Practical AI in Healthcare

Jaksot

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  S2E2 - Greg Raiz: Data-Driven Early-Stage Investing 13.09.2026 55min

  Early-stage investing is one of the last places where big decisions get made on gut feel. Greg Raiz, co-founder and general partner of the pre-seed fund Founders Edge, is trying to change that with data: a survey of more than 3,000 entrepreneurs and a review of some 42 published studies on what predicts founder success. In a solo conversation with Leon Rozenblit, Greg explains why venture capital may be backing the wrong age group, why angel groups are structured to kill their best deals, who actually gets the leverage from AI coding tools, and why electronic medical records are ripe for disruption. Leon is a limited partner in Founders Edge and discloses it at the top of the episode.
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  S2E1 - Raihan Faroqui, MD: The Doctor at the Front Desk, AI on the Line 06.09.2026 49min

  Raihan Faroqui trained in internal medicine and then went to work on the front desk. As VP of partnerships at Confido Health, he puts AI agents on the phone lines of what he says are about 1,500 outpatient practice sites, handling appointments, refills, insurance checks, and billing questions in twenty languages. His framing is that healthcare never had a software problem so much as a system of record problem, and agents are the first tool that does not demand a migration. In this conversation, we get into the economics of the unanswered phone call, why the deployment model looks more like consulting than software, how they define and measure agent failure, and the question the episode keeps circling: these agents do not tell patients they are agents.
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  S1, E50 - Jeremy Harper: The Broken Adoption Curve, Ambient Scribes Nobody Can Audit, and the Knowledge We Keep Paying For and Deleting 02.09.2026 53min

  Every technology healthcare has adopted moved through the same curve: a bleeding-edge few went first and documented what broke, and the majority followed. Large language models skipped that entirely. Jeremy Harper, a biomedical informatician who has worked at Epic, Ohio State, and Regenstrief, and who wrote Large Language Models (LLMs) for Healthcare, explains what we gave up by going all at once. Ambient scribes are everywhere, and because most vendors discard the audio as soon as they transcribe it, nobody can say how often the notes are wrong. He offers a fix borrowed from de-identification, a one-question test for any AI vendor, and a lament for the reusable knowledge we keep paying for and deleting.
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  S1, E52 - Reflections #8: Year in Review 30.08.2026 48min

  Steve and Leon close Season 1 by looking back across all fifty-two episodes. They start with what surprised them (how fast conservative institutions adopted, and how slowly AI literacy is moving), then work through five conclusions a year of guests kept reaching independently: the models are no longer the hard part, almost nobody monitors these systems after deployment, an interface built for a novice can degrade an expert, "compared to what" is the question everyone skips, and money is usually the real gate. They also notice the show has started talking to itself, with guests answering each other across dozens of episodes. Several questions are left open on purpose and handed to Season 2.
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  S1, E51 - Reflections #7: When the Machine Is Almost Always Right, Who Is Still Thinking? 23.08.2026 56min

  In their seventh Reflections episode, and their fifty-first overall, Steve and Leon look back across six conversations: Mika Newton on interoperability that finally started working, Peter Embi on monitoring clinical AI after deployment, Vimla Patel on how clinicians actually reason, Renee Deehan on an engine built so it can't hallucinate, Christine Dymek on AI literacy, and Jeremy Harper on an adoption curve that broke. They decide against forcing a single grand lesson and find three threads anyway: keeping a human in the loop as a deliberate design decision, the national clearinghouse for AI errors that still doesn't exist, and whether literacy is even the right word for what healthcare workers need.
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  S1, E49 - Chris Dymek: Healthcare AI Literacy 09.08.2026 50min

  Chris Dymek came to healthcare informatics by way of philosophy, and she still thinks like a philosopher: the first question is what we actually mean. As Director of Digital Healthcare Research at AHRQ, she funded AI research, helped launch work on AI and patient safety, and wrote a request for information asking healthcare organizations how they thought about AI literacy. It was never published. She left in May 2025 and carried the work into the DCI network instead. In this conversation, she lays out the distinction at the center of her framework, knowing-that versus knowing-how, the three constituencies who each need something different, and why a literate staff and a literate patient population are what let a health system move forward without breaking things.
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  S1, E48 - Renee Deehan: Trustworthy AI for Consumer Health 02.08.2026 50min

  The internet is full of wellness advice built on a single cherry-picked study. Renee Deehan, a molecular and cell biologist who leads science and AI at InsideTracker, spent two decades building the opposite. In this episode, she explains why the core of their recommendation engine is symbolic AI, knowledge representation and reasoning, rather than a large language model: it's deterministic, fully auditable, and by design cannot hallucinate. The LLMs are fenced off to chat and summaries, while humans still write and review every recommendation against convergent clinical evidence. She and the hosts dig into a 20,000-user outcomes study, the discipline of refusing to claim causality, the MCT-oil case where the system decides not to recommend, and how a data-science team grew its own AI literacy instead of hiring it.
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  S1, E47 - Vimla Patel: Cognitive Science of Clinical Reasoning 26.07.2026 54min

