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.

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.

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.

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.

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.

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/

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

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

More on Sarah Rossetti's work: https://www.dbmi.columbia.edu/profile/sarah-collins-rossetti/