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

Good morning from San Jose. Being here this week has been incredibly energizing. Already conversations with tech companies and founders have been sharp, focused, and centered on a single, unifying theme. It’s a topic I had the pleasure of diving into on the DeviceTalks podcast, discussing what it takes to build a revolutionary company in today's medtech landscape . That theme is the undeniable reality that the goalposts for success have moved. The era of scaling a startup on a great device and a passionate physician champion is over. Today, a new gatekeeper governs everything from venture funding to hospital purchasing: hard, quantifiable economic proof. The shift began in the VC boardroom. The macro-economic headwinds and the end of a decade of near-zero interest rates have swept away the "growth-at-all-costs" mentality. Capital is no longer cheap, and investors' tolerance for risk has recalibrated. The fundamental question has changed from "How big can this get?" to "How efficiently can this get to a profitable, commercial state?" For medtech startups, this means the most critical milestone is no longer just a prototype; it’s demonstrating a de-risked path to market adoption. As the H1 2024 analysis from Rock Health so aptly put it, "resilience leads to brilliance." In this climate, "resilience" isn't just a buzzword; it means running leaner teams, managing for a longer runway, and hitting milestones to justify valuation that were previously expected a full funding stage later. The startups thriving today are the resilient ones, those who build capital-efficient models and prove their value early. This investor mandate is a direct reflection of a seismic shift within their ultimate customers: the hospitals. The modern hospital is an organization under immense financial pressure. Grappling with persistently slim operating margins , crippling staffing shortages, and the inexorable transition to value-based care, their purchasing process has been radically centralized. The decision to buy a new medical device is no longer made by an enthusiastic department head. It is often made by a Value Analysis Committee (VAC): a cross-functional team of clinicians, supply chain managers, and finance analysts whose sole mandate is to scrutinize the total impact of every new product or initiative. Imagine your pitch being heard by a clinical nurse specialist focused on workflow integration, a finance analyst focused purely on ROI, and a supply chain manager concerned with inventory and maintenance costs. You have to satisfy them all. This means the physician champion, while still essential for validating clinical need, is no longer sufficient. They can get you in the door, but they cannot get you the budget. The VAC and the C-suite are asking a different, more demanding set of questions directly tied to financial models like bundled payments or ACOs: How will this device reduce length of stay? How does it improve operating room throughput? What is its direct impact on our 30-day readmission rates and our ability to avoid financial penalties from payers? This reality has forced a merger between the fundraising pitch and the sales pitch. The successful medtech startup of today is the one that builds its entire strategy around Health Economics and Outcomes Research (HEOR) from day one. This isn't just about creating a slick ROI calculator for your website; it's about embedding health economics into your product development and clinical strategy. It means partnering with an early-adopter health system to conduct a pilot study, meticulously gathering real-world evidence (RWE), and co-authoring a whitepaper or even a peer-reviewed study that validates your economic claims. This is the new playbook in action: You don't just claim your surgical robot is "more precise." You prove it reduces procedural time by 12 minutes, saving the hospital $1,500 per procedure. You don't just say your diagnostic is "more accurate." You build an economic model showing it avoids millions in unnecessary downstream procedures. You don't just say your monitoring device "improves outcomes." You prove it lowers readmission rates for CHF patients, saving the hospital from significant CMS penalties. This is the hard data that satisfies both the VC's demand for a de-risked investment and the hospital CFO's demand for a clear return. The conversation with an investor and the conversation with a hospital buyer have now converged on a single, powerful topic: measurable economic value. For those of us here this week, building the future of medical technology, the challenge has evolved. It is no longer enough to be a brilliant engineer or a visionary clinician. To succeed in this new environment, we must also become rigorous economists. So here is my call to action for every founder, innovator, and investor in this room: Go back to your office and kill one feature on your product roadmap. In its place, add a single, critical line item: "Develop Our Economic Value Story." Don't wait until you're pitching for your Series B or trying to close your first major hospital contract. Start now. Because in the medtech landscape of today and tomorrow, the companies that win will not be the ones with the most features; they will be the ones with the most undeniable proof of economic impact. -Dr. Matt 

