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Work Requirements Target the Wrong People

By KIM BELLARD

One of the key “cost savings” in last years’ Big, Beautiful Bill were work requirements imposed on most working aged beneficiaries in SNAP and/or Medicaid, despite the fact that the few times such requirements had been tried showed they were costly to implement and operate and don’t do much to increase work participation, although they are effective at getting beneficiaries to lose benefits. Republicans who pushed these requirements were infuriated at the thought that some able-bodied people – stereotypically young men – were sitting around on their couches playing video games while benefiting from the programs, despite those programs’ complex administrative burdens and meagre benefits.

A new GAO report reminds us that the people coasting off SNAP and Medicaid were not so much the beneficiaries but rather employers, especially large employers. And the names of the most likely employers won’t come as a big surprise.

The report — Federal Social Safety Net Programs: Millions of Workers, Including Many Employed by Large Employers, Continue to Rely on Medicaid and SNAP – was requested by Senator Bernie Sanders, in his role as Ranking Member of the Senate Committee on Health, Education, Labor, and Pensions, and is a follow-up to a similar 2020 report. It focused on 11 states: Arkansas, Georgia, Indiana, Maine, Massachusetts, Nebraska, North Carolina, Oklahoma, Rhode Island, Tennessee, and Washington.

The top-lines are that working aged beneficiaries in both programs were, in fact, not only likely to already be working—mostly full-time — but also at participation rates higher than working aged people not on the programs, and that companies like Amazon and Walmart were among the largest employers of these beneficiaries.

The key change in employment in these populations has been the explosion of gig workers in the app-based food delivery and ride sharing sectors. Workers at Amazon on these programs also tripled since the prior report. Walmart remains the employer with the largest number of these workers who receive Medicaid, but has slipped to second to ride sharing gig workers receiving SNAP. McDonalds and Dollar General round out the top five employers.  

Now, these are among the largest employers generally, but, gosh, doesn’t it gall you that have so many of their workers who still need SNAP and/or Medicaid?  It’s not like they’re not making money, it’s not that their CEOs and other executives aren’t raking in tens of millions of dollars, but they sure are reluctant to pay federal income taxes. The Wall Street Journal reported earlier this year that, as a result of The Big, Beautiful Bill, Amazon’s federal income taxes dropped from $9b to $1.2b in 2025, while profits soared 44.5% to $90b. Walmart looks like a sucker for paying $6b in 2025, an effective tax rate of about 23%.

An Amazon spokesperson defended its practices in a response to The Washington Post: “Amazon pay is among the best in the industry, regular full-time employees have access to health care from their first day … and 74% of our regular full-time employees are enrolled in an Amazon health insurance plan, well above the 65% private sector take-up rate for full-time workers.”

So, if all those employees have access to coverage from their first day and get among the best pay in the industry, why are any on SNAP or Medicaid?  And couldn’t you at least pay more than 1.3% on federal taxes?

Bernie, of course, was outraged:

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The Limits of Disclosure and the Power of the Outside Option: A Case for External Reference Pricing in Healthcare 

By DAVID INTROCASO & ADAM CUNNINGHAM

For over a decade, federal healthcare policy has operated on a foundational premise: if hospital prices are made visible, market discipline will inevitably follow. The push for price transparency—exemplified by federal disclosure rules and current legislative proposals such as the Lower Costs, More Transparency Act (H.R. 9393) and the Patients Deserve Price Tags Act (S. 2355)—aims to empower buyers and stimulate price competition. Yet, despite terabytes of disclosed price files, commercial hospital prices continue to escalate far out of proportion to underlying costs or quality improvements. The persistent failure of price transparency is not merely a problem of enforcement or compliance; it is a structural defect in market design. In highly concentrated hospital markets, publishing prices does not create market discipline because it leaves price-setting power entirely in the hands of the seller. To restrain further premium price growth, healthcare buyers or moreover ERISA plans must go beyond price transparency and restore or regain bargaining leverage.  

