Ignition by RocketTools

In February 2024, hackers walked into the largest healthcare clearinghouse in America through a Citrix portal that didn't have multi-factor authentication. They used credentials stolen from a previous breach — someone, somewhere, had reused their password. Within hours they had ransomware running. Within days, pharmacies across the country couldn't fill prescriptions.The ransom payment was $22 million in Bitcoin. The total cost to UnitedHealth Group is now over $2.457 billion. The number of Americans whose data was exposed is 192.7 million — roughly 58% of the country. And it all started with one reused password.This is Part 1 of an 8-part Healthcare Security series. In this episode I walk through why your password habits are probably just as dangerous, why "47 logins" understates the reality for healthcare executives, and the four password managers I actually recommend — with the honest tradeoff on each, and no affiliate links. I also explain why a single patient record sells for $250 on the dark web while a credit card goes for $5, and why your AI tool account in 2026 holds more sensitive information than most of your work files.In this episode:The Change Healthcare breach timeline and what Andrew Witty admitted under oath to CongressWhy password patterns ("FirstName2024!" and friends) are now exactly what attackers test firstThe 47-logins-on-average problem for healthcare execs and why the real number is higherThe four password managers I'd recommend: Dashlane, 1Password, Proton Pass, and Bitwarden — pricing, tradeoffs, who each is right forA four-step action plan you can run this week, starting with one email to your IT team📺 Watch on YouTube: https://youtu.be/N62kieISWiI📝 Read the director's cut companion post on Substack (deeper on Witty's Senate testimony, the dark web pricing texture, and the AI tool risk section I had to cut for time): https://open.substack.com/pub/danmccoymd/p/the-872m-password-mistake-was-actuallyNext week, Part 2: why CISA and the FBI told Americans to stop using SMS-based MFA, the authenticator app I switched to after leaving Microsoft Authenticator, and the small piece of hardware I added on top.I'm Dan McCoy. Ignition by RocketTools is the podcast for healthcare executives, physicians, and AI builders trying to think clearly about where this is all going.

Twelve years ago, a Target statistician built a model that could predict pregnancy from 25 shopping items. The story usually gets told as a privacy parable. I'm telling it differently — as a preview of how healthcare AI is going to work for the rest of our lives.Your smartwatch can already flag atrial fibrillation days before a cardiologist would. It can detect depression weeks before clinical scoring catches it. It can spot cognitive decline six to twelve months before you notice. The science isn't the question anymore. The question is who gets to see what comes out of the model — and whether we build the governance before the surveillance economy locks in.In this episode I get into:• Why Andrew Pole's 2012 Target model was a dry run for what's coming in clinical AI• What the wearable accuracy numbers (70–95%) actually mean — and where they get softer than the headlines suggest• The function-creep economy that's already running: CGM data sold to ad partners, period-tracking app subpoenas, life insurers bidding on de-identified wearable sets• The 99.98% problem — why "de-identified" data isn't• Habit-disruption windows: the real case for early-detection surveillance in healthcare• Four policy moves that would change the data-broker incentive structure overnightFull written companion with sources and citations: danmccoymd.substack.comWatch on YouTube: https://youtu.be/LbE6TbGIzIYI'm Dan McCoy. Ignition by RocketTools is the podcast for healthcare executives, physicians, and AI builders trying to think clearly about where this is all going. New episode every week.

Y Combinator has a name for it: burn tokens, not headcount. A health insurance company called Decent runs with nine people total. Twofold does revenue cycle management with three. Deep Cura Health handles patient scheduling, prior authorization, and insurance verification with two humans and seven AI agents.The AI-first model is real. It's working. And in healthcare, every one of these companies has made the same quiet choice: they're attacking admin, not clinical.This episode unpacks why — and why the conventional wisdom ("admin is safe to automate, clinical isn't") is the wrong frame. The real line isn't admin versus clinical. It's decision support versus decision making. IBM burned $4 billion learning the difference with Watson Health. The next generation of healthcare AI companies will either learn from that, or rebuild the same trap with better UX.What's covered:How AI-first companies are quietly rewriting healthcare staffingWhat IBM Watson Health actually got wrong — and why "the AI was wrong" misses the lessonWhy most "decision support" products today are decision-making in a trench coatThe payment-model problem nobody is pricing: a 5,000-patient panel breaks fee-for-serviceThe companies positioning themselves on the right side of the line🎥 Watch on YouTube: https://youtu.be/9fHKQqm15qo📝 Companion essay (with the receipts I had to cut for length): https://danmccoymd.substack.com/p/the-part-of-ai-first-healthcare-that