Here at the MERGE Built '25 conference, "personalization" is the word on everyone's lips. We are celebrating a future of healthcare tailored to the individual, powered by data and AI. But as I prepare to take the stage later today for our panel, The New Health Consumer: Beyond the Algorithm, I'm focused on a critical flaw in our industry's strategy. Getting personalization wrong isn't just a missed opportunity; it's a waste of billions in R&D and a failure to engage the very people we claim to serve. The central question we must ask is: are we building for everyone, or just the people who build the tech? The paradox is this: in our rush to personalize, we have defaulted to designing for a single user archetype, the digital native. It's the path of least resistance for product teams, who are often composed of a specific demographic. The result is a platform that feels intuitive for a 28-year-old but can feel alienating for their 68-year-old parent. It's like a world-class chef who, despite their talent, seasons every single dish with the same spice simply because it's their personal favorite. The quality is high, but it isn't truly personalized. This is a new and more insidious form of one-size-fits-all. The On-Demand Generation: What Gen Z and Millennials Expect For generations who grew up with a smartphone as a constant companion, life is on-demand. They view health not as a series of isolated clinical events, but as a core component of a holistic wellness lifestyle that integrates mental, physical, and even financial wellbeing....it's a journey. Engagement for them is the entire experience. They don't just want data; they want context, community, and coaching delivered through the same seamless interfaces they get from their favorite consumer apps. This is why data shows they are the most likely to switch providers for a better digital offering . Building for them requires a mobile-first approach that feels authentic and integrated. But we must also consider their unique life stage. Many are navigating financial precarity, from student debt to the gig economy, which directly impacts their healthcare decisions and drives a search for value and cost transparency. True personalization for them might mean a platform that pairs mental health check-ins with their workout data, offers budget-friendly healthy meal plans, and clearly explains the cost of a recommended lab test. According to recent consumer insights, these digitally savvy generations value authenticity above all else and have a low tolerance for corporate jargon. Trust is earned with every interaction. The Trust-First Generation: How Gen X and Baby Boomers Engage The dynamic shifts for Gen X and Baby Boomers. As "digital immigrants," their adoption of technology is often pragmatic and purpose-driven. In healthcare, their primary trust anchor has almost always been the human relationship with a clinician, built over years of face-to-face interaction. Technology's role is to enhance that trusted relationship, not replace it. Concerns over data privacy and security are a significant barrier to adoption for this group , making it crucial to build tools that feel safe and reliable. Personalization for these generations means clarity and confidence. They are not looking for streaks and leaderboards; they are looking for easy access to test results, simplified medication management, and clear communication with their care team. Many are managing chronic conditions, often on a fixed retirement income, so tools that help them navigate complex insurance benefits are invaluable. A successful feature is one that clearly explains their out-of-pocket spending and allows them to easily share their glucose readings directly with their endocrinologist's office. Their confidence in digital health tools is directly linked to the confidence they have in their provider , making the clinician's endorsement critical. The Blueprint for a Generational Bridge The path forward is not to pick a winning generation, but to build a generational bridge using "Adaptive Design." This is about more than just choice; it is about creating systems that intelligently default to a user's likely preference while still allowing for deep customization. The blueprint for this includes: Adaptive Onboarding: The very first interaction should ask users how they prefer to be contacted and how much detail they want to see, setting the right tone from day one. Flexible Interfaces: A platform should offer both a "Simple Mode" with only core information and an "Advanced Mode" for deep data dives, allowing users to choose the complexity they are comfortable with. Multi-Channel Support: Engagement cannot be one-dimensional. A robust system needs a spectrum of support, from an AI-powered chatbot for quick questions to a clearly displayed phone number for users who want to speak to a human. This is just a quick glimpse of the themes of the panel discussion today at MERGE Built '25. The companies that win won't just have the best algorithm; they will have the deepest empathy. A generational lens is the missing ingredient for unlocking the true promise of personalization. For the latest and greatest the MERGE full report, with comprehensive data, drops later today. Be the first to see it by watching my social channels this afternoon for the announcement. -Dr. Matt

