensions: availability, accuracy, conformance, and plausibility. His PIQXL Gateway scores data on a 1-100 scale before it enters your systems — not after. Early deployments reveal uncomfortable truths: lab data averages 70% quality against USCDI standards, and one facility coded every blood test to a single LOINC code. The framework is now going through HL7 balloting as an open national standard.

Every day, patients leave US hospitals with discharge instructions they can't read. Giovanni Donatelli, CEO of The Language Group, built FETCH — a patented AI system embedded in Epic that translates discharge documents in 15 minutes with human review. He did it because he was the 8-year-old interpreting for his immigrant parents at doctor's appointments. Hosts Steve Labkoff and Leon Rozenblit explore the discharge instruction gap, the tragic cases that make it personal, FETCH's three-layer translation pipeline, the case for keeping humans in the loop, and why healthcare executives think they've already solved a problem that doesn't yet have a solution.