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=B6MeSTiQTGId8388

Full 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-advertising

Watch 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. another
• How hospital systems use "must-have" leverage to inflate entire network contracts
• What narrow and tiered networks actually save (and what they cost in access)
• The real math behind reference-based pricing
• Why most employers have never seen the exposed rates they're paying

This 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 explanation
• The inverted suicide crisis alerts that fired for sadness but went silent when patients described specific plans for self-harm
• The sycophancy problem: why ChatGPT is 12x more likely to agree when you downplay your own symptoms
• Where ChatGPT actually performs well (93% in semi-urgent cases) — and why that makes the failures harder to spot
• What this means for anyone using, building, or recommending AI health tools

Sources & Links:

Primary Study — Nature Medicine, Feb 2026
https://doi.org/10.1038/s41591-026-04297-7

Mt. Sinai Press Release
https://www.mountsinai.org/about/newsroom/2026/research-identifies-blind-spots-in-ai-medical-triage

Forbes: "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-warn

Related: AI Chatbots and Medical Misinformation — Communications Medicine, 2025
https://doi.org/10.1038/s43856-025-01021-3

Full 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 work
• Chargemaster Fiction — fake list prices with negotiated discounts off fictional numbers
• Opacity Arbitrage — profiting from the inability of other parties to see the real price

We'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/px1eRptDHeg

Full sources and the deep dive: danmccoymd.substack.com