S1, E39 - Sarah Rossetti, RN, PhD: Nursing Informatics & the CONCERN Early Warning System

S1, E40 - Jeff Smith — AI Regulation, Transparency & Innovation from the Government Perspective

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://practicalaiinhealthcare.com/] https://www.youtube.com/@PracticalAIinHealthcare [https://www.youtube.com/@PracticalAIinHealthcare]

S1, E39 - Sarah Rossetti, RN, PhD: Nursing Informatics & the CONCERN Early Warning System

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/#S1E39 [https://practicalaiinhealthcare.com/episodes/#S1E39] More on Sarah Rossetti's work: https://www.dbmi.columbia.edu/profile/sarah-collins-rossetti/ [https://www.dbmi.columbia.edu/profile/sarah-collins-rossetti/]

S1, E38 - Reflections 5: How Specialized Does AI Have to Be to Actually Work?

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.

S1, E37 - Danny van Leeuwen, MPH, RN, Health Hats: Patient's POV on AI Tools

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.

S1, E36 - David Hidalgo-Gato, Founder & CEO, Cleo Health: Going a Mile Deep on Emergency Medicine — Specialization, Design Partnerships, and the Acute Care OS

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/ [https://practicalaiinhealthcare.com/]