For years, the conversation around remote patient monitoring has been anchored in familiar territory: tracking vitals post-discharge or, more recently, using wearables to detect acute infections. While valuable, this narrative barely scratches the surface of what’s now possible. The true revolution is not just identifying a binary event like an infection; it’s about decoding the subtle, chaotic signals of chronic disease, a challenge that represents one of the largest drivers of cost and suffering in our healthcare system today. Consider the landscape: more than 24 million Americans live with an autoimmune disease, and the collective cost of managing these 'unpredictable', inflammatory conditions runs well into the hundreds of billions annually. For this population, the greatest challenge is the unpredictable nature of a flare-up. These events, which can strike with little warning, are the primary driver of emergency room visits, hospitalizations, and the use of high-cost biologic drugs. While those with access to specialty care are on protocols ato reduce flares, our system remains very reactive, treating the crisis more often than preventing it. That is beginning to change. The next wave of healthtech is moving beyond simple anomaly detection and toward the creation of Physiological Digital Twins : dynamic, machine-learning-powered models of an individual's unique biological baseline. This is not just a marketing buzzword. It represents a fundamental shift from the threshold-based alerts of traditional RPM (for example, a blood pressure reading over 140/90) to a sophisticated, pattern-based intelligence. Instead of just looking for a single metric to cross a line, a digital twin synthesizes multi-modal data streams from consumer wearables. It analyzes heart rate variability (HRV), sleep architecture and fragmentation, activity patterns, and respiratory rate to understand the intricate interplay of a person's autonomic nervous system. By learning an individual's unique "rhythm of wellness," the model can detect the faint, complex patterns that signal a brewing inflammatory cascade, long before the patient feels the debilitating symptoms. The clinical evidence for this advanced approach is now emerging. A groundbreaking study on Inflammatory Bowel Disease (IBD) demonstrated that passive data collection from a consumer smartwatch could. Researchers found that a drop in HRV was a persistent signal in the week leading up to a confirmed flare. This is a crucial insight: while an infection causes a sudden, noisy alarm in your vitals, an impending autoimmune flare presents as a quieter, more sustained deviation that only a sophisticated, continuously learning model can reliably detect. The Data-to-Action Pipeline: An Operational Framework Bringing this concept to life requires an operational framework that bridges raw data with clinical action. For leaders and builders, the "Data-to-Action Pipeline" provides a roadmap for implementation. Data Ingestion & Harmonization: The first challenge is the messy reality of the consumer device market. A platform must be able to ingest data from a variety of sources, like an Apple Watch, Oura Ring, or Garmin device, and harmonize it into a standardized format. This is the foundational layer for any scalable solution. The Digital Twin Engine: This is the core intellectual property. Once data is harmonized, the AI engine establishes a highly personalized, multi-variant baseline for each user. It then runs continuously, using pattern recognition algorithms to identify deviations from that baseline that correlate with a specific adverse event, like an IBD flare. The Clinical Intelligence Layer: A statistical probability from the AI engine is not a clinical action. This crucial layer translates the model's output ("78% probability of flare within 72 hours") into a specific, clinically relevant recommendation ("Patient may be entering an inflammatory cycle. Suggest initiating prescribed anti-inflammatory protocol and schedule a telehealth check-in."). The Intervention & Feedback Loop: The final step is delivering the intervention, whether it's an automated notification to the patient, a task sent to a care manager's dashboard, or an alert within the EHR. The patient’s outcome and subsequent data are then fed back into the Digital Twin Engine, creating a closed loop that allows the model to become progressively smarter and more personalized over time. A New Strategic Imperative This capability creates a new strategic imperative for leaders and builders across the healthcare ecosystem. For Health Delivery Leaders: The focus must