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

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.com

Want 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 identifiers
• Why 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 call
• Dario Amodei's four warnings about government AI misuse
• The HIPAA timeline: written in 1996, last major update in 2003, new rules expected late 2026 — none of which address AI re-identification
• Why Fitbit, Apple Watch, Oura, and AI health chatbots aren't covered by HIPAA at all
• What 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/