In March 2026, the Blue Cross Blue Shield Association published research blaming hospitals' AI billing tools for $2.3 billion in added healthcare costs. It was a grievance — not a strategy. And it stands in sharp contrast to 1981, when the same Association faced a national-platform problem and built something: BlueCard, the shared claims-routing layer that turned 36 independent regional plans into the reason one in three Americans carry a Blue card today.This episode asks the contrarian question: in an AI world, is the Blues' patchwork of 36 plans a fatal weakness — or the exact architecture the future rewards?What we get into:Why Elevance and Highmark are racing in opposite directions (multi-vendor horizontal vs. Epic single-stack vertical) — and what the other 34 plans aren't doingThe plan-level wins that already shipped (BCBS Minnesota, Illinois, Arkansas) — and why none of them are federatedThe federated-learning research that says the Blues' structure is the ideal AI architecture — including a peer-reviewed BCBS Louisiana study where regional models beat national algorithmsThe Optum problem: what happens if UnitedHealth builds the AI equivalent of BlueCard before the Blues doThree concrete signals to watch by the end of 2027Full companion essay on Substack with sources and the three-signal checklist: https://open.substack.com/pub/danmccoymd/p/blue-cross-built-the-last-healthcareWatch the video version: https://youtu.be/LCrRFqTTtuoConnect with me at RocketTools.io for AI Strategy Consulting and podcast or speaking engagements. 

You've been told American healthcare is expensive because of greedy insurers, pharma profits, or the cost of innovation. That story is incomplete to the point of being misleading.The largest single driver of US healthcare spending isn't drug companies — it's hospitals. And hospital prices haven't merely risen; they've grown roughly 3x faster than overall inflation since 2000. No sector does that for two decades by accident.In this episode, Dan McCoy MD breaks down the three federal policy choices that designed America's hospital pricing crisis:• ACA Section 6001 — the 2010 ban on new physician-owned hospitals, the one competitor proven to be roughly a third cheaper. $2.2B in planned development killed; 75 hospitals never built.• Certificate of Need laws — still active in 41 states, letting incumbent hospitals veto their own competition.• Site-specific Medicare payment — paying hospitals 2–3x what it pays a physician office for the identical service.Add a starved FTC (~13 challenges out of ~561 hospital mergers from 2010–2015) and you get the result: ~97% of metro areas with highly concentrated inpatient markets, and prices that rise 15–30% higher than competitive ones.It isn't a mystery. It's a mechanism.If you run a health plan, here's the takeaway: your hospital costs are set by market structure, not market forces — and the policy landscape (site-neutral reform, Certificate of Need repeal) is finally starting to shift.📺 Watch the video version: https://youtu.be/aWtOw8PxHTU📝 Full research sources, all 12 cited studies, and a bonus analysis of the political economy of hospital lobbying — on the SubstackSubscribe so you don't miss the next episode.This episode is for educational and informational purposes and is not medical, legal, or financial advice.

There's a billionaire fight happening in an Oakland federal courtroom right now. The headlines are calling it Elon Musk versus Sam Altman—tech titans, AI drama, billions at stake. But here's what almost nobody is telling you: if Elon wins this case, the next phone call won't be from Silicon Valley. It'll be from the general counsel's office at every nonprofit hospital in America.The question on trial isn't really about AI chatbots. It's whether a charity that took tax-deductible donations under the explicit promise of serving the public can quietly convert itself into a for-profit empire where insiders pocket the upside. That is the exact business model of the modern nonprofit hospital.In this episode, I break down the numbers nobody talks about, the smoking gun study from Health Affairs, the three legal doctrines on trial, and what happens next—whether Musk wins, loses, or lands somewhere in between.Watch on YouTube: https://youtu.be/gXCwinAwrMARead the full breakdown: https://open.substack.com/pub/danmccoymd/p/is-your-local-hospital-doing-exactly

