AI Freak Out? Let's Reframe the Conversation
November 11, 2025
I get this a lot.
When I talk about the future of healthcare, people are energized. But when I pivot to AI, the mood shifts. People are "freaking out."
I was speaking on this topic the other day, and a respected physician came up to me and said, “I am so glad I’m retired!”
He’s not wrong to feel that way. The noise is deafening. We’re all being hit from two sides, and it's enough to make anyone feel paralyzed.
The Two Extremes (And Why They're Both Wrong)
On one side, there's the AI Hype.
This is the utopian promise. You’ve seen the vendors. You’ve read the headlines. AI will read every scan instantly, end diagnostic errors, write all our notes, eliminate clinician burnout, and solve our staffing crisis by next quarter. It’s the magic wand we've been waiting for.
On the other side, there's the AI Hysteria.
This is the dystopian warning. AI is a black box trained on biased data. It will amplify systemic inequities. It will replace our best clinicians. Hackers will cripple our systems. And insurance companies are already using it as a weapon to deny care at a scale we’ve never seen before.
No wonder that doctor is glad he’s retired. No wonder we feel paralyzed.
The Sober Reality: AI is a Mirror
Here is the reality.
AI is not magic. It's math. It is a powerful tool. And a tool is only as good as the system we put it into.
Here is the single most important thing I can tell you today: AI does not fix a broken system. It just scales the broken parts faster.
But here’s the part we're missing: AI is a mirror.
It's not inventing bias; it's just exposing the bias that's been in our data for decades.
It's not creating interoperability problems; it's just shining a harsh light on our absurd reliance on the fax machine.
It's not creating new financial barriers; it's just automating the ones that already exist.
This isn't a catastrophe. It's a diagnostic. AI is showing us, with data, exactly where the cracks in our system are. And that is not a reason to be paralyzed. That is a reason to be focused.
A Practical Path Forward
We are leaders, clinicians, technologists, and more. Here is how we move from paralysis to action.
- Start with Problems, Not Platforms. We must have the discipline to reject "shiny object syndrome." The conversation needs to change. Instead of a random sales guy asking, "Do you want to buy an AI platform?" we need to be clear: "Show me how you will reduce my nurse's documentation time by 30%." "Show me how you will get my sepsis patients their antibiotics 15 minutes sooner." We start with the problem, not the tech.
- Govern What You've Got. We must be the ones to audit the data, asking before we buy a tool, "Who is not in your training set?" We must also consider when to keep a human in the loop, empowered to overrule the algorithm, or when it's not necessary.
- Invest in the "Boring" Stuff. AI doesn't work in a vacuum. It needs the boring infrastructure: the broadband in our rural counties, the interoperability between our EHRs. It needs payment models that reward using AI for prevention, not just for billing. And it needs us to design for trust; which means bringing patients, community leaders, and our frontline care teams into the room before we buy the tool.
- Teach How to Use It. We are responsible for creating the next generation of healthcare professionals: the nurses, the PAs, the therapists, the coders, and the physicians. We have a mandate to make AI literacy a core competency. This is not as simple as handing someone a new app. A striking JAMA study highlighted this very gap. It found that AI, used alone, actually outperformed both physicians working alone and physicians who were given the AI tool to help.
What does that tell us? It tells us that this is a complex, learned skill. Simply giving a clinician a powerful AI doesn't guarantee a better outcome. We have to train our teams how to use it, when to trust it, and when to overrule it. They can't just learn to use AI; they must learn to effectively partner with it.
The Bottom Line
The goal of AI is not to be intelligent. The goal is to be useful. The goal is to be safe. The goal is to restore the human connection that technology so often breaks.
The future of healthcare is not about replacing our clinicians with algorithms. It's about augmenting our care teams.
It's about giving them the tools and the time to do what only humans can do: listen, show empathy, and heal.
The future isn’t about intelligence without borders. It’s about building a system that delivers humanity, to everyone, without barriers.
