I usually ask the questions. This time I’m the guest.

I met Steve Labkoff and Leon Rozenblit a couple of years ago at a DCI Network conference. They host Practical AI in Healthcare, a show I’ve listened to steadily, though it creates more tension for me than any other podcast I keep listening to. Usually, I jettison podcasts that do that. I stay with this one because I approach AI in healthcare the way I approach health: I’m an N of one and resist generalizing, while most guests do a fair amount of it. I bristle at most of them, wanting the shades of grey that reflect deep understanding. In four of 44+ episodes, the guest has had lived experience: ePatient Dave DeBronkart, Amy Price, Hugo Campos, and me.

I invited Steve and Leon to join my virtual Reckoning group, which I’ve hosted since 2019, where we give podcasters warm critiques of selected episodes: the kind of feedback you give when you’ve made a hundred mistakes yourself, can spot them quickly in someone else’s cut, and have endless thoughts about production, audience, dissemination, and life. They took the critique well, more openly than most. So, when Steve later asked me to come on his show and talk about how I use AI, not the theory but the daily grind, I readily agreed, and they let me publish the audio unchanged. This newsletter follows my usual pattern of editing for readability and flow. You can find the verbatim transcript in the show notes.

I struggled to prepare for this conversation. I wanted to wear all my hats but had to narrow my scope to two. I chose my lived experience and nurse hats.

Underneath it all was the question I keep circling back to. Not a cure. Best health, the most function, and the least suffering available to me right now, given what I actually have. Here’s the conversation.

The cape unfurls - superpowers

Steve Labkoff, host of Practical AI in Healthcare, asked me how I got my superpowers. As a teen, the Vietnam War draft frightened me. I felt adrift, with no purpose. When I was 16, I sought draft counseling at a church in Detroit. They invited me to become a counselor myself. I learned the craft of changing systems from within. Not a revolutionary, but a what’s-the-next-step kind of person. Nursing came almost by accident. At 17, I had to choose between reading water meters and working as an aide at the Detroit Psychiatric Institute. The meter job paid more, but I had to cut my hair. I absolutely refused to cut my hair, so I took the aide job. The nurses introduced me to the idea of nursing. Voila, nursing school!

After nursing school, my first jobs weren’t in acute care. In 1976, I was the first male public health nurse in Western Massachusetts, providing home care and physical rehabilitation. I learned that most healthcare doesn’t happen in the medical system. It happens outside it. Over the next two decades, I worked in the emergency department, the ICU, pediatrics, and behavioral health. I then shifted from studying individual health to studying organizational health: performance improvement, electronic health record (EHR, the digital version of a patient’s chart) implementations, and a few stints in the C-suite (senior leadership).

Then, in 2009, I was diagnosed with multiple sclerosis and learned I’d had it for 25 years. Every time I’d had an MS episode, I’d undergone a cardiac workup instead. My father died of a heart attack at 45. The pattern had been in my records the whole time. Nobody had connected the dots.

Checked Boxes to Seat at the Table

I call myself Health Hats because I wear a lot of them: patient, caregiver, nurse, advocate, informaticist, podcast host. Wearing that many hats has earned me a seat at many tables: technical expert panels at the Centers for Medicare & Medicaid Services (CMS), the National Academy of Medicine (NAM), the Agency for Healthcare Research and Quality (AHRQ), the National Quality Forum (NQF), and the Patient-Centered Outcomes Research Institute (PCORI). Organizations could check many boxes by engaging me as a patient representative. A safe addition when they tried something new.

Although I learned a great deal, I was never there for a seat, but rather to open seats for people who weren’t there yet. An early example was a seat in 2013 on the Office of the National Coordinator (ONC)’s Blue Button Plus initiative. It was a federal effort to standardize how patients could retrieve their health data from clinical systems. I was selected because of my many hats. I spent two years on that project, trying to add a caregiver field to the data set and to get anyone to care about data informing the variability of pain, fear, and cognition. Fear closes your heart. It closes your mind. When you’re scared in a clinical encounter, you’re not making good decisions. You’re just saying yes to end it. And cognition varies. I can absorb better at 10 in the morning on a good day compared to 3:00 in the afternoon when I’m spent.

The caregiver field was added. The pain and fear never gained traction. Wearing my nurse exec hat, I found that my colleagues saw the caregiver field only as something to fill in, not to use. Perhaps if I had been able to add more patient representatives, I would have had more success.

I’m an N of One

I want to be clear that I’m a privileged white old man with MS living in Boston, but I’m an N of one and don’t represent other patients, let alone other privileged old white men with MS. I’m representing myself and my perspectives. My health goal is best health, which I define as optimal physical, mental, and spiritual health and function. Not a cure, but best health for where I am and what I have right now. To get there, I need my own health data, not just what’s in my clinician’s chart, but what I know about myself, my circumstances, my environment, my history, and my habits. Not just my medical history, but my life history and my treatment responses. That’s my patient-reported data. Stuff that only exists because I observe it, and sometimes I record it.

Hundreds of Pages, Zero Information

That gap between collecting data and using it followed me into my own care. Six months before this conversation, I decided to gather everything my doctors had on me from 2011 to 2025. Two months later, I got a box: a four-pound box of paper from my primary care practice’s records vendor. Not in chronological order. Not digital. An actual box. Hundreds of pages to scan. The health system where many of my specialists practice provided a file of three months of records in fifteen minutes. Eight visits generated 296 pages of redundant, non-searchable PDFs. Fast and useless, which is a different problem than slow and useless, but still not usable. Another health system provided nothing.