The Illusions of Price Transparency and Internal Benchmarks

The inarguable limitation of hospital price transparency is that disclosure cannot alter the underlying power dynamics of a concentrated market. Hospitals are the price setters, plans the price takers. In approximately three out of four metropolitan statistical areas—and up to 97 percent of urban hospital markets—provider consolidation has erased competition. Commercial insurers and self-funded health plans cannot drop dominant, “must-have” hospital systems from their networks without breaching regulatory network-adequacy requirements. Knowing they cannot be excluded, consolidated health systems set prices based on bargaining leverage rather than operational cost. As a result, posting a chargemaster price or a negotiated rate publicly merely certifies what a captive buyer was forced to pay; it does not give the buyer the power to walk away.

Furthermore, recent empirical analyses demonstrate that disclosed price data remains functionally unworkable for market discipline. Hospital disclosure files are rife with noncompliance—full compliance has dropped to roughly one in five hospitals—and the posted figures lack a standardized unit of payment. Hospital contracts mix fixed dollar amounts, per diems, case rates, and percentage discounts off unlisted chargemasters, rendering the data noisy and incomparable.

When policymakers attempt to correct these market failures using internal benchmarks, the results routinely backfire:

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Matthew reviews ChatGPT Health

OpenAI just made ChatGPT Health generally available. This is their partnership with B.Well which allows you to bring your data from various EMRs into chatGPT. So I took it for a spin–Matthew Holt

Healthcare Has Confused Disclosure With Understanding

By JOE FEGHALI

Healthcare has become very good at producing disclosures. It is much less good at producing understanding.

A hospital posts a price file. A health plan publishes negotiated rates. A provider gives an estimate. A patient portal contains a document somewhere. A consent form is signed. The box is checked.

But the patient may still not understand what they are being asked to approve, what the likely cost pathway looks like, or what happens when treatment changes.

This is the quiet failure of healthcare transparency. We have spent years trying to make prices more visible. That was necessary. But visibility is not the same as usability. A price that exists somewhere is not the same as a patient understanding the financial commitment they are making before care begins.

Price transparency matters. It is just not enough.

The next frontier is not whether healthcare can disclose more numbers. It is whether healthcare can explain what those numbers actually mean.

The price is rarely the product

Most consumer markets understand the difference between a price and a purchase. A flight price means one thing if it includes luggage and another if it does not. A construction quote means one thing if it includes materials, labor, permits, and cleanup, and another if each of those becomes an add-on.

Healthcare often asks patients to make decisions with less clarity than they would expect in much simpler markets.

The deeper problem is not only that prices are hidden. Sometimes the prices are visible. The problem is that the object being priced is unclear.

A patient does not experience healthcare as a billing code or a machine-readable file. A patient experiences healthcare as a journey: consultation, diagnosis, imaging, procedure, medication, facility involvement, follow-up, revision, complication, recovery, and sometimes a second opinion when the first pathway becomes confusing.

Yet transparency policy often focuses on isolated prices rather than the care pathway the patient is actually buying into.

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The Drug Changing Medicine

By EMANUEL SARKEES

The United States is in the middle of what researchers are calling one of the most significant pharmacological developments in modern medicine. GLP-1 receptor agonists, the drug class behind Ozempic and Wegovy, have become able to reduce obesity, lower cardiovascular risk, control blood sugar, and show signals improving areas like addiction and dementia. It is backed by large scale clinical trials and is actively reshaping how physicians think about chronic disease management. The accessibility, however, is not keeping up with the science. The people who will benefit most from GLP-1s are the same people who have no path to receiving them.

This is not a coincidence. It is the outcome of a pricing structure, an insurance system, and a policy environment that have consistently made the same choice, which is to let cost determine who receives care.

What GLP-1s Actually Do

GLP-1s were originally developed to treat Type 2 diabetes. They work by acting similarly to a hormone the gut naturally produces after eating, which signals the brain to reduce appetite, slows digestion, and stabilizes blood sugar levels. The effects have been significant enough that the medical community’s interest has expanded well beyond diabetes management.

