Health Data Ethics

This week's Health Data Ethics podcast continues our series on the Joint Commission and CHAI guidance on the responsible use of health AI. In this episode we're digging into privacy and transparency.The guidance itself is reasonable. What I spent most of the episode on is how you actually implement it, because that's where things get interesting.Adding AI language to the Notice of Privacy Practices is a good first step, and a lot of health systems are doing it. But I think the most-told lie in modern life is still "I have read and agreed to the terms and conditions." Broad disclosure is honest, and it matters, and it's also not going to carry the whole weight of a transparent relationship with your patients.The piece I really wanted to dig into is opt-outs. If you offer patients the ability to opt out of something you can't actually turn off, you've built opt-out theater, and that erodes trust faster than just being honest about the limitation would. Ambulatory scribe is a real opt-out. Inpatient sepsis prediction is not technically feasible to opt out of, and we probably shouldn't pretend it is.I also spend some time on the clinician side, which I think gets short shrift in a lot of these conversations. Operational training on a tool is not the same thing as understanding how the model behaves, where it fails, and which patients it might be wrong for. Clinicians are the ones carrying accountability for human-in-the-loop judgment, and they need real explainability to do that well.

Getting an AI policy approved in a large health system is a different skill than writing one.In part two of my AI policy series on the Health Data Ethics Podcast, I share what months of drafting, socializing, and navigating formal approval at Cleveland Clinic actually looked like: the champions you need, the scope battles you'll face, and why the approval process is won or lost long before the policy enters formal review.The biggest takeaway: identify domains where your scope overlaps with someone else's, and get those leader in the room early before formal review even starts.

Writing an AI policy sounds straightforward — until it becomes the place where everyone in your organization hangs all their hopes and dreams for AI governance.In this episode of the Health Data Ethics Podcast, I walk through the first item on the Joint Commission and Coalition for Health AI's responsible use guidance: establishing an AI policy as your governance foundation. I share what we learned working on Cleveland Clinic's AI policy in late 2024 — before the JC/CHAI guidance even existed — including the structural traps that slow policies down, and why pre-approval stakeholder alignment is so important. If you're starting from zero or trying to get a stalled draft across the finish line, this one's for you.

Part two of my breakdown of the White House National AI Policy Framework — what it says about workforce, what it leaves out, and what it would take to become law.The workforce section has good instincts but no mandates, no funding, no timelines. In healthcare, this can create a patient safety problem, if our health systems don't fill this gap thoughtfully.The legislative road is crowded and uncertain, but even if codified into law, this federal posture is a light touch with the rest landing on health systems.

This week's episode of the Health Data Ethics Show: The White House just released a four-page legislative framework asking Congress to pass national AI policy this year. Not a law. A wish list, but one that tells us a lot about where federal AI policy is heading.In this episode I break down what it means for healthcare governance: the preemption debate, FDA as the designated health AI gatekeeper, and the notable absence of HIPAA from the entire document.The governance responsibility has always sat with health systems. This framework confirms the federal government intends to keep it that way.