Access to data and access to usable data are not the same thing. That’s not a complaint about any one health system. It’s the whole game. My goal isn’t a cure. It’s best health, the most function, and the least suffering available to me right now, given what I actually have. Getting there requires my own data: not just what’s in a clinician’s chart, but what I know about my circumstances, environment, habits, and life history. Most of that exists only because I observe it and occasionally write it down.

As a Nurse, My Job is to Put Myself Out of Business

Here’s the nursing angle, which I don’t get to use as often as I’d like anymore. My goal as a nurse was always to put myself out of a job. From minute one with a patient and family, I’m planning my exit. That takes maximum face time, real presence, real conversation, not more charting, not more hunting through information I can’t find.

That’s where I think artificial intelligence (AI) could earn its keep in nursing: pattern recognition across a cohort of patients. When I was an emergency nurse in rural West Virginia, if I’d had more readily available information about my patients and their families, AI could have helped me surface the patterns that already existed. I’d get time back as a nurse. If the nurse gets time back, the patient and family get the nurse’s presence instead of her paperwork. That’s the trade I’m actually interested in.

Unfortunately, my experience with increasing a clinician’s productivity results in fewer support staff, which leads to unaccounted-for tasks being added to nurses’ work. Less time with patients.

Toolkits and 3 T’s 2 Cs

For my own health, I’ve built toolkits. I have a constellation of symptoms. I’m not MS. I’m not my symptoms. My symptoms loom large. I have pain, anxiety, bladder issues, and mobility challenges. I need a toolbox. I keep spreadsheets, a journal (my podcast), and large language model prompts (LLMs like Claude or ChatGPT) that I feed my data into. I ask, “Do you see a pattern in this?” Sometimes what comes back is almost embarrassingly simple: “Have you thought about seeing a physical therapist?” when I clearly hadn’t. Once, it flagged a mood change from a neuropathy medication as a possible side effect before I’d made the connection myself. My neurologist agreed and entered it in my record as an allergy.

My neurologist adds the new visit note at the bottom of a years-long note. I reverse-engineered his notes. I gave Claude ten years of my neurologist’s notes and asked how I was actually doing, tracking my progress. Claude identified the specific rating scale he’d been using but never shared with me. I asked my neurologist to put the scale at the top of his note.

None of this makes me trust AI uncritically. My rule is to sleep on anything it gives me and check it again, because the first read is always impressive and the second usually reveals what is underwhelming, wrong, or missed. That’s part of why I argue for keeping a human in the loop, even though there’s real research, including Adam Rodman’s workᶦ, showing that human-in-the-loop review doesn’t always improve the outcome. I don’t care what studies say. Keep the humans in the loop anyway.

Over the years, I’ve built a framework for judging any digital health tool, the Three T’s and Two C’s (Time, Trust, Talk, Control, Connection). Time (you need it to learn and build trust), trust (it can’t be a shortcut), talk (real conversation, which AI can help you prepare for but not replace), control (I decide what goes in and who sees it), and connection (the human lifeline no tool can manufacture, though a good one can make room for it).

Reflection

Looking back, a few threads keep surfacing. The four-pound box of paper and the 300 unsearchable PDFs are the same problem in two costumes: access to data and access to usable data are not the same thing, no matter how fast or slow the system hands it over. My whole career as a nurse was built on trying to hand myself a pink slip, more presence, less charting, and I still think that’s where AI could actually earn its keep, if it doesn’t just get absorbed into fewer support staff and more unaccounted-for tasks instead.

What I keep learning is that none of this is about the tool being smart. It’s about who has the patience to ask the right question twice and who’s willing to sit with an answer overnight before trusting it. My toolkit, the spreadsheet, and the ten years of notes I fed to Claude only work because I stay skeptical of it, the same way I stay skeptical of any clinician until I’ve built some trust. The Three T’s and Two C’s aren’t really about AI at all. They’re about how I decide who and what gets access to my attention and my trust, whether machine or human.

So, here’s what I ask of you. If you’re a patient: use AI, keep using it, but find a buddy to do it with and talk to your clinician about it. Their reaction alone tells you something. If you’re comfortable with it, mentor someone who isn’t. And if you’re building these tools: get patients, caregivers, and practicing clinicians into the room from day one, not after the design is done and you’re looking for validation.

See you around the block.

i “Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial” — Ethan Goh, Robert Gallo, Jason Hom, Eric Strong, Yingjie Weng, and colleagues including Adam Rodman, published in JAMA Network Open, October 28, 2024.

Melissa Reynolds and I bonded when she invited me to review her book on pregnancy, fibromyalgia, and chronic fatigue syndrome in 2019. That still makes us both laugh: a man had written one of the blurbs on the back cover. I thought it was a riot. Melissa thought it made perfect sense because the people who most need to understand what a pregnant body is going through are often the ones standing next to it, trying to help but not quite getting there.

Although we follow each other and frequently comment on each other’s posts, our last real conversation was in 2020 about a yoga program she was starting. A few small things from that conversation are still part of my every-other-day stretching and balance routine.

I’m drawn to Melissa because she accepts what is, including that hard-to-live-with body, and creates and shares tools for those of us with the same or different diagnoses but similar lived experiences. All for best health.

Our friendship has grown virtually, so we can pick up where we left off. This time, I reached out to Melissa after seeing her posts about her exploration of AI.

Alone in the dark with a question

Health Hats: What lessons are you learning as you use AI?

Melissa: It’s funny to say you use AI because it’s hard not to use it now. But I’ve started exploring how AI can support me on my health journey. For a while, I was using it for bits and pieces. Then this gut issue came up. I don’t know if you’ve seen much of the journey, but I suddenly developed severe gut issues. They sent me for stool tests, which I’d never done before, and the results came back abnormally, astronomically high, so they sent me to the hospital.