A 2023 clinical trial, known as the SELECT trial, found that semaglutide, the active ingredient in Wegovy, reduced the risk of cardiovascular issues like heart attacks and strokes by 20% in patients with obesity who did not have diabetes. That finding alone drew serious attention from cardiologists. Early research is also exploring GLP-1s in alcohol use disorder, sleep apnea, chronic kidney disease, and Alzheimer’s. The amount of potential application is unlike anything seen from a single drug class in recent times.

The Price Problem

The issue is not whether these drugs work. It is whether the system is built to let people use them. Wegovy, the FDA approved version specifically made for weight management, holds a price of around $1,300 per month in the United States without insurance coverage. Ozempic, technically approved for Type 2 diabetes but widely prescribed off label for obesity, runs at a similar cost. As one analysis noted, GLP-1 pricing “increased 442% between 2021 and 2023, creating a market three times larger than cancer spending, with list prices reaching $1,400”. Novo Nordisk and Eli Lilly, the two companies that dominate this market, charge American patients prices that are dramatically higher than what patients pay for the same medications in other countries.

The actual price a patient pays has very little to do with that $1,349 list price and almost everything to do with how they access the drug. With commercial insurance that covers obesity, costs can fall to around $25 a month, though that requires a plan that actually covers weight loss medications and prior authorization that actually gets approved. The Wegovy pill is available directly through NovoCare at $149 per month, the lowest price point ever for an FDA-approved GLP-1 weight loss medication. Zepbound starts at $299 per month through LillyDirect for patients who pay out of pocket. The government’s TrumpRx platform offers GLP-1s at around $350 per month for cash-paying patients who do not have coverage. Compounded versions through telehealth platforms like Ro and Hers run anywhere from $99 to $349 per month, though the FDA is actively restricting that market. And as of July 1, 2026, eligible Medicare beneficiaries can access Wegovy, Zepbound, and Foundayo at a flat $50 per month through the new Medicare GLP-1 Bridge program. That is a 27-times spread between the lowest and highest price for the exact same drug, determined not by what the medicine costs to make, but almost entirely by who you are when you walk up to the pharmacy counter. And for the uninsured, low income patient with no Medicare, no qualifying commercial plan, and no compounding option, none of those prices are on the table at all. 

At $1,300 per month, the annual cost of Wegovy exceeds $15,000. For millions of Americans, that number is not just high, but is completely out of reach. That gap in pricing is not accidental. In countries where governments negotiate drug prices directly, the same medication costs a fraction of what Americans pay. The United States remains one of the only developed nations that does not regulate pharmaceutical pricing at the federal level, and patients suffer the difference.

Insurance coverage for GLP-1s follows a path that is both frustrating and familiar.

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The price of Ozempic is a meaningless phrase

By JOHN SAMARAS

Ask what Ozempic costs. The honest answer runs from $25 a month to $1,100 a month, and every number in that range is real, published, and defensible. A phrase that covers a forty-four-fold spread is not a price. It is a fog, and patients make four-figure annual decisions inside it.

I run GLP Chart, an independent GLP-1 price index. The index shows that “the price of Ozempic” fails as a concept for three stacked reasons. The molecule sells in five forms under four names. Each form sells through different channels at different prices. And the advertised price rarely survives to month four.

One molecule, five forms

Ozempic is semaglutide, branded for type 2 diabetes. The same molecule is Wegovy when approved for weight loss, sold as a weekly pen and, since 2026, a daily pill. It is Rybelsus in the older oral form. And 503A compounding pharmacies still sell it as compounded semaglutide where the rules allow, though the FDA’s compounding restrictions and the manufacturer lawsuits thinned that market through 2025 and 2026.

When someone says “Ozempic price,” they almost always mean “what will semaglutide cost me.” Those are different questions with different answers.

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Value Is in the Eye of the Beholder

By KIM BELLARD

The most (unintentionally) amusing story I read this week was Tim Higgin’s Wall Street Journal article Alex Karp Is Saying What Every Angry CEO Is Thinking About AI. Dr. Karp (yes, he has a Ph.D.), co-founder and CEO of Palantir Technologies, is upset about how AI companies are using relationships with their business customers to harvest data and business insights from those customers. “Something has gone completely wrong,” he fumed.