shift from episodic RPM to continuous, predictive chronic care management. Resource Allocation: This model allows for the targeted deployment of care managers and expensive therapies to the highest-risk patients before a crisis, preventing costly ER visits and improving outcomes. Value-Based Care: It provides the objective, longitudinal data needed to succeed in value-based arrangements by demonstrating a reduction in acute events and lowering the total cost of care. Clinician Well-being: By automating surveillance and filtering out the noise, this approach allows clinicians to practice at the top of their license, focusing their expertise on the patients who need them most and reducing the burnout associated with managing overwhelming data streams. For Healthtech Builders: The opportunity is to build the platforms that create and manage these digital twins at scale. A new report on the future of digital therapeutics highlights the growing demand for AI-driven disease management platforms that go beyond simple tracking. Algorithmic Differentiation: The competitive advantage is no longer the sensor, but the sophistication of the AI. Companies that can build and validate algorithms for specific conditions will own the market. New Business Models: This opens the door to outcomes-based pricing. Instead of a simple SaaS fee, companies can share in the savings generated by successfully predicting and preventing a costly adverse event, aligning their incentives directly with the health system and the patient. We are at an inflection point. The conversation around wearables must evolve from the novelty of detecting a virus to the profound impact of managing a lifetime of chronic illness. By embracing the complexity of Physiological Digital Twins and building the operational pipelines to support them, we can move beyond the reactive sick-care model of the past and begin building the proactive, predictive, and truly personalized healthcare system of the future. While the technology is soaring, now we must collectively consider the next step....how to bring this kind of chronic care management to the masses. #StayCrispy -Dr. Matt 

We are in the golden age of health innovation. From AI-driven diagnostics to personalized wellness platforms, our work is fundamentally reshaping the future of care. This progress is fueled by an unprecedented flow of data. Yet, a critical vulnerability exists at the core of our industry: the growing deficit between our technological capabilities and the trust of the patients we serve. The prevailing model of opaque data collection and secondary monetization is not just a reputational risk; it is an unsustainable business strategy. The next generation of market leaders will not be defined by the cleverness of their algorithms alone, but by the robustness of their trust architecture. The HIPAA Paradox and the Coming Regulatory Storm: As an industry, we navigate our data strategies around HIPAA (in the US, GDPR and others around the world). We treat it as the definitive rulebook for patient privacy. But this perspective is dangerously narrow. HIPAA is a floor, not a ceiling, and it was built for a world that no longer exists. It governs covered entities, leaving a vast, unregulated ecosystem of wellness apps, wearables, and direct-to-consumer platforms in a compliance gray area. This regulatory gap is well-documented by the Department of Health and Human Services, which clarifies that data shared with many third-party apps falls outside HIPAA's protections . This regulatory gap is closing. With state-level privacy laws like the California Consumer Privacy Act (CCPA) setting new precedents and the FTC signaling more aggressive enforcement via its Health Breach Notification Rule , the era of regulatory ambiguity is ending. Relying on a minimalist, check-the-box approach to compliance is a strategy with a rapidly expiring shelf life. The question is no longer if a stricter regulatory framework will arrive, but when. The smart play is not to wait for it, but to build for it proactively. Deconstructing the Flawed Value Exchange: The current unspoken contract with the user is often a lopsided one. We offer a service, and in exchange, we capture data whose downstream value far exceeds the immediate benefit provided to the user. This data flows into a complex secondary market of data brokers and aggregators, a market projected to be worth hundreds of billions of dollars , fueling everything from pharmaceutical research to targeted advertising. While the process of "de-identification" provides a layer of legal and ethical cover, we know its limitations. The increasing sophistication of analytical techniques means that re-identifying individuals from de-identified data is often possible by cross-referencing datasets. More importantly, this model creates a fundamental misalignment. When users discover how their data is being leveraged, trust is broken, often irreparably. This leads to increased churn, negative brand perception, and a user base that is increasingly unwilling to share the very data our innovations depend on. It is a house