A 6-month-old named KJ Muldoon just received the first personalized CRISPR gene therapy ever made — designed, manufactured, and administered for his exact mutation in six months. Nature named him to the Top 10 People Who Shaped Science of 2025.The miracle is real. The financing model isn't.Gene therapies run $2M to $3.5M each. Insurance contracts are annual. Gene therapy benefits last a lifetime. The employer who pays in year one rarely sees the savings — average commercial plan tenure is three years.In this episode:— Why the "gene therapy tsunami" narrative is overstated (EBRI's numbers tell a different story)— The free-rider problem and why annual contracts can't price lifetime cures— The concierge medicine paradox: we pay $3M to cure you, but won't pay $300/mo to keep you healthy— Four financing models worth knowing for 2026: gene-therapy-specific stop-loss, outcomes-based agreements, risk-pooling platforms, and performance-based annuities— How AI is compressing drug development timelines — and why that compounds the budget problemWe have 21st century cures and 20th century accounting. Eventually, one of those has to change.Watch the full video: https://youtu.be/ap2XjNf3LN0Read the Substack companion piece: https://open.substack.com/pub/danmccoymd/p/21st-century-cures-20th-century-accountingFull sources and the deep dive: danmccoymd.substack.com

If you're a medical student picking a specialty in 2026, you're being asked to bet a decade and $300,000 in debt on a market nobody is teaching you to read.This episode is the framework I wish someone had given me — plus the data nobody else is putting in front of med students.In this episode:• Why 76% of all FDA-cleared medical AI targets a single specialty — and why residency applications to it are still up 30%• The four kinds of physician cognition (and why "AI replaces cognitive work" is the wrong frame entirely)• Which specialties get repriced, restructured, or protected — and why psychiatry might be safer than radiology• The 2026 CMS efficiency adjustment nobody is talking about — and why it's a ratchet, not a one-time cut• What medical schools are (and aren't) doing to prepare the next generation of physicians for an AI-driven workforceWatch the video version with charts and visuals: https://youtu.be/NETog9SBtZsFull sources and the deep dive: danmccoymd.substack.com

Last week the Texas Medical Association joined a federal antitrust lawsuit against MultiPlan — recently rebranded as Claritev. The story being told is doctors versus insurers, an unlawful cartel, $19B in alleged underpayments. It's a clean story. The moment you look at the actual fee math from the complaint, it falls apart.In this episode I walk through MDL 3121, the $1,000 → $200 cascade, the DOJ's March 27 Statement of Interest backing the plaintiffs' antitrust theory, and what self-funded employers should be asking their TPA on Monday morning. The bellwether trial isn't until December 7, 2027 — three years of fees away.About 42% of what the algorithm calls "savings" never makes it to the plan. That's not a doctors-vs-insurers story. It's a third-character story.▶ Watch the video version: https://youtu.be/H3TQUxJt2Xo 📝 Read the written deep dive (full fee math, sources, 3-question employer checklist): https://danmccoymd.substack.com/p/nobody-in-the-multiplan-lawsuit-isSOURCES MENTIONEDAMA on the litigation: https://www.ama-assn.org/health-care-advocacy/judicial-advocacy/health-insurance-price-fixing-real-and-ama-fighting-itTMA press release: https://www.texmed.org/Template.aspx?id=67699DOJ Statement of Interest coverage: https://www.jdsupra.com/legalnews/doj-adopts-aggressive-stance-against-4076897/NYT investigation summary: https://whatleykallas.com/nyt-investigation-shows-how-health-insurers-use-multiplan-to-reduce-payments-to-medical-providers-to-iincrease-their-fees-and-profits-at-the-expense-of-patients/Capitol Forum on MultiPlan billing incentives: https://thecapitolforum.com/provider-shows-how-multiplan-incentivizes-him-to-raise-his-billing-rate-multiplan-says-it-does-not-encourage-providers-to-overcharge/Seattle Times reprint: https://www.seattletimes.com/nation-world/insurers-reap-hidden-fees-by-slashing-payments-you-may-get-the-bill/KFF on 2026 small group premiums: https://www.kff.org/health-costs/how-much-and-why-premiums-are-going-up-for-small-businesses-in-2026/Medscape Physician Compensation Report 2025: https://www.medscape.com/slideshow/2025-compensation-overview-6018103LEGAL DISCLAIMER This episode covers public reporting and pending litigation. All allegations are unproven and may ultimately be rejected in court. MultiPlan, Claritev, and the named insurers deny wrongdoing. Nothing here is legal, financial, or medical advice.Full sources and the deep dive: danmccoymd.substack.com