Melissa: They ran all sorts of tests. They rushed me through a colonoscopy, and then I was sitting there on my own in the dark in this hospital room. It’s an ADU unit, so it’s for diagnostic purposes. It’s not a ward. There was no TV, hardly anyone around, and I was quite alone, with no idea what could happen next.

Melissa: So, I went into Claude and explained what had happened, and I said I needed to know, statistically, what was likely going on. It talked me through what it could be. That was enough for me to relax and go, okay, that’s cool.

Health Hats: Where does it stand now?

Melissa: Until a week ago, it looked very likely it was going to be one of those irritable bowel diseases. But right now, we’re completely unclear. I’ve got more specialists to see. But I realized the applications, so I started researching.

Deciding to use every tool

Melissa: Look, I’ve been sick for 20 years. I’ve been mistreated more than I’ve been well treated, and I’ve lost half my life. A lot of the doctors I saw were, meh. In the last 10 years, I’ve improved my life dramatically, but what upsets me is that I’m still nowhere near normal. That means I was very sick, and most of the doctors I saw were like, meh, even though there were concrete things to treat. They were misdiagnosing me. They were not treating me.

Melissa: So I thought I was going to use every tool I had available. I actually told Claude, “Okay, you know my history. We’ve been chatting for a while. Tell me how I can use what you can do better.”

The fatigue was signaling two days early

Melissa: I do a lot of data analysis in my part-time job, so I thought, let’s get serious about my data analysis. I moved my symptom tracking from a physical book to a spreadsheet. Then I created a prompt where I upload it once a month and say, “Here’s my data. Tell me what you’re noticing that I’m not.” It notices things I don’t.

Health Hats: Like what?

Melissa: It was the post-exertion malaise flares that I wasn’t quite understanding.

Health Hats: Post-exertion malaise. That’s the blowback from overdoing it, the hallmark of ME/CFS and other energy-limiting conditions?

Melissa: Yes. It also picked up that when I was having my gut flares, my fatigue would signal a couple of days beforehand. Every time I had a gut flare, my fatigue would worsen beforehand. So, it’s now pretty clear that whatever’s going on with my gut is systemic. It’s part of a larger situation, not just related to my gut.

Melissa: The data analysis and the research have been so helpful. I say, do some deep research, and I want you to talk to me about this topic, and it does. But you have to be very clear about what you want it to do. There’s a lot to learn about prompting. It’s very nuanced.

Smiling, nodding, and using it anyway

Health Hats: How do the clinicians you’re partnering with respond? Are they curious or suspicious? There must be a range of responses.

Melissa: It depends. My gastroenterologist keeps saying, “Oh, I hope you’re not using that,” and they always say ChatGPT when they mean AI. So I’m smiling and nodding, but obviously I was. My GP, though, is fantastic. She loves it when I bring her research. She’s engaged. If you’re comfortable with people googling, then AI is just the next step. It’s more efficient than googling.

Melissa: And I never go to her and say, “I’ve self-diagnosed myself with this.” It’s more like, “I’ve done some research.” Here’s a practical example. The gastroenterologist suggested a medication, and I don’t feel comfortable taking it. Even though they downplay the interaction with another medication I’m on, I don’t feel comfortable with the overall risk, especially when you’re playing with heart rate and blood pressure. I have low blood pressure and heart rate issues.

Melissa: The wonderful thing about AI, compared to what I can do on a hard day, is that it can pull things together. We were talking about this medicine, and it found an alternative, a lower-risk medicine that also supports this other thing. The one thing I don’t want is to end up on loads of medicines and not be sure what’s working. A doctor is surely happy to have me as an informed participant in my care, especially when chronic conditions require patient buy-in.

Where the records actually live

Health Hats: You’re in . I always wonder how the culture and politics around medicine and these tools differ from those here, where it’s a bit of a free-for-all and the guardrails are thin.

Melissa: We’re in a very different situation. For a start, we’re a public system, but it’s crumbling. You have the people reliant on it, the people failed by it, and the few who can afford private insurance, which mostly just means you see the same people without being gatekept. We’re very segregated. Each specialty focuses on a single organ. As far as I know, we have one multidisciplinary clinic for long COVID, and it’s in the South Island, so I have no access to it, even though my ME/CFS came on after a viral illness and I’d benefit from exactly that.

Melissa: What we do have is one public record that’s stayed with me, and a recent change that allows patients to request any information an organization holds about them. That’s actually how a lot of things changed for me. I got access to my patient portal at 32, and that’s how I found out I’d been diagnosed with chronic fatigue syndrome. No one had told me. They’d just written it in there.

Health Hats: As opposed to all the times you were misdiagnosed, with both false positives and false negatives. And pulling it all together is the trick. I have a four-pound box of paper from one office, 500 pages, and 291 pages of PDF from another for three months of visits, all out of order and wildly redundant. So much of it is wrong. You start to realize that, at best, it’s grade-D information, and what I put in my journals and spreadsheets is probably the most accurate, which a doctor would never agree to.

Melissa: It’s the same here. The onus is still on the patient to gather it all and then use it. That’s a whole other thing, and it’s something I’ve always struggled with.

A very powerful toy

Health Hats: What words of wisdom do you have for people who are using these tools? Do you want to encourage them or caution them?

Melissa: First, think about what state you’re in. If you’re a bit vulnerable, don’t feel confident with technology, or are unsure about any of it, then seek guidance. Have a buddy or a mentor to do it with.