of cards. Trust as a Competitive Moat - An Architectural Blueprint: In a crowded market, the most defensible competitive advantage is not a feature or a price point; it is trust . Companies that treat trust as a core business metric rather than a legal hurdle will attract more engaged users, command greater pricing power, and build more resilient brands. Research consistently shows that a lack of trust is a significant barrier to the adoption of digital health technologies . (Hey, my book talks all about this!) Here is a blueprint for moving beyond compliance to build a foundation of trust: Frame Transparency as a Brand Pillar. Your data policy should not be a document crafted by lawyers to minimize liability. It should be a manifesto, written in plain language, that your marketing team can proudly feature. Use your onboarding, UI, and communications to be radically transparent about what you collect, why you collect it, and the value it creates. Engineer an Equitable Value Exchange. For every data point requested, you must clearly articulate the direct, tangible benefit the user receives. Move away from implicit collection and toward explicit, granular consent. If the value exchange is strong enough, users will willingly opt in. If it is not, the problem is with your value proposition, not the user's reluctance. This is why we allllllll share our data with Google maps for example. We get immense value from up to date directions, and precise placement of where all the construction delays are. Take my data! Build for User-Centric Governance. Empowering the user means more than a settings page. It means building intuitive privacy dashboards, enabling effortless data portability, and providing a simple, verifiable process for data deletion. The future is user-owned health records, and the platforms that embrace this will render closed-silo competitors obsolete. Champion Data Stewardship. The ultimate evolution is to shift the corporate mindset from being a data processor to a data steward. This means accepting a 'fiduciary-like' responsibility to act in the best interest of your users and their data. This is not altruism; it is a long-term strategy for building enterprise value. A Strategic Call to Action: The conversation about data needs to move from the legal department to the C-suite and the product roadmap. It is a fundamental strategic issue that will define the winners and losers of the next decade in health tech. This week, ask these questions within your organization: How clearly do we articulate our data value exchange in the first 60 seconds of a new user's experience? Could a non-technical user read our privacy policy and feel empowered rather than confused? (cough cough, the answer today is probably no!) How would our business model be impacted if our users could instantly port their data to a competitor? The future of healthcare innovation depends on a foundation of trust. It is our collective responsibility to build it.....and it's good business for the future. #StayCrispy -Dr. Matt

Last night, I gave a workshop to a group of international founders about the US healthcare landscape and what they need to know to break in. But as I gave my talk, I realized something important. Even though this advice might sound basic or obvious to some, every founder, whether you're from abroad or based right here in the US, needs to hear this again. These are the foundational questions that determine success or failure. For every health tech founder, the US healthcare market is the ultimate prize: a glittering, multi-trillion-dollar opportunity. But for those entering from abroad, it’s a labyrinth of misaligned incentives and hidden rules. (For those already here, it’s an equally complex home turf!) No matter your origin, you need a playbook. Here is the 5-minute briefing every founder needs before launching in the US. And whether you've been working your startup for a day, a week, a month or a year, make sure to go through the checklist at the end and be honest about your true readiness. The Paradox: Your Biggest Obstacle is Your Biggest Opportunity First, the scale is staggering. The U.S. spends over $4.5 trillion annually on healthcare , representing nearly 18% of its economy. But here’s the paradox: that historic spending buys some of the developed world's worst health outcomes. A 2023 Commonwealth Fund report shows the U.S. lags in life expectancy and has the highest rates of avoidable deaths. The reason is massive inefficiency and waste. For you, the founder, this isn't a bug; it's a feature. Your company's value will be measured by how effectively you solve the friction that costs the system a trillion dollars a year. The Gatekeepers: The Cast of Characters You Must Convince As a founder, you are not selling into a single "system." You are selling to a handful of competing stakeholders who all ask different questions: The Payer (e.g., UnitedHealth): "Show me the 3-year ROI." The Provider (e.g., a hospital): "Will my doctors use it and will it slow them down?" The Regulator (e.g., the FDA): "Where is your clinical data?" The Patient: "Is it easy and does my insurance cover it?" Historically, money flowed through a Fee-for-Service model, rewarding volume. The entire system is now shifting toward Value-Based Care , (at a snail's pace, forever!) where payment is tied to patient outcomes. This shift is your single greatest tailwind. Models like Accountable Care Organizations (ACOs) are hungry for technology that improves quality and reduces costs. The Four Pillars of Your Go-to-Market Plan Whether you are coming from Seoul or Silicon Valley, your strategy must answer four critical questions. Get these right, and you have a foundation for success. Your Clinical Strategy: What is the absolute minimum clinical evidence you need to convince your first customer? The gold standard is a Randomized Controlled Trial (RCT), but pragmatic Real-World Evidence (RWE) is increasingly accepted for digital health. Your Reimbursement Strategy: How, specifically, will you get paid? Is it through an existing CPT code? By enabling a hospital to succeed in a value-based contract? Or a simple SaaS license? "We’ll figure out reimbursement later" is a fatal mistake for a founder. Your Commercial Strategy: Who is your ideal first customer: a research-focused academic center or a profit-driven hospital system? How will you integrate with their Electronic Medical Record (EMR)? Without a clear path into Epic or Cerner, your product is dead on arrival. Your Compliance Strategy: Do you know your FDA classification for Software as a Medical Device (SaMD) ? Are you prepared for the legal and security requirements of HIPAA ? These are not afterthoughts; they are foundational. Entering the US market is a test of strategic clarity. It's not the best technology that wins, but the best technology with a credible answer to the market's unique and complex questions. The tech, is the easy part. Founders Checklist: Key Questions & Action Items Market Understanding: Have we clearly defined how our solution solves the friction and waste in the US system? Do we have a crisp answer for each of the four gatekeepers (Payer, Provider, Regulator, Patient)? Is our strategy aligned with the shift from Fee-for-Service to Value-Based Care? Clinical Strategy: Have we defined the minimum level of clinical evidence required to win our first customer? Have we decided between an RCT, RWE, or a hybrid evidence-generation approach?Have we budgeted for a Health Economics and Outcomes Research (HEOR) study to prove financial value? Reimbursement Strategy: Have we identified the specific mechanism for payment (e.g., existing CPT code, SaaS license)? If pursuing a new CPT code, have we mapped out the multi-year timeline and budget?Can we clearly articulate our value proposition for a value-based care model (e.g., an ACO or bundled payment)? Commercial Strategy: Have we identified our ideal first customer profile (e.g., Academic Medical Center, IDN)? Do we have a detailed, 12-18 month plan for EMR integration with Epic or Cerner? Have we mapped the key decision-makers at a target health system (Clinical Champion, CMIO, CISO, CFO)? Compliance/Regulatory Strategy: Have we determined our product's FDA classification as Software as a Medical Device (SaMD)? Have we budgeted for the legal and technical requirements of HIPAA compliance? Have we signed a Business Associate Agreement (BAA) with all partners who will handle patient data? Start with one specific problem, find US-based partners early, and remember: in American healthcare, evidence is the only currency that matters. Keep innovating, and #StayCrispy! -Dr. Matt

Imagine your doctor concludes your visit not just with a prescription for a pill, but also one for a specialized app to manage your diabetes, improve your sleep, or even treat your child’s ADHD. This isn't science fiction. It’s the reality of Digital Therapeutics (DTx) , a new class of medicine delivered through software. We have incredible technology that can provide cognitive behavioral therapy through a smartphone or guide a patient through addiction recovery. The biggest challenge isn’t inventing these tools, but rather figuring out the billion-dollar question: Who actually pays for them? Today, we're breaking down the complex world of DTx reimbursement, the key that will unlock the future of digital medicine for everyone. First, What Exactly is a "Prescription App"? It’s crucial to distinguish DTx from the thousands of general wellness apps on your phone. Unlike a calorie counter or a meditation guide, a true digital therapeutic must: Be rooted in evidence-based medicine and proven effective in rigorous clinical trials. Receive oversight and often clearance or approval from regulatory bodies like the FDA. Be prescribed by a licensed clinician to treat, manage, or prevent a specific disease or disorder. These are medical interventions, not just lifestyle tools. For an excellent overview, you can check out the work of the Digital Therapeutics Alliance , which sets industry standards. The cautionary tale of pioneers like Pear Therapeutics, which developed FDA-authorized DTx for addiction but ultimately struggled with inconsistent payment, highlights just how critical solving the reimbursement puzzle is. The Three Paths to Payment So, how does a prescription app go from a doctor's recommendation to a paid claim? Currently, there are three main pathways, each with its own set of challenges. 