49% of plan sponsors reported claims exceeding $1 million last year — double the rate from the year before. And stop-loss carriers are responding not by covering you better, but by finding creative ways to limit their exposure to the most expensive treatments.In this episode, I break down:→ Why stop-loss insurance was built for a different era (when catastrophic meant $300K, not $4.25 million)→ What carriers are actually doing — lasering, exclusions, and reimbursement term misalignment — to push risk back to employers→ The gene therapy pipeline problem: 48 approved therapies today, dozens more coming, and no historical data to predict frequency→ Why even jumbo employers who've never needed stop-loss should reconsider→ The 5 things every self-funded employer needs to do at renewal — including the one question that will tell you everything about your coverageThe mainstream story is that gene therapy is a miracle of modern medicine. It is. But the financial infrastructure wasn't built for this reality, and carriers have decided that the most expensive thing in healthcare is now predictable enough to exclude.You should find that arrangement concerning.📺 Watch the full video breakdown:https://youtu.be/1wwrtwmYAPE📖 Read the Substack post with sources:https://open.substack.com/pub/danmccoymd/p/the-stop-loss-crisis-what-self-funded📧 Subscribe for more healthcare benefits analysis:https://danmccoymd.substack.com

Andrej Karpathy just released AutoResearch — a 630-line Python tool that lets AI agents run hundreds of experiments overnight on a 35-cent GPU rental. In one test, 35 autonomous agents completed 333 experiments while everyone slept and found 20 improvements that worked. No humans involved.Meanwhile, your benefits broker spends maybe 20 hours on your entire annual renewal — most of it pulling quotes and formatting spreadsheets — to manage a budget that consumes 25-40% of your total payroll.In this episode I break down what autonomous research actually is, why the analytical pattern applies directly to healthcare data and benefits design, and three specific questions every employer should be asking their broker at renewal. I also cover the three failure modes of AI research identified by economist Joshua Gans and how Karpathy's system addresses each one.The benefits consulting industry is about to have its spreadsheet moment. The tools exist, the data exists, the pattern is proven. The only question is whether you'll be the one using it or the one disrupted by someone who does.Full sources and the deep dive: danmccoymd.substack.comAlso checkout my YouTube Channel.

The AI labs are selling you $10,000 a month in computing power for $600. They're doing it at massive losses. And they have very specific reasons you should understand.In this episode, I break down the $670B subsidy era fueling healthcare AI — what it means for builders, when it ends, and four strategies to exploit it before the economics correct themselves.We cover:Why AI pricing follows the exact same playbook as early AWSThe real numbers behind OpenAI's and Anthropic's lossesFour strategies to maximize subsidized compute (from $600/mo subscriptions to $2,500 local hardware)Healthcare startups building real businesses on below-cost AIThe subsidy timeline: when prices normalize and what to do before they doWhether you're a healthcare founder, an operator evaluating AI tools, or just trying to understand why trillion-dollar companies are giving away compute — this one lays it out with data, not hype.Watch on YouTube: https://youtu.be/uDB_VJAX05gFull sources and the deep-dive subsidy timeline analysis: https://open.substack.com/pub/danmccoymd/p/the-golden-age-of-healthcare-ai-and?r=11z0su&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

Amazon launched an AI health agent inside the Amazon Shopping app — the same app where you order paper towels. It books appointments, manages prescriptions, explains lab results, and connects you to real doctors. Prime members get five free virtual care visits covering 30+ conditions.This isn't a chatbot experiment. Amazon built this on Bedrock using a multi-agent architecture with auditor and sentinel agents that escalate to human providers in real time. Combined with One Medical's 200+ clinics, Amazon Pharmacy, specialty referral partnerships with Rush and Cleveland Clinic, and a billing relationship with 200 million households — Amazon now owns the full healthcare stack.In this episode, I break down how Amazon assembled this over nearly a decade (PillPack in 2018, One Medical for $3.9B in 2023), why the pricing strategy matters more than the AI, and the three things every health system executive should be watching over the next 12 months: the employer channel play, the data advantage, and the behavioral shift that happens when asking a healthcare question becomes as casual as checking the weather.Watch the full video: https://youtu.be/OhHrxHEnSA0?si=B6MeSTiQTGId8388Full sources and deep dive: https://open.substack.com/pub/danmccoymd/p/amazon-just-put-a-doctor-in-your