Melissa: If you’re like me, data-oriented and logical, deep research is great. But if you’re someone who needs minimal information and more would fry your nervous system, then either don’t do it, ask someone to do it for you, or tell the AI, “I don’t need lots of detail; give me the three key points I can take away.” You can always guide it. Many people use it like they’re talking to someone, which can be useful when you’re working through things. But if you can prompt it well, you’ll get what you need.

Melissa: That’s why I’m writing a series of articles. I want to guide people so they can focus on one thing, like how to use their data to get good analysis, because it’s a lot. First, you’ve got to learn how to prompt, then what to put in, then how it works. My articles are trying to make it more accessible. It’s always us, the people who are chronically ill, who are least able to jump on opportunities and make the most of them, and we’re the ones who need it most. But if you’re worried about it or opposed to it, leave it.

Health Hats: I’m not a black-and-white person; I’m more nuanced. It helps with some things but not others. One thing I’m struggling with is that it gives me too much to share, and I want to share all that depth. Maybe it’s useful for me, but not for other people. So, I’m learning to set limits. My audience has three minutes or 500 words. Then I can ask more questions. It’s amazing. It’s a toy, in a way. A very powerful toy.

Melissa: Thank you so much. I can’t believe it’s been so long.

Health Hats: I know. Do we need to make an appointment for another four years?

Melissa: No, let’s do six months.

Health Hats: Sounds good.

See you around the block.

Reflection

Neither of us is going to be cured, whatever that word even means. But I am living a good life. I am playing music, traveling, and in love. My grandson just turned eighteen and is graduating from high school. Life is good. That is the whole point, really. The point was never the technology.

I know my enthusiasm for using Claude turns some people off. A number of you seriously distrust anything with AI in it, and I don’t dismiss that. I’m uneasy too, less about the tool in my hands than about the AI-industrial complex behind it, the money, power, and momentum, something like splitting the atom: enormous force, no guarantee of where it gets pointed. And yet here I am, using Claude and Claude Cowork to cut the forty to sixty hours I spend on each episode down to about twenty. I’ll share how in future episodes. I hold the worry and use the tools anyway.

The point is deciding to drive our own train and being glad to have one more tool in the cab. A tool, a toy used best by someone who knows their own mind and keeps both hands on the wheel.

Referenced in episode

Melissa’s Substack

Melissa’s book on pregnancy, fibromyalgia, and chronic fatigue syndrome

Melissa’s yoga program

Melissa’s book: Fibromyalgia Won’t Win: Learning, Loving and Living with Chronic Pain and Fatigue (Melissa vs Fibromyalgia The Collection)

https://linktr.ee/healthhats

https://health-hats.com/pod248/

Topics covered:

• Open visiting hours in the ICU — and what happened when staff pushed back

• Seven therapy visits, no prior authorization required — and what happened when the company was acquired

• A disability services resident on a board of directors — and the simple fix that improved every patient experience metric

• Why participatory governance is the fastest, cheapest diagnostic tool most health system leaders never use

• The honest difference between patient advisory boards and actually sharing power with patients

• What patient-centered care looks like when it moves beyond consultation into real shared decision making

In this episode, Danny_Health Hats uses the sausage-making analogy to explore what transparency in healthcare AI really means: what we want to know, what we can handle knowing, and what we should be demanding.

“Values are hard. Slogans are easy.”

Danny moves from personal curiosity to a challenge for seekers, solvers, and communicators in the health space. How do business interests shape the push for transparency? Who’s doing the work? And what’s your next step?

Watch the full episode:

My friend Mark Hochgesang, former Nike exec and host of Heavy Hitter Sports, recently interviewed me. While I usually wear my life on my sleeve on Health Hats, this conversation revealed something different—how I think about myself as an adaptive athlete.

Phil Knight’s mantra: “If you have a body, you’re an athlete.”

I never thought of it that way until Mark helped me see it. Training to travel? That’s athletic training. Loading a 60-pound wheelchair into an SUV? Strength work. Walking 3,500 steps a day with MS? Competition with myself.

Here’s what we covered:

🧠The Swiss cheese brain scan – My MS diagnosis story (turns out I’d had it for 25 years)

🎷The baritone saxophone – My neurologist’s #1 prescription for MS. Music creates new neural pathways.

🚶The 3,500-step goal – Why movement is non-negotiable, even with foot drop and proprioception issues

💪 The “every other day” rule – Setting yourself up for success (stretching, balance, upper body work, squats)

😤The two-minute bitch – No “happy horseshit” allowed. Life with MS sucks sometimes. Two minutes to vent, then move forward.

🌍Training to travel – From 70 miles of Camino de Santiago to just returning from Belize (videos coming!)

👥The team – Wife (OT), sons, grandkids (my scouts!), medical team, massage therapists, the Cuban van driver who didn’t speak English

🎯The win – People understanding that disability takes many forms and asking “How can I help you?” instead of aggressively helping

💡The legacy – Being remembered as “the cool Opa”

The biggest lesson? Build a team. You can’t do this shit alone.

Listen to the full episode: https://health-hats.com/pod244/
Everything Health Hats: https://linktr.ee/healthhats

The Bottom Line

I’m going to spend the next few months connecting with you, my community—podcast and social media channel subscribers, followers, friends, and colleagues—focusing on TRUST. I’ll share nuggets I’ve learned, suggest prompts, ask and answer questions, and respond to others’ channels on trust.

Everywhere and Nowhere

Unsurprisingly, the word of the year for me is TRUST. When has trust not been paramount in history? Never, I think. More than 120 of my 600+ episodes have included trust, with 25-30 primarily on the topic.