1. The Pharmacy Benefit (The "Pill" Pathway) This is the holy grail. The DTx is assigned a National Drug Code (NDC), just like a traditional medication. A doctor sends the prescription to a pharmacy, and a pharmacy benefit manager (PBM) like Express Scripts or CVS Caremark processes the claim. Why it's great: It seamlessly integrates into the existing workflow for doctors and patients. There’s no new process to learn. Why it's hard: This path is complex and expensive. Convincing PBMs to add a digital product to their formulary requires a mountain of clinical and economic data proving its value is comparable to, or better than, existing drugs. For more on this, see recent policy discussions on how PBMs are approaching DTx . 2. The Medical Benefit (The "Device" Pathway) In this model, the DTx is treated more like a medical device or service. Clinicians use specific medical billing codes (like HCPCS codes) to bill insurers for the product. Why it's great: It offers flexibility and has been a successful route for remote patient monitoring tools and other digital health services. Why it's hard: It’s often a clunky, claim-by-claim process that many doctors' offices aren't set up to handle efficiently. The establishment of new codes by CMS is a step forward, but navigating medical coding for digital health can still be a significant administrative burden. 3. Employer & Health System Contracts (The "Enterprise" Pathway) Frustrated by the slow pace of traditional insurance, many DTx companies have pivoted. They sell their products directly to large, self-insured employers or entire hospital systems as a health benefit for their employees or members. Why it's great: It's a much faster way to get a product to market and generate revenue. Companies like Teladoc and Microsoft have partnered to integrate digital health solutions directly into workplace platforms. Why it's hard: This B2B approach creates a patchwork of access. It only serves people who work for specific large companies, leaving out Medicare patients, small business employees, and the unemployed. A Challenge for All of Us The path to clear and consistent payment for digital medicine is still being paved. This isn't just a problem for tech companies to solve; it's a conversation for clinicians, employers, and patients. As this technology becomes more integrated into standard care, our reimbursement structures will have to evolve with it. If you're a founder in this space, start your reimbursement planning NOW (yesterday would have been better!). This can be what makes or breaks your company. Now I want to hear from you. As a patient, provider, builder, or leader, what's your take on prescription software? Comment and let me know your thoughts. Until next time, #StayCrispy, Dr. Matt

Whether you’re on the clinical front lines or building the technology that supports them, the last few years have felt like a case of whiplash. We rocketed from a period of "growth at all costs", fueled by unprecedented venture capital and promises of total transformation, straight into an era of economic constraint, layoffs, and a relentless focus on the "path to profitability." For those of us in the trenches, this shift isn't just a headline; it's a tangible change in our daily reality. It’s creating a unique and challenging environment: a "messy middle" caught between the immense pressures of today's economy and the revolutionary promise of tomorrow's technology. This isn't just a patient-facing issue. It's a professional crisis for the very people meant to be healing and innovating. Today, I want to dissect this awkward gap from both sides of the screen; from the perspective of the clinicians using the tools and the health tech professionals building them. The Anatomy of the Awkward Gap To navigate this period, we first have to understand the two powerful, opposing forces squeezing the industry. 1. The Economic Headwinds (The "Now") The financial landscape has fundamentally changed. The era of ZIRP (Zero Interest Rate Policy) is over, which means capital is no longer cheap. Venture funding for healthtech has tightened dramatically, and the metrics have shifted from user growth to hard ROI. For health tech companies, this means the directive from the board is no longer "disrupt," but "survive." The focus is on conserving cash, achieving profitability, and reducing burn rate, which has led to widespread layoffs. These cuts often hit "cost centers" first; the crucial but hard-to-quantify teams in customer success, implementation, and forward-looking R&D. For hospitals and health systems, the pressure is just as intense. Facing their own razor-thin margins, CFOs have become the primary gatekeepers for any new technology purchase. A tool that doesn't demonstrate a clear, near-term return on investment, either through cost savings or proven efficiency gains, is a non-starter. 