Anthropic just announced Claude Mythos Preview — an AI model so capable at finding software vulnerabilities that they won't release it publicly. Instead, they launched Project Glasswing with Apple, Google, Microsoft, and others, committing $100M to use the model defensively. In weeks, Mythos found thousands of zero-day vulnerabilities across every major operating system and browser — including one in OpenBSD hiding for 27 years.But the cybersecurity headlines aren't the whole story. The same vulnerability chaining capability that links multiple software flaws into sophisticated exploits maps directly to how polygenic disease works — cascading gene interactions across multiple variants that we've never been able to trace. With models like Arc Institute's Evo 2 and DeepMind's AlphaGenome already decoding the genome, Mythos-class reasoning could change everything about how we understand and treat disease.If we can hack code to break it, we can hack code to fix it — including the code that makes us sick.Sources and full write-up: https://open.substack.com/pub/danmccoymd/p/hacking-dna-the-anthropic-story-nobodys

Meta acquired Moltbook. OpenAI put ads in ChatGPT. Microsoft found 31 companies actively poisoning what AI assistants recommend. Everyone's calling it AI-native marketing — but what they're really describing is an influence mechanism with no disclosure, no regulation, and direct access to how people make decisions.In this episode, I break down Microsoft's AI Recommendation Poisoning research, why OpenAI's health advertising exclusions don't actually solve the problem, the insurance company AI lawsuits you should know about (UnitedHealth's nH Predict, Cigna's PXDX), and the 70-year regulatory gap between subliminal advertising bans and prompt injection. When this reaches healthcare — and it will — the implications for patients, providers, and benefits managers get genuinely concerning.Research sources and extended analysis: https://open.substack.com/pub/danmccoymd/p/prompt-injection-is-subliminal-advertisingWatch the video version: https://youtu.be/4vECwmEUHEs

What's really happening with AI in healthcare? The common story is that health systems just need to find the right tool — the best ambient scribe, the smartest chatbot. But the reality is more complicated.In this episode, I break down why Sutter Health's AI agent deployment through Hyro tells us everything about where healthcare AI is actually heading, why 63% of healthcare leaders say interoperability is the number one AI capability they want, and why the organizations seeing real ROI did the boring infrastructure work first.If you're a benefits consultant, health system leader, or anyone advising healthcare organizations — stop evaluating AI tools. Start evaluating integration readiness.Sources and the deep dive: danmccoymd.substack.com

The telehealth boom was supposed to revolutionize healthcare forever—but what went wrong? In this episode, Dan McCoy unpacks the dramatic fall of industry giants like Teladoc and Amwell, revealing how their high-profile bets on nationwide distribution networks failed to stand the test of time. More importantly, you'll hear why the real disruptor isn’t a return to in-person care, but the explosive rise of AI-powered tools that are decentralizing healthcare delivery.Dan breaks down the structural shifts pushing telemedicine incumbents to the brink, explores the rapid adoption of ambient clinical AI, and explains how local practices now leverage advanced technology to deliver more personalized, context-rich care. If you care about the future of healthcare—from benefit managers to health system execs to curious entrepreneurs—this episode is your essential guide to what’s next, who’s winning, and why yesterday’s telehealth playbook no longer applies.Check out my SubStack for a deeper analysis:  https://danmccoymd.substack.com/

Most employers think they're getting a deal on healthcare. They're not. The exposed rate data tells a different story — one where the same knee replacement costs wildly different amounts depending on which hospital and which network you're in, even within the same city.In this episode, I break down the network arbitrage game: how hospital systems use their leverage to extract premium pricing, why your "broad network" plan is probably the most expensive option, and what the exposed price transparency data actually reveals about where the money goes.We cover:Why the same procedure can cost 3-5x more at one hospital vs. anotherHow hospital systems use "must-have" leverage to inflate entire network contractsWhat narrow and tiered networks actually save (and what they cost in access)The real math behind reference-based pricingWhy most employers have never seen the exposed rates they're payingThis isn't theory — it's what the data shows.Full sources and the deep dive: danmccoymd.substack.com