My mission is ‘Learn with people on the journey toward best health.’ Learning is built on trust. Embarking on a journey requires trust. Best health is an uncertain destination. Comfort with uncertainty entails trust. Decision-making is easier with trust.

Model of Trust

Researchers break TRUST down into interpersonal trust—do I trust you? —and organizational trust—do I trust this hospital?—and then way up into societal trust—do I trust the healthcare system itself? There’s a 1995 model by Mayer, Davis, and Schoorman that keeps showing up in the research. They say trust boils down to three things: Can you do what you say you can do? Do you actually care about me? And do your actions match your words? Ability, benevolence, integrity. Simple framework, massive implications. There’s a 2007 update that looks back at what has happened in trust research since 1995. I especially bonded with sections on affect and emotion in trust, on distrust separate from trust, and on cross-cultural tensions.

Trust in Self

Missing from these articles is trust in myself. Do I trust my ability to manage my health and own my life? What a can of worms—self-knowledge. Understanding my risk tolerance, my culture, my history. I may be a trusting soul at heart, but I’ve been burned. I’m curious, but a skeptic. It affects trust. But I can control myself more than anything.

Comfort with uncertainty

I’m daunted by the stormy sea of trust. Gosh, I’m scared of water and a low-confidence swimmer. The sea of trust is massive, too much for me to get my brain around. I need a cove to focus on - boundaries. You know me. I can spin anywhere. No, Danny, focus.

I want to explore the trust we need to make health decisions. Think about it. Someone hands me my lab results—that’s data. How does that become information I trust enough to act on? My doctor says I need surgery—what makes me believe her? I’m looking at my health data, trying to figure out next steps—what combination of data quality, sensible interpretation, and relationship strength tips me toward yes or no?

I keep coming back to this intersection: a trustworthy self, trustworthy data, and a trustworthy person or source interpreting it for or with me. That’s the slice I want to dig into over the next few months. Not trust in general—trust in the specific moment when someone with a health concern must decide what to do next. What data matters? All of it? Some? Which part? What would you do with it? What approaches, tools, and methods do you use to clean it up and trust it?

Calculated Risks

When exploring, I need to feel safe. I’ll take calculated risks, but I won’t set sail without a team, provisions, a destination, and a sextant (or other guiding tools).

What’s next

So, what’s next? I’ll start with a Book Club format, well, not books. Too long. I don’t have the time - perhaps a story, a quote, a chart, a comment made, or an article. I’m going to partner with my buds, Laura Marcial, Amy Price, and Christine Von Raesfeld, on Substack. Maybe include LinkedIn and YouTube. Social media will have short or shorter content - Two to 15 minutes. Of course, my podcast will be included - combining shorter stuff. We’ll see how it goes. We’ll figure out some kind of rhythm—maybe we’ll even end up with something publishable. Who knows? That would be fun. Spread the wealth.

I’m depending on you to join me. Onward!

#CommunityResearch #PowerDynamics #InformationParadox #EquityInPractice #MixedMethods

40 min episode and show notes https://health-hats.com/pod241

Everything Health Hats https://linktr.ee/healthhats

Summary

According to Fabienne Bourgeois, MD, patients want control over their health data, but privacy preferences and constant changes complicate this. The discussion is relevant to people with disabilities, caregivers, and others navigating complex health information. About 80% of people share common privacy concerns that current systems can’t address. The remaining 20% need more detailed controls and customization, though balancing autonomy with privacy remains challenging. Ownership means individuals have the right to participate in research and make informed choices. They need “digital intermediaries”— professionals who assist with data sharing—and genuinely intuitive interfaces. Privacy protections must remain a top priority as health and AI tools continue to develop.

#HealthcarePrivacy #PatientTrust #AdolescentHealth #RespectConfidentiality #PersonalHealthData

Full 45 min podcast episode and show notes https://health-hats.com/pod240

Everything Health Hats https://linktr.ee/healthhats

We’re asking the wrong questions about AI in healthcare. Instead of debating whether it’s good or bad, we need to examine the system-eating-its-tail contradictions we’ve created: locking away vital data so AI learns from everything except what matters most, demanding transparency from inherently secretive companies, and fearing tools could make us lazy instead of more capable. Privacy teams protect data, tech companies build tools, regulators write rules—everyone’s doing their part, but no one steps back to see the whole dysfunctional picture. AI in healthcare isn’t a technology problem; it’s a mirror reflecting how our health system already falls short with privacy rules that hinder progress, design processes that exclude patients, and institutions that fear transparency more than mediocrity. The real question is whether we’re brave enough to fix these underlying problems that AI makes impossible to ignore.

Podcast and shownotes https://health-hats.com/pod239

Video A: I Used AI to Make Sense of AI (The Irony Isn’t Lost on Me)

Terrible conference recordings, crowd noise, no clear story. So I used six AI tools to clean, transcribe, find themes, and edit. The meta lesson? AI solves practical problems better than philosophical ones.

Video B: Circular Dependence, Chasing Your Tail

AI democratizes expertise—until you realize you need expertise to verify it. The solution requires the problem it’s meant to solve, revealing AI’s limitations, not its strengths.

Video C: AI Isn’t the Problem. Our Healthcare System Is

We lock away the data AI needs to learn. We demand openness from closed systems. Everyone does their part, but no one sees we’re undermining ourselves.

Proem

Here’s the thing about AI in healthcare—it’s like that friend who offers to help you move, then shows up with a sports car. The Iron Woman meant well, but it doesn’t quite meet your actual needs. I spent September 5^th at Academy Health’s 2025 Datapalooza conference about AI in healthcare, ‘Innovation Unfiltered: Evidence, Value, and the Real-World Journey of Transforming Health Care. a is Academy Health’s strongest conference for people with lived experience. I’m grateful to Academy Health for providing me with a press pass, which enabled me to attend the conference.

