The Anti-AI Stimulus: Why the "Boring" Money is Winning in 2026

November 25, 2025

If you spent the last month scrolling through LinkedIn or attending health tech mixers, you would be forgiven for thinking the only thing happening in healthcare right now is the deployment of autonomous AI agents. The hype cycle has fully pivoted from "AI as a tool" to "AI as a replacement," with endless pitch decks promising to automate everything from revenue cycle management to patient triage.


But while the venture world is looking at the stars, the federal government is pouring concrete.


The most significant news in health technology this month didn’t come from a product launch in the valley. It came from a bureaucratic filing deadline in Washington, D.C., where the Centers for Medicare & Medicaid Services (CMS) confirmed that states have overwhelmingly bought into the new wave of rural health funding.


We are witnessing the quiet launch of what I call the "Anti-AI Stimulus."


While the industry obsesses over Large Language Models (LLMs), the checkbooks for 2026 are opening for something far less sexy but far more critical: basic infrastructure. We are seeing a convergence of major funding streams, most notably the CMS AHEAD Model and the rapid expansion of Rural Emergency Hospital (REH) designations, that signal a massive shift in how care is delivered outside of major cities.


For the founders, investors, and strategists reading this: ignore this shift at your own peril. The smart money isn't chasing chatbots; it is chasing the plumbing that makes modern medicine possible.


The Policy Shift: From Volume to Value (Finally)


To understand where the money is going, you have to understand the problem the government is trying to solve. The rural hospital business model is broken. Recent data from The Chartis Group indicates that 50 percent of rural hospitals are operating in the red. They cannot survive on fee-for-service medicine because they simply do not have the volume.


The government’s response, through programs like the AHEAD Model, is to move states toward "global budgets."


In plain English, this means paying hospitals a fixed amount to keep a specific population healthy, rather than paying them for every MRI and surgery they perform. This is a radical reimagination of the financial incentives. When a hospital is paid a flat fee, keeping a patient out of the hospital becomes profitable.....but it also sounds like 'pop health', or 'at risk' models?!


This changes the technology wishlist overnight. If you are a hospital CEO under a global budget, you don't need a robot that does surgery faster. You need a remote monitoring platform that prevents the patient from needing surgery in the first place. You need data interoperability that actually works. You need the "boring" stuff.


The Conflict: A Turf War for Survival


There is a tension inherent in this funding that few are talking about. It is effectively a battle over the definition of "rural healthcare" and who controls the purse strings.


On one side, you have the Critical Access Hospitals (CAHs) and independent rural clinics. Their argument is straightforward: they are the physical lifelines in these communities. They view this capital as survival money intended to fix leaking roofs, update 15-year-old servers, and keep the emergency room lights on.


On the other side, you have the large academic medical centers in larger cities. Their argument is equally compelling: because rural facilities often lack specialized capabilities, the complex cases are transferred to the city. They rgue that they are the de facto safety net for the rural population and deserve a cut of the funding to maintain the "mothership" capacity. This isn’t just improved accounting; it is a fundamental strategic conflict. Is the goal to treat patients where they live, or to build better highways to the city?


The Technology Implications: The "Unsexy" Thesis


This is where the rubber meets the road for the health tech community. If you read the specific language in the Notice of Funding Opportunities related to these rural initiatives, the focus is on "sustainable access" and "technology-enabled solutions." If you are pitching a generative AI copilot that costs $100 per seat, you are likely barking up the wrong tree. The winners of these contracts will not be the companies selling "optimization." They will be the companies selling "foundation."


1. Cybersecurity is the Priority


We cannot ignore the reality that rural hospitals are currently the softest targets for ransomware. They are often running legacy software on deprecated operating systems, think Windows 7 or even XP running on MRI machines (a gross over exaggeration but you get the point), because they lack the IT budget to upgrade.


When a rural hospital gets hacked, patients get diverted, and people die. A significant portion of this new funding will go strictly toward cybersecurity hardening. It is not exciting. It will not make for a viral TechCrunch headline. But it is the prerequisite for everything else.


2. The Death of the "AI vs. Broadband" Debate


We love to talk about AI diagnostics in remote clinics, but those conversations are theoretical if the clinic has a shaky DSL connection that drops every time it rains.


The USDA’s ReConnect Program and similar initiatives are acknowledging that "digital health" is impossible without "digital access." Expect massive spending on "plumbing"; high-speed satellite links, secure data backbones, and reliable telehealth endpoints. You cannot deploy the future of medicine on 1990s infrastructure.


3. Workforce Extension, Not Replacement


The most viable "tech" play here is not replacing doctors, but extending the few we have left. Rural America is facing a massive shortage of specialists. We do not need AI to be the doctor; we need technology that allows one intensivist in a city to monitor patients across ten different rural ICUs simultaneously.


Tools that facilitate this "one-to-many" care model, like virtual nursing or e-ICU platforms, will find immediate product-market fit. The goal is leverage.


The Borderless Reality


As I wrote in The Borderless Healthcare Revolution, the concept of "borderless" care isn't limited to medical tourism or crossing international lines. The most difficult borders to cross are often the invisible ones within our own country: the county lines that separate a well-funded university hospital from a struggling rural clinic.


Technology has the power to erase those borders, but only if we invest in the right kind of technology.