Mount Sinai just published the first independent safety evaluation of ChatGPT Health — and the findings should change how you think about AI in healthcare.Published in Nature Medicine, researchers ran 960 patient interactions across 21 medical specialties. What they found wasn't that ChatGPT gets medicine wrong. It's that it gets the diagnosis right, then tells you to do the wrong thing about it.In this episode, we break down:Why ChatGPT told patients to wait in over half of true emergencies — after correctly identifying the danger in its own explanationThe inverted suicide crisis alerts that fired for sadness but went silent when patients described specific plans for self-harmThe sycophancy problem: why ChatGPT is 12x more likely to agree when you downplay your own symptomsWhere ChatGPT actually performs well (93% in semi-urgent cases) — and why that makes the failures harder to spotWhat this means for anyone using, building, or recommending AI health toolsSources & Links:Primary Study — Nature Medicine, Feb 2026https://doi.org/10.1038/s41591-026-04297-7Mt. Sinai Press Releasehttps://www.mountsinai.org/about/newsroom/2026/research-identifies-blind-spots-in-ai-medical-triageForbes: "ChatGPT Provided Wrong Advice In Over 50% Medical Emergencies Tested"https://www.forbes.com/sites/brucelee/2026/03/08/chatgpt-provided-wrong-advice-in-over-50-medical-emergencies-tested/NPR: "ChatGPT might give you bad medical advice, studies warn"https://www.nhpr.org/2026-03-11/chatgpt-might-give-you-bad-medical-advice-studies-warnRelated: AI Chatbots and Medical Misinformation — Communications Medicine, 2025https://doi.org/10.1038/s43856-025-01021-3Full research brief and deep dive on Substack:danmccoymd.substack.com

Mark Cuban's Cost Plus Drug Company sells a cancer drug for $47 a month. That same drug often costs well over $2,000 at your pharmacy. Both include a 15% markup. The markup is the same — the price is dozens of times higher, and nobody's asking why.In this episode, I break down why percentage-based pricing is the single most inflationary structural design choice in American healthcare. Not because people are corrupt, but because a math decision made decades ago created a system where every participant — insurers, PBMs, brokers, hospitals, pharmacies — gets richer when costs go up.I identify three distinct "pricing diseases" in healthcare:Percentage Parasitism — when compensation scales with cost, not workChargemaster Fiction — fake list prices with negotiated discounts off fictional numbersOpacity Arbitrage — profiting from the inability of other parties to see the real priceWe're only treating one of them. And I make the case that the AI industry has already solved this problem with per-unit token pricing — they just don't know they solved it for healthcare too.Watch the full video: https://youtu.be/px1eRptDHegFull sources and the deep dive: danmccoymd.substack.com

Everyone's pitching AI as the solution to prior authorization. And they're right — the technology is about to solve it. Ambient scribes capturing every detail. Clinical decision support guiding every order. Automated systems submitting perfectly optimized requests. Approval rates heading toward the high 90s.But here's what nobody's talking about: what happens to healthcare costs when a system designed around 15-20% of requests getting denied suddenly starts approving almost everything?In this episode, I break down the three distinct layers of AI in prior authorization — and why most people are lumping them together when they have very different implications. I dig into a UCSF study showing physicians using AI scribes saw a 5.8% RVU increase with no rise in claim denials. I explain why over 80% of appealed denials get overturned, but only 12% are even appealed — revealing that prior auth was never really about clinical evaluation.And I make the case that once AI solves the coding problem, the question shifts from "did you code this correctly?" to "should you have ordered this at all?"The end game isn't faster paperwork. It's AI evaluating medical judgment. The bar is going up, not down.Full source list and research citations available on Substack.