I talked to attendees about how they use AI in their work and what keeps them up at night about AI. I recorded some of those conversations and the panels I attended. When I listened to the raw footage, I heard terrible recordings filled with crowd noise and loud table chatter, like dirty water spraying out of a firehose. Aghast, I thought, what is the story here? I was stumped. How can I make sense of this? I had to deliver something.

So, here’s how I use AI in my work as a podcaster/vlogger. I used the Auphonic app to clean up the audio and remove noise, and then the Descript app to create transcripts of all the recordings. I went into my Claude podcast Project (a Project is an ongoing thread with everything I’ve done with Claude for my podcast over the past three months). I attached the transcripts and prompted the AI platform to identify themes. OK, that was helpful, but dull. So, I prompted Claude to think like a tech-savvy teen with a sense of humor. Eureka! Now we’re getting somewhere. I edited heavily and then prompted Claude to identify clips of speakers that illustrated the themes. I used the Perplexity app for research. Finally, I did the last written edit with a polish from the Grammarly app.

For audio, I returned to the Descript app, found the recommended clips, and extracted them. Then I recorded a video of myself, again using Descript. Compilation editing of the video was done with the DaVinci app. I should give production credit to Auphonic, Claude, Descript, Grammarly, Perplexity, and DaVinci.

Paradox, Irony, Catch 22

Datapalooza 2025 showcased the health and care industry’s intense focus on Artificial Intelligence, whatever that means. My podcast acts as a Rosetta Stone to share the excitement of what I learn and deem important in my journey toward best health. How can we use AI safely? Let’s jump in with some lessons I learned.

Burying the Treasure to Keep It Safe

There’s a Data Privacy Paradox. The very health data that could benefit most from AI faces the most restrictions. Sushmita Macheri works with Medicare/Medicaid data—information about some of our most vulnerable populations—but can’t use AI to identify errors that could improve their care. Meanwhile, commercial entities are freely training AI on whatever data they can scrape. Therefore, the most sensitive and valuable healthcare data remains locked away while AI trains on potentially biased and unrepresentative information.

Sushmita Macheri:  I work with healthcare, Medicare, and Medicaid data. I would like to upload the data so I can understand what errors I’m getting, but I’m unable to do that due to the restrictions we have at work. So, if I were able to upload one, let’s say, like a file that I am having errors with.

Health Hats: So, what kind of errors, like missing data, what are the errors that you notice?

Sushmita Macheri: I work with Tableau, mostly. Sometimes, if I’m having issues with a calculated field, I would like to upload that calculated field or the logic behind it in the calculator to try to understand what the error is, but I’m unable to do so. For me, it’s the biggest challenge.

Bias, Treating the Chart, Not the Patient

Bob Stevens points out a harsh irony: AI makes decisions about patients while being trained on data that intentionally excludes patient perspectives. The people most affected by AI decisions had the least input in training the systems. It’s like having a medical advisory board that leaves out doctors and patients, then questioning why the recommendations fail.

Bob Stevens: I am concerned about bias, as I mentioned, and that really worries me for two reasons. First, AI uses all available content, and as patients, we know that patient perspective content has not been well represented. Now, as AI starts making decisions based on this, all the content it has is just what’s available. It’s gathering it all. We haven’t been well represented in that process. So, it’s going to stay biased, right? Without patient information and the patient perspective, that creates a bias.

Bob Stevens: The second type of bias is related to how it’s designed. It’s not being general because it’s a technology, while they’re asking for patient input. There’s also bias in the design process because of who is doing the designing. So, you have two levels. One can be considered intentional, but the other is the accumulation of all this data that is there. We’re not represented in and haven’t been represented in. And how do we change that? The incremental change in the AI dataset is expected to take decades. What bothers me is that we are now relying on AI to assign a label that can then trigger a response or action.

Bob Stevens: That’s a high-risk moment, asking AI to make a decision that’s inherently high-risk. So what AI should always do is say. Here’s what I see. Now consider this when going in. And that brings us to the second part of a PCORnet study that I was involved in, which focused on the ER. And we had our electronic health record, and depending on how certain things, it was called a natural language processing process. And it looked at all these different things, and then based on that, it said, look to this, or looked to that, or looked to the other. It was those AI prompts that were based on the information from the electronic health record, which was then entered into the electronic health record. For that physician in the ER, they would then need to do certain things.

Circular Dependence, Chasing Your Tail

Rolanda Clark hits on something profound: we need expertise to verify AI, yet AI is supposed to democratize expertise. She notes you “still have to educate yourself on how to check the information,” but if you already have that expertise, why do you need AI? And if you don’t have the expertise, how can you verify it? It’s a circular dependency that reveals AI’s limitations rather than its strengths.

Rolanda Clark: So, I’d say with AI, it’s not foolproof. You still have to educate yourself on how to verify the information that’s being presented, and that’s hard to do.

Health Hats: I’ve started saying, ‘What is wrong with your algorithm?’ Correct. And I get some kind stuff I didn’t think about that makes me wanna burrow in more.

Rolanda Clark: But I think that’s imperative. I think you must counter to mitigate this like bullshit.

Health Hats: Because you need to do that with experts anyway, because just because they’re experts in this little thing, they think they’re experts in way more.

Rolanda Clark: Exactly. As a patient advocate, I ask myself, ‘Why am I here?’ And then I realize I have common sense, and I can see when it’s bullshit. I definitely have value regardless. And you’ve had experiences that these organizations often don’t promote or share because they want to highlight all of the good.