The "hype" cycle tells us that the future is an AI agent that can diagnose a rare disease in seconds. The "reality" cycle, fueled by federal dollars and actual clinical need, suggests the future is a rural hospital that doesn't get hacked, has a stable internet connection, and can access a specialist without putting a patient in an ambulance for a three-hour drive in the snow.


If you are building for that future, 2026 is going to be a very good year.


#StayCrispy


-Dr. Matt

The Anti-AI Stimulus: Why the "Boring" Money is Winning in 2026

The Access Revolution Hidden in FinTech

November 18, 2025
During the 8th China International Import Expo (CIIE) last week, Sino Biopharm and MediTrust Health signed a landmark strategic agreement that signals a massive shift in how we pay for innovation. While the ink dried in Shanghai, the implications are vital for global healthcare access. This partnership is not just a distribution deal. It is the formalization of a "drug-insurance integration" model where commercial insurance logic is embedded directly into the pharmaceutical supply chain. This move coincides with China's broader "Healthy China 2030" initiative and the introduction of a new Category C in the National Reimbursement Drug List (NRDL) . This regulatory sandbox is specifically designed to allow commercial insurers to cover high-value innovations that the state budget cannot yet absorb. The Core Friction: Financial Barriers are Access Barriers We are currently witnessing a collision between two unstoppable forces. First is the exponential rise in the cost of curative therapies. We are seeing gene therapies for conditions like Sickle Cell Disease and Hemophilia priced upwards of $3 million per dose. Second is the rigid, legacy infrastructure of payer reimbursement. In the US and Europe, this manifests as increasing "financial toxicity" , a term now frequently discussed in tumor boards alongside clinical toxicity. Prior authorization delays, copay accumulators, and value-based contracting (VBC) hurdles create a landscape where a drug can be FDA-approved but functionally non-existent for a patient in a rural zip code. The traditional model; where Pharma makes the drug, Payer decides coverage, and Patient prays for access, is failing. It creates a two-tier system where scientific breakthroughs are only accessible to those with the liquidity to pay for them or the geographic luck to be near a major academic center. The New Model: Democratizing Risk The Sino Biopharm x MediTrust collaboration introduces a different architecture. They are building what they call a "dual-track" system. Instead of waiting for public or state insurance to cover a new and expensive oncology drug, the manufacturer partners with a tech-enabled platform to wrap the drug in a specific commercial insurance policy at the point of access. This is FinTech meets Pharma. The drug is no longer just a chemical product. It is a financial asset bundled with a risk-management tool. Key components of this convergence include: Outcomes-Based Underwriting: This isn't just a coupon. It relies on Real-World Data (RWD) integration to underwrite the patient's risk in real-time. If the therapy fails to meet specific clinical milestones, the "insurance wrapper" kicks in. This protects the patient's wallet from wasted spend and protects the payer's ledger from high-cost failures. It moves the industry from paying for treatment to paying for results. Direct-to-Patient Financial Rails: MediTrust’s "Smart Insurance Solution" and "Care2Pay" platform bypass traditional claims processing lag. By using AI to process claims instantly ("one-code direct payment"), they act as a dedicated centralized bank for that specific therapy. This ensures that cash flow issues, often the death knell for adherence in chronic care, do not interrupt a course of treatment. The "Commercial Catalog" Catalyst: This move aligns with the Category C regulatory push in China , effectively privatizing the risk of innovation while keeping basic care public. It creates a specific lane for drugs that are "too expensive for public, too important to ignore." The "Access" Angle: Why This Matters for Patient Access The real promise here is not just better margins for pharma companies. It is about expanding the Total Addressable Market (TAM) to include patients who are currently 'priced out of hope'. When a manufacturer subsidizes the financial risk via embedded insurance, they lower the barrier to entry for patients in rural or underserved demographics. It transforms a high-risk gamble into a manageable subscription or warranty. We see early echoes of this with Pfizer and AstraZeneca's recent moves toward "warranty models" , but the US market is often stymied by the complexity of Medicaid Best Price rules and fragmented private payers. The China model, with its "platform" approach, offers a blueprint for how to potentially scale this. It could be the key to bringing expensive cell and gene therapies to community hospitals rather than keeping them sequestered in major academic centers. Strategic Takeaway For health tech leaders, the lesson is clear. The next unicorn will not be a company that discovers a new molecule, nor one that sells a new insurance plan. It will be the infrastructure layer that connects the two. If you are building in health fintech or digital health, perhaps stop looking at workflow efficiency (e.g., "faster prior auth") and start looking at financial liquidity . Can you build the rails that allow a manufacturer to underwrite a patient's deductible? Can you build the data pipe that proves a gene therapy worked, triggering a payment release? You are not just solving a billing problem. You are solving an access problem. The Path Forward We often mistake invention for innovation. But a breakthrough therapy that remains out of reach due to antiquated billing models is not a success. It is a failure of imagination. The technology to democratize access exists. We simply need the will to deploy it.  Here is my challenge to you this week. Look at your product roadmap. Find the friction point where a financial barrier is masquerading as an operational one. Solve that and you do not just improve a metric. You open the door for a patient who has been waiting outside. Let's get to work. #Stay Crispy -Dr Matt

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 

The Ultimate Personalization: How Digital Twins Are Revolutionizing Your Healthcare

November 4, 2025
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