Stay grounded. #StayCrispy
-Dr. Matt
For decades, medicine has operated on a foundation of averages. We rely on clinical trials that tell us how a drug affects the "average" person, and we follow treatment protocols designed for a broad population. But as any clinician knows, there is no such thing as an "average" patient. Each person is a unique combination of genetics, environment, and lifestyle. What if we could change that? What if we could test a new heart valve on your specific heart before surgery? Or simulate five different cancer treatments on your specific tumor to see which one works best, all without you ever taking a single dose? This is the promise of the digital twin : a dynamic, living, and personalized virtual model of a patient. If It's Not a New Idea, Why Talk About It Now? The concept of a "digital twin" is not new. It has been used for decades in advanced manufacturing and aerospace to model complex machines like jet engines. So why is it suddenly one of the most talked-about topics in health tech? The answer is convergence. For the first time, three powerful forces are maturing at the same time: Massive Data: We now have oceans of data from EHRs, rich genomic sequencing, and medical imaging. Constant Data: The explosion of wearables and remote patient monitoring devices provides a continuous, real-time stream of data about an individual's physiology. Powerful AI: We finally have the advanced artificial intelligence and computational power to make sense of all this data, building and running simulations that were impossible just a few years ago. This convergence is moving digital twins from a futuristic concept, and evolving into a practical clinical tool. The Volcano in Your Computer When I explain this concept, I often use an analogy that seems to resonate. Think about scientists trying to understand a volcano. They cannot safely trigger a real eruption just to study it. That would be impossible and catastrophic. Instead, they build a highly complex computer model of that specific volcano. They feed it real data: magma pressure, ground tremors, gas emissions, and geological structures. This model allows them to run simulations. They can ask "what if" questions. What if the pressure increases by 10%? What if a fissure opens on the north flank? This simulation allows them to test scenarios and predict a real eruption, all without any real-world risk. Now, apply this exact logic to the human body, which is infinitely more complex than a volcano. We cannot ethically or safely test ten different interventions on a live patient. But we can test them on their digital twin. Where Virtual Patients Are Already Making a Real-World Impact This is not just theory. Digital twins are actively being used to improve outcomes. In Cardiology: The Dassault Systèmes "Living Heart" project creates highly accurate, personalized heart models. This allows cardiologists to test how a specific patient's heart will react to a new device, like a stent or valve, before it is ever implanted. Similarly, FEops HEARTguide helps clinical teams predict how a transcatheter aortic valve implantation (TAVI) device will interact with a patient's unique anatomy, helping them choose the right size and position to avoid complications. In Hospital Operations: Beyond individual patients, Karolinska University Hospital in Sweden has utilized digital twins to optimize its surgical workflows. By simulating the flow of patients, staff, and resources, they can identify bottlenecks, improve scheduling, and ensure operating rooms are used more efficiently. The Hurdles on the Horizon As with any revolutionary technology, the path forward has significant challenges. Data Integration: Building an accurate twin requires pulling vast amounts of different data from siloed systems. Computational Cost: Running these complex simulations requires enormous processing power. Validation and Ethics: How do we "validate" a digital twin? How do we know it is accurate enough to base life-or-death decisions on? And who owns your virtual data? These are critical questions we must answer. The digital twin represents the ultimate destination for personalized medicine. It is not a tool to replace the clinician, but a powerful new instrument to inform their judgment. The goal is no longer just to treat the average patient, but to provide precise, predictive, and personal care for the individual patient. And it all starts with building the virtual you. #StayCrispy -Dr. Matt
For the last decade, we’ve talked about clinician burnout as a problem. Let's be blunt: it’s no longer a problem. It’s an existential crisis. It’s the "pajama time" spent logging hours in the EHR after the kids are in bed. It's the "death by a thousand clicks" that has turned highly-trained physicians and nurses into the world's most expensive data-entry clerks. And it’s the moral injury of knowing you could provide better care if you weren't constantly battling your own inbox. For years, tech has felt more like an antagonist in this story than a solution. But the narrative is changing. Generative AI is finally here, and it’s making two very different, very powerful promises. The question is: are we listening to both? Part 1: The AI Scribe - A Fix for the Process The most visible, headline-grabbing solution to burnout is the Ambient Clinical Scribe . This is the "shiny object" that's actually working. The news is now dominated by massive, enterprise-wide rollouts. Kaiser Permanente