2. The AI Horizon (The "Next") Simultaneously, we're living through an AI revolution that promises to solve healthcare's most intractable problems. The vision is no longer science fiction; it’s a tangible roadmap featuring: Ambient Intelligence: AI scribes that passively listen to a clinical encounter and auto-generate a complete, accurate note, order labs, and draft referral letters. Predictive Analytics: Algorithms that can identify patients at high risk for sepsis or readmission before they decompensate, allowing for proactive intervention. Generative AI & LLMs: Tools that can automate the soul-crushing process of prior authorizations or summarize a 500-page patient record into a concise clinical summary. Here’s the fundamental problem: The economic cuts are happening today. The AI-powered solutions, while incredibly promising, are not yet seamlessly integrated, universally trusted, or capable of navigating the last-mile complexities of clinical workflow. We are living in the gap between the drawdown of human-powered support and the ramp-up of AI-powered automation. The View from the Front Lines: A Clinician's Reality For doctors and nurses ( and I'm waving at you too pharmacists!), this "messy middle" isn't a strategic challenge; it's a daily burden. You were promised technology that would reduce burnout, but the human support for the systems you already have is thinning out. When your EHR freezes mid-shift or a third-party application fails, the dedicated support specialist you used to call has likely been replaced by a generic ticketing system with a 24-hour response time. You’re forced to become a part-time IT specialist, a role you were never trained for and that takes you away from patients. This is the downstream effect of health tech’s financial squeeze. The "technical debt" accrued over years of using clunky, non-interoperable EHRs is now compounded by a "support debt," and clinicians are the ones paying the interest with their time and sanity. Many of you have experienced "pilot program purgatory": participating in an exciting trial of a new AI tool that actually saves you time, only to see it shelved after six months due to budget cuts, forcing you back to the old, inefficient workflow. The View from the Trenches: A Health Tech Professional's Dilemma For those of you building the products, this period is just as fraught with tension. You entered this field to solve problems for clinicians, yet you’re caught between your users' needs and your company's financial imperatives. The game has shifted from "disruption" to "integration." A slick UI is no longer enough. To succeed, your product must now flawlessly integrate with the complex, legacy ecosystems of Epic, Oracle/Cerner, and others. This requires deep institutional knowledge and robust engineering resources; the very things that are often downsized in a layoff. You understand the customer success crisis intimately. You know that in healthcare, a product is only as good as its training, implementation, and adoption. Yet you watch as those very teams are cut, knowing that it will lead to failed deployments and frustrated users down the line. Most painfully, you're under immense pressure to "show ROI now." The 18-to-24-month sales and implementation cycle that's standard in healthcare is at odds with a board that needs to see positive ROI in 6 to 12 months. This forces difficult product decisions, prioritizing features that look good on a sales deck over foundational improvements that solve deep, systemic workflow problems for your clinical partners. Dr. Matt's Take: Forging a Path Through the Middle This "messy middle" is one of the greatest leadership challenges our industry has faced. But it is also a powerful clarifying moment. The hype has evaporated, and only real, durable value will survive. To get to the other side, both clinicians and builders must adapt their approach. For Clinicians: Your voice has never been more critical. Stop accepting technology that adds to your workload. Become demanding, educated customers. Your detailed feedback on workflow inefficiencies is the most valuable commodity in healthtech. Champion the tools that give you time back and band together to demand that your administration invests in them. Don't settle for "good enough." For Health Tech Professionals: This is a flight to quality. The companies that win this next decade will be those that are obsessively focused on solving a specific, painful clinical problem completely. They will treat clinicians as essential design partners, not just end-users. They will over-invest in implementation and support, recognizing that trust is the ultimate currency. Your mission is to be the voice of the user in every meeting, relentlessly advocating for solutions that deliver real, measurable value, not just hype. The bridge across this gap will be built on a foundation of co-creation and trust. We have a once-in-a-generation opportunity to build a truly intelligent, efficient, and humane healthcare system. But we can only do it together. #StayCrispy and informed, -Dr. Matt