  Dr. Vimla Patel has spent four decades studying how physicians actually reason, and what happens when technology ignores it. In this episode, she explains the difference between forward reasoning (the fast, pattern-driven hallmark of expertise) and backward reasoning (the slower, hypothesis-testing mode of novices), and why most clinical AI is built for the wrong one. Through two vivid cases, a textbook expert diagnosis and a near-fatal potassium overdose driven by a flawed order-entry system, she shows how good design preserves clinical judgment and bad design erodes it. She closes with a warning about confident AI and the quiet loss of independent thinking.
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  S1, E46 - Peter Embi: The Doctor Who Diagnosed Himself 19.07.2026 57min

  Peter Embi has spent his career at the intersection of medicine and informatics: coining the term "algorithmovigilance," serving as the nation's first Chief Research Information Officer, and leading AI work at Vanderbilt. He is also a patient. A rare adrenal tumor took nearly 15 years and a self-diagnosis to catch, and it nearly killed him. In this conversation, Embi connects that diagnostic odyssey to the case for monitoring clinical AI the way we monitor drugs: continuously, in the real world, across a network of institutions. The discussion runs from zebras and missed diagnoses to VAMOS, the "air traffic control tower" his team built to keep deployed models honest.
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  S1, E45 - Mika Newton, xCures: Intelligent Interoperability 12.07.2026 53min

  Interoperability has been healthcare's twenty-year broken promise, but something genuinely changed. Mika Newton, CEO of xCures, explains how provider data exchange jumped from roughly 30% to 85–90% in just two years, and why that's only half the story. Moving records, it turns out, was never the hard part. As Newton puts it, "records travel, but they don't translate" — most of a record arrives as duplicated notes and scanned images that still have to be made usable. Steve and Leon dig into the honest limits of AI parsing, the card-network model behind nationwide exchange, and why the patient is becoming the real access point to their own data.
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  S1, E44 - Reflections #6: After Five Episodes, What Still Has to Be Human? 05.07.2026 48min

  In their sixth Reflections episode, Steve and Leon look back across five conversations (Sarah Rossetti, Jeff Smith of ONC, Hugo Campos, Fred Bennett, and Zak Kohane) and decide, deliberately, not to force a single grand theme. What surfaces anyway is one question asked five ways: as AI gets genuinely capable, what still has to be human, and what makes it trustworthy? They cover Rossetti's CONCERN system that models the nurse instead of the patient, ONC giving AI agents patient-like data rights, the tension between patient empowerment and black boxes you don't understand, the Minimum Trustable Product, and Kohane's method for measuring the values a healthcare AI actually acts on. A practical tour of where good is starting to look real.
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  S1, E43 - Isaac "Zak" Kohane, MD, PhD (Chair, Biomedical Informatics, Harvard Medical School; founding Editor-in-Chief, NEJM AI) 28.06.2026 57min

  A quiet war is underway over who controls your health data in the age of AI, and the front-runners aren't the usual suspects. Dr. Isaac "Zak" Kohane, Chair of Biomedical Informatics at Harvard Medical School and founding editor of NEJM AI, traces the line from SMART on FHIR (the accidental standard he helped create in 2009) to today's battle for the doctor-facing AI layer, where companies like OpenEvidence are outrunning the EHR incumbents. He also unveils his Human Values Project, which measures the values embedded inside clinical AI models, and warns how easily payers and pharma could tune them. A two-act conversation about freeing data and guarding values.
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  S1, E42 - Live with Fred Bennett, Founder & CEO, PatientTalker 21.06.2026 58min

  For its first-ever live episode, recorded before an audience at New York Tech Week, Practical AI in Healthcare sits down with Fred Bennett, founder and CEO of PatientTalker — an ambient-AI app built for the patient rather than the clinician. (Steve Labkoff is a disclosed advisor to the company.) Bennett traces the idea to his father's cardiology visit, where three family members left with three different memories of the same conversation. The discussion covers why patients are the forgotten end-user of clinical AI, how to build a "minimum trustable product," the honest question of who pays for patient-first tools, and why the technology is rarely the hard part.
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  S1, E41 - Hugo Campos: Patient-Directed AI 14.06.2026 51min

  Patient advocate Hugo Campos spent more than a decade fighting for access to the data from his own implanted defibrillator. When the system wouldn't budge, he stopped trying to reform it and started building around it. In this episode, Hugo shows how he used agentic AI coding tools to create OpenKP, an open-source app that liberates his records from inside Kaiser Permanente, despite calling himself a non-coder. He and the hosts unpack the line between institutional AI and patient-directed AI, the discipline of having two AIs check each other, and why he believes "critical AI health literacy" now matters more than knowing how to code.https://practicalaiinhealthcare.com/
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  S1, E40 - Jeff Smith — AI Regulation, Transparency & Innovation from the Government Perspective 07.06.2026 43min