Health Hats: So, transparency? That’s an honorable challenge. How do you be transparent, and how do you trust yourself? Push the boundaries of what you’re willing to be transparent about. For me to cross a line, I find it helpful to know who’s behind it, their motivation, how they make money, and what they’ve decided to keep protected. It’s the company’s value that you don’t want to share because it’s the secret sauce. You don’t want to share the recipe. Kentucky Fried Chicken, sure.

It Doesn’t Have to Make Sense.

There’s a systemic irony where the most regulated industries that could benefit most from AI innovation are the least able to experiment with it. Healthcare organizations can’t risk HIPAA violations when exploring AI capabilities, so they often fall behind less-regulated sectors. Meanwhile, tech companies with no healthcare expertise are building health and care AI tools. I told my kids that life doesn’t have to make sense.

Grace Cordovana: In my advocacy work, I have the privilege of accompanying patients and their families to the point of care. I’ve been observing this anecdotally, essentially running my own informal study. I notice that when consent is asked for, it creates a really positive experience.

People are excited; patients and families are excited, and the doctor is excited, creating a spark of energy because now we can connect and talk as people. But what I’m noticing is that patients and families are now using their own tools, and they say, ‘That’s great, doc. You hit record, and I’m going to hit record too because I have my own tool.’

All hell breaks loose. Fractured relationship. Wait. We can’t do that. I, that’s not HIPAA compliant. We don’t have, hold on. No, we really can’t do that. I’m sorry. I’m not comfortable. Now, if this is a new patient encounter, it’s a major problem. Think about a new patient encounter where this patient has cancer, and this is their first appointment for a second opinion on an advanced cancer. And that’s how we’re starting off.

Throwing Out the Baby with the Bathwater

Madhu Jalan’s concern about her son reveals another contradiction: we’re afraid AI will make us lazy, so we avoid using tools that could boost our productivity. But this avoidance might actually make us less competitive and adaptable. It’s like refusing to use spell check because you want to be a better speller—you end up writing less, not better.

Madhu Jalan: I have a 17-year-old, and I worry that he won’t learn the skills he needs to get along. Critical thinking, for example, involves writing. I worry that it will just make him lazy and completely redundant. That’s what I fear. He’s 17, so he still needs to learn how to learn and shouldn’t take the lazy way out. That’s what I worry about.

Clear as Mud

A delicious irony is that most people call for AI transparency, yet the most successful AI companies are among the muddiest. We want to understand how AI works, what data it uses, and how it makes decisions—but the companies we think have the best AI are the ones most protective of their “secret sauce.” The transparency advocates have the least power to enforce transparency.

Grace Cordovana: I encourage you if you haven’t read it or haven’t heard about it, take a look at the Light Collective AI Rights for Patients document, and it’s rooted in seven pillars. So, we boiled the ocean down to the crux of what was important for us from a patient’s perspective in that setting. Not just the foundation but looking at what the apex of ethics and the apex of good would look like for the people for whom all of these tools and technologies were being developed.

So, we answered the question, What do patients want, need, expect, and demand? And we landed on patient-led governance.

We committed ourselves to uphold patients’ transparency and self-determination. This includes identity, security, privacy, the right of action, and shared benefits. When you explore the document further, you’ll find all the specific details. I can assure you that each of us carefully reviewed every word and statement, and we all consented to the final version.

Patients can and will do good work, laying foundations that set a precedent for other stakeholders. This approach is divided. Designed to work in multi-stakeholder settings, public-private partnerships—which I will advocate for—should include public, private, and patient collaborations as we envision the future.

Redistricting to Democratize

There’s an ironic class dynamic where AI is supposed to democratize access to capabilities, but it actually requires significant skill to use effectively. It’s like learning a foreign language with characters that change shape with the weather. The people who most need AI help (like Yvonne McLean Florence learning scientific terms) are least able to verify its accuracy or notice its biases. Meanwhile, those most capable of using AI responsibly (like Madhu) are the most worried about its risks.

Yvonne McLean Florence: We attend specific conferences, and they put us in groups. We have to conduct research and learn new terms, so I use it for that. Help me understand the different scientific terminologies, as I don’t have a strong science background. Not that I have to be a professional when we’re at these conferences, but you do go there to learn.

Humanize Through the Looking Glass

Bob’s “Yogi the AI” example, which we heard earlier, reveals a graphic irony: we humanize AI to make it seem safer, while at the same time dehumanizing the process it’s meant to support. They give the AI a cute mascot name and treat it like a team member, but the underlying process reduces complex human problems to data patterns and algorithmic responses.

Bob Stevens: What they’ve done with the AI is that the AI person sits there as a member of the committee, and they actually name them and interact with them as if they’re sitting at the computer, as if they’re present. They usually do something silly. The name of the school is Bucknell Bears. So, if they’re the Bears, then they would end up naming AI, Yogi, and it becomes a real person sitting in that meeting, providing that input. But the key is, they’re just one person on the team? What is AI? Is one person on the team

Driving while looking into the Rearview Mirror

There’s an irony in the way we’re using 21st-century AI to perpetuate 20th-century biases. As Bob notes previously, changing the training data will “take decades,” meaning that today’s AI systems will continue to reflect historical inequities even as they are used to generate predictions to make forward-looking decisions about health, care, education, and social services.

A Million Interns Working for You

Finally, there’s Rolanda Clark’s observation about having “a million interns”—AI gives us unprecedented capability while making us more dependent. She can now act on creative ideas immediately, but what happens when the AI isn’t available? We gain productivity but lose resilience.