recently announced a historic deployment of Abridge to 10,000 of its clinicians. This comes on the heels of dozens of other health systems adopting Microsoft’s DAX Copilot (formerly Nuance), Oracle/Cerner , Abridge , and similar tools integrated directly into Epic and Cerner. The promise is intoxicatingly simple: The doctor and patient just talk. The AI listens in the background. By the time the patient has left the room, a structured, accurate, and billable clinical note is 80-90% complete in the EHR. This is not a small thing. It’s a direct assault on the 2+ hours per day that physicians spend on documentation. This technology gives clinicians back the single most valuable asset they have: time . It’s a powerful painkiller for the most acute symptom of burnout. But what happens when you’ve taken the painkiller? The immediate, throbbing pain of documentation is gone. But the underlying disease remains. What if you get two hours of your day back, only to spend it in a unit where you feel isolated, unvalued, and completely disconnected from leadership and your colleagues? Part 2: The Deeper Disease - A Crisis of Culture This brings us to the other side of the burnout coin. This crisis was never just about documentation. The clicks were the symptom. The disease is a fundamental breakdown in culture, connection, and belonging. Burnout is what happens when a nurse doesn't feel safe speaking up. It’s what happens when a physician feels a total lack of autonomy and a deep misalignment between their values and the hospital's business objectives. It’s the isolation of a 12-hour shift where you feel like a cog in a machine, not a human in a community. For decades, how have we tried to "fix" this? With a clumsy, 60-question annual employee engagement survey. This is a tool from a different era. By the time the data is collected, analyzed (six weeks later), and presented to managers, it’s a historical document. It’s a rear-view mirror. It tells you how your team felt last quarter, not how they feel right now. And worse, it provides managers with a mountain of data but no clear path to action, so it often gathers dust. Part 3: The AI "Pulse" - A Fix for the Culture This critical gap has created a new category of tools: real-time employee listening or "pulse" platforms. For years, major platforms like Glint (now part of Microsoft), Culture Amp , and Perceptyx have tried to solve this, arguing that continuous feedback is far better than an annual snapshot. They provide powerful analytics to HR leaders, helping them understand the macro trends driving attrition and engagement. But a different, more lightweight approach is also emerging, one focused less on periodic surveys and more on creating a daily habit of connection. Full disclosure, it’s a space I’ve recently started advising in, after being introduced to a platform called Sayhii . Their model is designed to act as a high-frequency pulse. It’s built on a deceptively simple premise: one simple, science-backed question sent to every employee, every day. It’s a 10-second interaction, not a 30-minute survey. "Do you feel your work has purpose?" "Do you trust the leadership of this organization?" "Did you feel you belonged at work this week?" Instead of a rear-view mirror, this approach creates a real-time, anonymous "check engine" light for frontline managers. A nurse manager can see an anonymous, real-time dashboard indicating that their team’s "sense of purpose" score has dipped 15% this week, and then be prompted with a micro-action to address it, like starting the next huddle by sharing a recent patient-win story. The Full Prescription: Clicks and Culture A health system that gives its doctors two hours back with an AI scribe (but leaves them in a culture where they feel unheard and unvalued) hasn’t solved burnout. It’s just created more efficient, slightly-less-tired, still-burnt-out employees. The AI scribe is the painkiller . It's essential for immediate, acute relief. We absolutely need it. But these continuous listening tools, like the daily pulse of a @Sayhii, are the antibiotic . They are the long-term therapy designed to fix the underlying cultural infection that made the system sick in the first place. The smartest health systems in 2026 and beyond will be the ones that realize they must do both. They will use one set of AI tools to fix the process and another set to fix the culture. Because you can't heal a workforce by just treating the symptoms. Until next time, #Stay Crispy Dr. Matt
Take a moment and look at your wrist. There's a very good chance you are wearing a device that is, at this exact second, counting your heartbeats, tracking your movement, and maybe even measuring the oxygen saturation of your blood. Millions of us have invited these tiny, powerful computers; our smartwatches, Fitbit (now part of Google) , ŌURA rings, and WHOOP bands, into the most intimate spaces of our lives. We’ve embraced the dashboards, the sleep scores, and the gamified satisfaction of "closing our rings." This technology is empowering. It makes the invisible visible and nudges us toward better habits. The clinical potential is undeniably revolutionary. An Apple Watch can detect an irregular heart rhythm (Atrial Fibrillation) and alert a user to a life-threatening condition (not new news, but a good reminder!). A continuous glucose monitor (CGM), once a niche device for Type 1 diabetes, can now give anyone real-time feedback on how a single piece of toast