  What happens when the rules for getting AI into clinical care are written by someone who has spent his career inside both the advocacy world and the government? In this episode, we talk with Jeff Smith of ONC at HHS, the first government official on Practical AI in Healthcare. Smith walks us through ONC's proposed HTI-5 rule, including a striking move to treat AI agents as "users" with the same data-access rights as clinicians, and a new question about whether blocking data from being written back into the EHR is itself information blocking. We also dig into the limits of what a regulator can actually do, and why the real work is coordination across agencies rather than control from any one of them.https://practicalaiinhealthcare.com/https://www.youtube.com/@PracticalAIinHealthcare
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  S1, E39 - Sarah Rossetti, RN, PhD: Nursing Informatics & the CONCERN Early Warning System 31.05.2026 53min

  On National Nurses Day, Practical AI in Healthcare welcomes its first nurse: Sarah Rossetti, RN, PhD, of Columbia University. Her CONCERN early warning system takes an unusual approach to predicting patient deterioration. Instead of modeling a patient's vital signs and labs, it models the nurse's documentation behavior, since the frequency and timing of charting reflect clinical concern long before the numbers move. In a 74-unit randomized trial of more than 60,000 patients, published in Nature Medicine, CONCERN was associated with a 35.6% reduction in instantaneous mortality risk. Rossetti and the hosts unpack the method, the counterintuitive rise in ICU transfers, equity safeguards, and what ambient AI means for the signal.https://practicalaiinhealthcare.com/episodes/#S1E39More on Sarah Rossetti's work: https://www.dbmi.columbia.edu/profile/sarah-collins-rossetti/
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  S1, E38 - Reflections 5: How Specialized Does AI Have to Be to Actually Work? 24.05.2026 34min

  In their fifth Reflections episode, Steve and Leon look back across six conversations (Matt Truppo at Sanofi, Ted Shortliffe, Barry Chaiken, David Hidalgo-Gato, and Danny van Leeuwen) to ask a sharper question: how specialized does AI have to be to actually work? The throughline is depth. The LLM is a commodity, and so, increasingly, is the generalist agent. What stays scarce is specialization in a workflow, the revival of symbolic methods like knowledge graphs, the literacy that separates an AI's ~95% solo accuracy from the under-35% people get using it themselves, and leaders willing to use themselves as the test rig. After 37 episodes, the technology is no longer the question. The specificity of the work around it is.
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  S1, E37 - Danny van Leeuwen, MPH, RN, Health Hats: Patient's POV on AI Tools 17.05.2026 42min

  Danny van Leeuwen is a nurse of 50 years, a multiple sclerosis patient, host of the Health Hats podcast, and a serial member of national outcomes panels at CMS, AHRQ, PCORI, and the National Academy of Medicine. He has tried more than 100 health apps and uses five. In this episode, he explains how he uses AI to interrogate his own chart, surface symptom patterns, and prepare for clinical encounters, and he shares his Three T's and Two C's framework for evaluating any digital health technology: Time, Trust, Talk, Control, Connection. The conversation covers what patients want from AI, what they do not, and why pain, fear, and cognition still escape the data.
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  S1, E36 - David Hidalgo-Gato, Founder & CEO, Cleo Health: Going a Mile Deep on Emergency Medicine — Specialization, Design Partnerships, and the Acute Care OS 10.05.2026 49min

  David Hidalgo-Gato is the founder and CEO of Cleo Health. While more than 100 competitors were building generic ambient AI scribes, David's team chose emergency medicine and stayed with one design partner for nine months and roughly 50 product iterations before launching. The result: an average 54-minute time savings per shift, a patient-assignment tool that turned a four-hour process into 15 to 20 minutes, and use across 400+ hospitals nationwide. The conversation covers why ED workflow breaks generic ambient scribes, why generative AI fits patient assignment specifically, and David's argument that workflow understanding is the moat AI cannot commoditize.https://practicalaiinhealthcare.com/
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  S1, E35 - Barry P. Chaiken, MD, MPH: Physician-as-Patient Perspective on AI in Healthcare 03.05.2026 53min

  When physician Barry Chaiken was diagnosed with prostate cancer, his clinical training gave way to fear. It took a friend asking, "What are you doing?" to snap him back into doctor-mode thinking. That experience reshaped how he sees AI in healthcare. In this episode, Chaiken draws on his dual perspective as physician and two-time cancer survivor to argue that consumer health AI is failing patients, not because the models are bad, but because patients don't know how to use them. He shares a practical framework for AI-assisted patient education, makes the case for an aviation-style safety reporting system for healthcare AI, and explains why interoperability is an incentive problem, not a technology problem.