What if the way we measure a doctor's productivity is completely wrong?Software companies have already abandoned "lines of code" as a productivity metric — they now budget in tokens, the fundamental unit of AI work. Some developers spend $10,000-20,000 a month on AI agents. Microsoft says 30% of its code is AI-written. The old measurements are dead.Meanwhile, healthcare is still stuck on RVUs — a system where physicians spend two hours on EHR documentation for every one hour with patients, family doctors lose 86 minutes every night to "pajama time" charting, and Medicare physician payment has declined 26% since 2001 after adjusting for inflation. Value-based care was supposed to fix this. It didn't. CMS's own innovation center actually increased federal spending by $5.4 billion between 2011 and 2020.In this episode, I lay out the case for replacing RVUs with token-based measurement — shifting the question from "How many patients did you see?" to "How much agentic activity did you perform to improve population health?" I walk through the data from JAMA, McKinsey, MedPAC, and the AMA's own admission that MIPS is broken, and explain why AI-augmented healthcare systems should receive better reimbursement, not worse.This isn't a theoretical framework. The AMA just added 26 new CPT codes for clinical AI solutions. CMS launched the ACCESS Model in February 2026. The transition is already underway — the only question is whether your organization is measuring what matters.Full sources and the deep dive: danmccoymd.substack.comWant a personal walk through, check out our AI consultancy at RocketTools.io.

The Pentagon just labeled Anthropic — the company behind Claude AI — a national security supply chain risk. Not because they're a foreign adversary. Because they refused to remove two guardrails: no mass surveillance of Americans and no autonomous weapons without human oversight.The $200 million contract is canceled. The Trump administration ordered every federal agency and defense contractor to phase out Anthropic's technology. Anthropic is preparing to sue.But the Pentagon fight is the opening act. The real story is what AI can already do with your health data — and why the rules protecting it don't work anymore.In this episode:How AI re-identifies "anonymous" medical records for 800,000+ Americans even after removing all 18 HIPAA-required identifiersWhy your body is becoming a biometric database — facial reconstruction from MRI scans (83-98% accuracy), chest X-rays as fingerprints, and 12 million voice biomarkers extracted per minute of a telehealth callDario Amodei's four warnings about government AI misuseThe HIPAA timeline: written in 1996, last major update in 2003, new rules expected late 2026 — none of which address AI re-identificationWhy Fitbit, Apple Watch, Oura, and AI health chatbots aren't covered by HIPAA at allWhat the Anthropic situation tells us about who's drawing the line on your health data (spoiler: almost nobody)The rules haven't caught up to the technology. This episode breaks down exactly where the gap is and why it matters.Full sources and the deep dive: danmccoymd.substack.com

The common story about healthcare cybersecurity is that big hospital systems are the targets. The reality is more complicated. Rural America is ground zero — and the math is brutal.The Change Healthcare attack knocked out 50% of all U.S. medical claims processing. 80% of physician practices lost revenue. 300 hospitals didn't even apply for federal relief — mostly small and rural. But that was the supply chain breaking. The direct attacks on rural hospitals are worse.In this episode, I break down three things:First — what the Change Healthcare attack actually revealed about how fragile small-provider healthcare really is.Second — why rural hospitals are easier targets with the same valuable data. 69% lack basic multi-factor authentication. Most have one or two people handling all of IT — cybersecurity, printers, wifi, everything. And attackers know rural hospitals are more likely to pay ransoms because they can't afford to go offline when the nearest alternative is an hour away.Third — the AI double-edged sword. Only 29% of healthcare executives feel prepared for AI-powered attacks. But AI might also be the thing that levels the playing field for small providers who will never be able to hire a security team.Full sources and the deep dive: danmccoymd.substack.com/

Gyde just raised $60 million to build the first AI-native insurance brokerage. Not a tool for brokers — a replacement for the brokerage model itself. Led by Lightspeed, backed by Optum Ventures, founded by a 10-year Oscar Health veteran.In this episode, I break down three things:First — what Gyde actually is and why this isn't another SaaS platform. They're acquiring agencies and rebuilding them with AI from the inside out.Second — the 80-90% automation thesis. Most of what benefits brokers do is pattern execution, not judgment. Analytics, renewals, compliance, repricing — AI handles all of it. The 10-20% that remains is where the real value lives.Third — who survives. Three groups are forming: the acquirees, the resisters, and the adapters. WTW just paid $1.3 billion for Newfront explicitly for "agentic AI capabilities." The market is telling you what it values.The US benefits consulting market is projected to more than double to $10.5 billion by 2035. The question isn't whether AI disrupts this space. It's whether you're building or being bought.Full sources and the deep dive: danmccoymd.substack.com/