Now, this is Palantir, mind you; it may not have invented surveillance capitalism but it might have perfected it. It has become essential to government and large corporations across the world. Most of us are aware of how tech companies like Meta or Google give us “free” services that exist primarily to collect more data on us, which they then use to target ads to us, but Palantir’s data collection and analysis operate at a level we often don’t recognize.  But make no mistake; it is using our data, and not necessarily in our best interests.

Mr. Higgins quotes former White House AI czar David Sacks in support of Dr. Karp’s concerns:

Anthropic has launched Claude Science, Claude Security, Claude Legal, and of course Claude Code—each expanding into categories previously served by companies building on top of their models. The pattern is consistent: Watch where value is being created, then move in directly. Dominate the model layer, then use that position to capture the most lucrative verticals.

So it is delicious irony that Dr. Karp and others are finding themselves at the wrong end of the power inequality with their data.

I find myself thinking about healthcare when I think above this new wave of data collectors/ synthesizers. It seems pretty clear that the AI companies aren’t going anywhere, and are expected to reshape most industries, including healthcare. Lots has been written about AI’s use in healthcare, including by me. It is both inevitable and, in many cases, desirable. Now this issue of AI’s insatiable appetite for data makes me wonder if we’re looking at things wrong.

I’ve worked in healthcare for longer than I care to admit, and at no point did people not complain that healthcare in general, and health insurance in particular, was too expensive. And yet, costs have kept rising. We’re closing in on $6 trillion in U.S. healthcare expenditures. No matter what kind of health insurance you have – large employer, small employer, ACA Marketplace, Medicare Advantage, even Medicare Supplements for traditional Medicare – your premiums (and/or out-of-pocket costs) are likely going up at rates we haven’t seen in years.

Two well known facts about rising costs are, one, that it is not so much we’re using too many services as it is that Americans pay way higher prices for healthcare than in most countries, and, two, that a relatively small percentage of people account for the vast majority of healthcare spending. The latter has an insidious effect on health insurance premiums, as people with fewer expenses are less likely to have or keep health insurance, making premiums for the remaining people higher. Nobody wants to pay for the people who use a lot of health care, but they want other people to help pay if they end up being one of those people. It’s a conundrum.

Now, optimists hope that AI can do a better job of identifying all the wasted, unnecessary, or inappropriate care we use – estimated as much as one-third – and help make administration more efficient; current levels are estimated as 15-30% of spending. Good goals, both of them, and it is entirely plausible that AI can help with both. But it would still remain that sick people are the “problem” with our health care spending and health insurance premiums, and I want to propose a different way of looking at them.

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The wrong people are scared of clinical AI

By CRAIG HAUBEN

Ask anyone outside healthcare who resists clinical AI and you’ll get a confident answer. The older doctors. The ones who spent thirty years building expertise and now see a machine coming for it. The story writes itself, which should have been the first clue it was wrong.

I’ve spent thirty years in healthcare, and I now run a company that builds and runs AI inside provider and payer organizations. At Clutch we use AI’s data analysis to solve engagement challenges. Who is the patient today? What message will land with them? When do they want to read it? Get those right and you can drive the kind of sustained behavior change that moves clinical outcomes like drug adherence, care plan adherence, and gap closure.

So I’m not working from theory. I watch this land in real workflows, and here’s what I see. The clinicians most enthusiastic about AI are usually the ones who’ve done the job the longest. The resistance comes from somewhere else. If you run a health system, that difference should change how you plan your next deployment.

Start with the adoption numbers, because they already break the resistance story. The AMA’s latest survey found four in five physicians now use AI in practice, up from 38 percent in 2023. That’s not a profession digging in against a threat. That’s a profession that found something useful.

Now the veterans. A doctor with three decades in a specialty can see, better than anyone, what these systems are good at. Pattern recognition at scale. Catching the thing that should have been flagged two visits ago. Surfacing what was already sitting in the data: the missed finding in last year’s imaging, the lab trend across eighteen months that looked unremarkable one value at a time, the three ED visits in six weeks nobody had the time to connect.