Rolanda Clark: I feel it’s so advantageous that I can come up with a thought, and that becomes the catalyst for so much more. Years ago, when I had these wonderful inklings, it was more imaginative because I usually didn’t have the time or energy to go full throttle. Now, you have a million interns digging for you.

What Keeps Me Up at Night About AI?

I should answer my own question. At night, my apocalyptic mind worries about the corporatization of AI, the huge energy and water use that AI farms require, and the potential collapse of our financial stability when the AI bubble bursts.

Reflection

We’re asking the wrong questions about AI in healthcare. Instead of debating whether it’s good or bad, safe or dangerous, we should examine the contradictions we’ve created: We lock away vital health data to protect people, so AI learns from everything except what matters most. We demand transparency from companies built on keeping secrets. We avoid tools that could make us more thoughtful because we fear becoming lazy.

People I talked to see some of these problems clearly. Sushmita can’t use AI to catch errors that could help vulnerable patients. Bob knows the bias is baked in from the start. Grace sees the HIPAA panic when patients want recording tools that their doctors already use. Rolanda understands that verifying AI requires expertise, undermining its promise of democratization.

So why aren’t we fixing this? Because each problem seems like someone else’s job. Privacy teams protect data. Tech companies build tools. Regulators write rules. Everyone’s doing their part, but no one is stepping back to realize, “Wait—the whole system is eating its own tail.”

AI in healthcare isn’t a technology problem. It’s a reflection of how our health system already falls short—privacy rules hinder progress, design processes exclude patients, and institutions fear transparency more than mediocrity.

The question isn’t whether we should use AI in healthcare. It’s whether we’re brave enough to fix the underlying problems that AI makes impossible to ignore.

See you at the PCORI 2025 Annual Meeting and the Camden Coalition’s Putting Care at the Center 2025 where I’ll keep asking uncomfortable questions.

I thought I’d start writing you letters. I miss letters. I send cards thanking my guests for their participation. People universally appreciate getting something via snail mail. But cards aren’t letters. They’re preformatted notes, where I just change the image and the name.

For giggles, I looked back at my very first blog post, July 31, 2012. It was a paragraph, a letter of sorts, short and simple, Improv and Best Health.

It's taking me longer to produce each episode. So, no more frequently than once a month. So, why not a letter, short and sweet, from time to time?

Let’s start with trust. A letter feels more trustworthy than an email or a tweet. It’s signed; a person who writes a letter really wants to communicate and thinks about what they’re saying. So, perhaps, not a troll, more trustworthy. I always open letters.

My immediate, momentary, default reaction to almost anything is trust. My kids say I’m gullible. My next instant reaction is skepticism. I think about what’s not true about whatever. ‘AI is the solution to everything.’ What do you mean, everything? What is AI anyway? Like that.

I’ve been married for 50 years because at our core, my wife and I trust each other. We disagree, we misunderstand, we anger, I sulk. Yet we trust. On the other hand, I make stuff up. I misremember, create a story, and if it serves my purposes, stick with it or modify it as needed. My wife and grandkids are my fact-checkers. Still, we trust each other.

In my personal life, trust isn’t an on-or-off switch, all or nothing. Well, not usually. It’s a matter of degree; it’s about something. I trust that I can count on you to be there for me, unless you can’t. I trust that you’ll return my call, unless you’re hurt, don’t feel like it, or missed it.

Distrust sucks energy; be more careful with my words, self-censor, close my heart and mind.

I don’t expect to trust everybody or everything. When I do trust, it’s priceless.

Thanks for listening, I’ll be back.

The conversation focuses on practical strategies for managing power dynamics and ensuring genuine community participation in health advocacy. Alexander highlights the crucial importance of listening to marginalized communities—those "furthest from opportunity"—and creating systems that give them real decision-making power, not just token representation. She promotes proactive governance planning, likening effective coalition building to chess strategy, where groups need to think "seven to eight moves down the board." Key tactics include allowing time for quiet reflection, facilitating small group discussions, and developing shared talking points for coalition members to take back to their organizations. Alexander's main message for health advocates is clear but powerful: start by listening to the communities you want to serve, trust what people tell you about their needs, and create systems that place those most affected by health inequities in leadership roles.

An innovative approach to livestock protection and disease prevention through strategic coyote management. Rather than shooting coyotes, this farm fosters a "neighborly relationship" through the use of electric fencing and habitat management. Coyotes are allowed to thrive in hedgerows with abundant prey like mice and squirrels, while electric fences teach clear boundaries around sheep and lambs. This ecosystem approach creates an unexpected benefit: coyotes naturally buffer the farm from wild waterfowl that carry bird flu, keeping geese and ducks away from farm ponds and livestock areas.

4 min YouTube video here:

Full 33 min podcast and show notes here: https://health-hats.com/pod236

Everything Health Hats here https://linktr.ee/healthhats

A multi-generational farming family shares their journey adapting to increasingly frequent bird flu outbreaks. What started as simple "farm boot" protocols in 2015 has evolved into comprehensive biosecurity measures as avian flu cycles have shortened dramatically. Shannon credits their state veterinarian partnership through the New York State Grown and Certified program for keeping them ahead of threats. The family made strategic changes, such as relocating customer interactions off the farm and implementing strict equipment hygiene protocols. This personal account highlights how traditional farming wisdom ("don't leave your shit on anyone else's farm") combines with professional veterinary guidance to create effective modern biosecurity practices.

Four min YouTube video here:

Full 33 min podcast and show notes here: https://health-hats.com/pod236

Everything Health Hats here https://linktr.ee/healthhats