sends their blood sugar soaring, transforming our approach to metabolic health. This is the promise of the health tech revolution: personalized, preventative, and predictive medicine in real-time. But this flood of data, streaming from our bodies 24/7, flows in two directions. It flows to us, empowering our daily decisions. And it flows away from us, into a complex and opaque ecosystem of corporate servers, third-party apps, and data brokers. This brings us to the critical, and often uncomfortable, questions we must start asking. My colleague, Meredith Challender framed these questions perfectly in a recent post about our upcoming panel. They get to the very heart of this new digital-health equation. From Wellness Data to a Digital Profile Meredith asks: Ever wonder what information your device is gathering? Do they know more about your health than you know about it yourself? The answer is almost certainly, yes. We see the surface level: steps, sleep duration, and calories. But the real value is in the data beneath the data. These devices are gathering: Biometric Signatures: Your resting heart rate, and more importantly, your Heart Rate Variability (HRV) , a powerful proxy for your body's stress, recovery, and autonomic nervous system function. Physiological Patterns: Detailed sleep staging (REM, Deep, Light), respiratory rate, skin temperature, and blood oxygen (SpO2). Metabolic Response: With CGMs, this is a minute-by-minute log of your body's reaction to every single thing you eat, drink, or do. Algorithms synthesize this. They find patterns you can't. A wearable may detect signs of an impending illness like COVID-19 or the flu days before you ever feel a symptom by noticing a subtle rise in your resting heart rate or skin temperature. The promise is an early warning. The risk? This data lacks context. An algorithm doesn't know you had a stressful deadline and two cups of coffee; it just sees a "high stress" score. This can create a new, digital-age anxiety without the guidance of a clinician to interpret it. As a mom of four, an exec, and and and....I live on coffee and cortisol! The Great HIPAA Black Hole This leads to the next, and perhaps most critical, set of questions: Who are they sharing this with? Health (and maybe even life?) insurers? Are they appropriately securing that information? This is the multi-trillion-dollar problem. When I, as your physician, take your blood pressure, that reading is P rotected Health Information (PHI). It is shielded by the federal law HIPAA (the Health Insurance Portability and Accountability Act). It cannot be shared without your explicit consent. But when your smartwatch or health app takes that same reading, it is most likely not protected by HIPAA. It's considered "consumer data," governed by a company's privacy policy (that 40-page document you scroll past and click "Agree" on). This creates a "HIPAA black hole" where your most sensitive personal data can flow. The Federal Trade Commission (FTC) has been issuing stark warnings to health app makers about this very issue. So, where does your data go? Data Brokers: It's often "anonymized" (a very fuzzy term) and sold or shared with data brokers, marketers, and research firms. Your Employer: Many corporate wellness programs offer insurance discounts if you "voluntarily" share your activity data, creating a direct pipeline to your employer or their partners. Insurers: This is the big one. In the health insurance world, the (ACA) and GINA (Genetic Information Nondiscrimination Act) prevent insurers from using this data to set your premiums (we still deal with preexisting conditions!). But what about life insurance ? Or disability and long-term care insurance? The rules are far grayer. It's not hard to imagine a future where a life insurer requests your last five years of "wellness data" to set your rates. And as for security? Health data is one of the most valuable assets on the dark web. The IBM Cost of a Data Breach Report consistently finds that healthcare data breaches are the most expensive, precisely because the data is so personal and permanent. We are trusting tech companies to secure our data with the same diligence as a hospital or a bank, and the track record across the industry is... mixed. What This Means: From Data to Trust These aren't hypothetical fears; they are the most urgent, practical, and high-stakes challenges at the intersection of technology, insurance, and medicine. And that is exactly what I'll be discussing at the Inaugural Emerging Technologies Insurance ExecuSummit in a few weeks. I'm honored to be part of a panel assembled by Meredith Challender to tackle these issues head-on. I'll be joining a brilliant group of experts, including @ David Standish , Afik Gal, MD,MBA and Kevin Mekler to debate the true benefits and risks of these devices. We'll be moving past the marketing hype and digging into the insurance, liability, and clinical realities of this data-driven world. The genie is not going back into the bottle. We will not stop using these devices; they are too good, and their potential for improving health is too great. But the next frontier isn't just a better sensor. It's building a system of data governance and trust. We must move from a model of passive data collection to one of active patient consent, true data ownership, and transparent, secure sharing. If you're attending the ExecuSummit, I look forward to seeing you there. Stay healthy (and data-aware), Dr. Matt