This isn’t hypothetical. The Nature study of Google’s breast cancer screening system showed a 9.4 percent drop in false negatives for US patients, the cancers human readers missed. The largest NHS evaluation to date, across 175,000 women, found AI caught more invasive cancers with fewer false positives than human readers. The harm these systems go after, information that existed and never got connected, is one experienced clinicians know cold. They’ve spent careers watching its absence hurt people.

Here’s one from our own work. We’re working with a national government programs payer on some of their hardest members to engage, the high intensity ones who need contact four or five times a day for six months or more. We got engagement to 95 percent, measured by the customer, and adherence to 93 percent. The result was a 0.8 average drop in HbA1c and an 18 percent reduction in symptoms.

When a system takes the mechanical load off so the judgment work gets more attention, the thirty-year clinician doesn’t feel threatened. They feel relieved. Their expertise is the judgment, not the data retrieval, and they’ve always known the difference.

Now look at where the fear actually lives. It comes from the middle.

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Future of AI and Telehealth in primary care — Panel discussion

A couple of months back I hosted a conversation about my favorite topics, primary care and how telehealth and AI are changing it. The panel was Timm Schneider — Co-Founder & COO, Third Way Health, Jamie Reddick — COO, Graybill Medical Group & Erin Parks, Ph.D. — Co-Founder, Equip. The panel was at a tech heavy conference called TechCon Global in San Diego. Sadly the weather stopped us getting Matt Siegler from Akido Labs. We got into it about tech. incentives, specialty care and the role of AI in access and patient support–Matthew Holt

How Online Prescription Services Are Closing the Access Gap for Patients Who Skip the Doctor’s Office

By DAMIAN WONJO

Every clinician keeps a private list of the patients they don’t see. Not the ones who cancel, the ones who never book. The shift worker who reschedules the same appointment three times and then quietly gives up. The parent who can’t justify half a day off and a waiting-room afternoon for a routine refill. The patient whose condition carries enough stigma that the friction of a face-to-face visit becomes, in itself, a reason to do nothing. These people don’t show up in no-show statistics. They show up later, as complications, as emergencies, as avoidable deterioration.

This is the access gap, and from where I sit as a physician in Europe, it has less to do with the raw supply of doctors than with the cost of reaching one, measured not only in money but in time, distance, and effort. When that cost climbs higher than a patient’s tolerance, care simply doesn’t happen. Online prescription services and remote consultation won’t fix every part of this problem. But they target precisely the variable that most reforms ignore, which is friction.

The patients who fall through

It is tempting to frame telemedicine as a convenience for the already-healthy and already-connected. In practice, the people who benefit most are often those with the least slack in their lives. A patient stabilised on the same antihypertensive for three years does not need a fresh diagnostic odyssey to continue it, they need a prescription before they run out. A working adult who recognises a recurring, familiar problem does not always need a physical examination to be helped safely. Forcing every such encounter through the narrow door of an in-person appointment does not raise the standard of care, it raises the rate at which people abandon it.

Continuity is where this matters most. Chronic conditions are managed in refills, and a missed refill is not a clerical event but a gap in treatment with real physiological consequences. Lowering the barrier to that refill is not a luxury. It is, quietly, one of the highest-yield interventions available.

What changed in Europe

The pandemic did not invent telemedicine, but it normalised it, and it accelerated the infrastructure underneath it. Poland is a useful case study. Electronic prescriptions became the national standard in 2020, and today essentially every prescription is issued digitally, retrievable by the patient through a government health account and dispensable at any pharmacy with a code. The clinical encounter and the prescription were decoupled from a single physical location without being decoupled from a licensed prescriber.

That distinction is the whole argument. A responsible online prescription service is not a vending machine. It is a licensed physician, working within the same legal and ethical framework as any clinic, using a different channel to reach the patient. The technology is mundane. The consequence, that a patient who would otherwise have gone without is now seen, is not.

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