GPT-4o: What’s All The Fuss About?

By MIKE MAGEE

If you follow my weekly commentary on HealthCommentary.org or THCB, you may have noticed over the past 6 months that I appear to be obsessed with mAI, or Artificial Intelligence intrusion into the health sector space.

So today, let me share a secret. My deep dive has been part of a long preparation for a lecture (“AI Meets Medicine”) I will deliver this Friday, May 17, at 2:30 PM in Hartford, CT. If you are in the area, it is open to the public. You can register to attend HERE.

This image is one of 80 slides I will cover over the 90 minute presentation on a topic that is massive, revolutionary, transformational and complex. It is also a moving target, as illustrated in the final row above which I added this morning.

The addition was forced by Mira Murati, OpenAI’s chief technology officer, who announced from a perch in San Francisco yesterday that, “We are looking at the future of the interaction between ourselves and machines.”

The new application, designed for both computers and smart phones, is GPT-4o. Unlike prior members of the GPT family, which distinguished themselves by their self-learning generative capabilities and an insatiable thirst for data, this new application is not so much focused on the search space, but instead creates a “personal assistant” that is speedy and conversant in text, audio and image (“multimodal”).

OpenAI says this is “a step towards much more natural human-computer interaction,” and is capable of responding to your inquiry “with an average 320 millisecond (delay) which is similar to a human response time.” And they are fast to reinforce that this is just the beginning, stating on their website this morning “With GPT-4o, we trained a single new model end-to-end across text, vision, and audio, meaning that all inputs and outputs are processed by the same neural network. Because GPT-4o is our first model combining all of these modalities, we are still just scratching the surface of exploring what the model can do and its limitations.”

It is useful to remind that this whole AI movement, in Medicine and every other sector, is about language. And as experts in language remind us, “Language and speech in the academic world are complex fields that go beyond paleoanthropology and primatology,” requiring a working knowledge of “Phonetics, Anatomy, Acoustics and Human Development, Syntax, Lexicon, Gesture, Phonological Representations, Syllabic Organization, Speech Perception, and Neuromuscular Control.”

The notion of instantaneous, multimodal communication with machines has seemingly come of nowhere but is actually the product of nearly a century of imaginative, creative and disciplined discovery by information technologists and human speech experts, who have only recently fully converged with each other. As paleolithic archeologist, Paul Pettit, PhD, puts it, “There is now a great deal of support for the notion that symbolic creativity was part of our cognitive repertoire as we began dispersing from Africa.” That is to say, “Your multimodal computer imagery is part of a conversation begun a long time ago in ancient rock drawings.”

Throughout history, language has been a species accelerant, a secret power that has allowed us to dominate and rise quickly (for better or worse) to the position of “masters of the universe.”  The shorthand: We humans have moved “From babble to concordance to inclusivity…”

GPT-4o is just the latest advance, but is notable not because it emphasizes the capacity for “self-learning” which the New York Times correctly bannered as “Exciting and Scary,” but because it is focused on speed and efficiency in the effort to now compete on even playing field with human to human language. As OpenAI states, “GPT-4o is 2x faster, half the price, and has 5x higher (traffic) rate limits compared to GPT-4.”

Practicality and usability are the words I’d chose. In the companies words, “Today, GPT-4o is much better than any existing model at understanding and discussing the images you share. For example, you can now take a picture of a menu in a different language and talk to GPT-4o to translate it, learn about the food’s history and significance, and get recommendations.”

In my lecture, I will cover a great deal of ground, as I attempt to provide historic context, relevant nomenclature and definitions of new terms, and the great potential (both good and bad) for applications in health care. As many others have said, “It’s complicated!”

But as this yesterday’s announcing in San Francisco makes clear, the human-machine interface has blurred significantly. Or as Mira Murati put it, “You want to have the experience we’re having — where we can have this very natural dialogue.”

Mike Magee MD is a Medical Historian and regular contributor to THCB. He is the author of CODE BLUE: Inside the Medical Industrial Complex (Grove/2020)

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Chakri Toleti, Care.ai

Chakri Toleti is an occasional Bollywood film producer (you can Google that) and also the CEO of Care.ai–one of the leading companies using sensors and AI to figure out what is going on in that hospital room. They’ve grown very fast in recent years, fundamentally by using technology to monitor patients and help improve their care, improve patient safety and figure out what else is needed to improve the care process. You’ll also see me doing a little bit of self-testing!–Matthew Holt

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It’s the Bureaucrats, Stupid

By KIM BELLARD

Universities are having a hard time lately. They’re beset with protests the like of which we’ve not seen since the Vietnam War days, with animated crowds, sit-ins, violent clashes with police or counter protesters, even storming of administration buildings. Classes and commencements have been cancelled. Presidents of some leading universities seemed unable to clearly denounce antisemitism or calls for genocide when asked to do so in Congressional hearings. Protesters walked out on Jerry Seinfeld’s commencement speech; for heaven’s sake – who walks out on Jerry Seinfeld?

Derek Thompson wrote a great piece for The Atlantic that tries to pinpoint the source problem: No One Knows What Universities Are For. The sub-title sums up his thesis: “Bureaucratic bloat has siphoned power away from instructors and researchers.”  As I was nodding along with most of his points, I found myself also thinking: he might as well be talking about healthcare.

Mr. Thompson starts by citing a satirical piece in The Washington Post, in which Gary Smith, an economics professor at Pomona College, argues that, based on historical trends in the growth of administration staff, the college would be best served by gradually eliminating faculty and even students. The college’s endowment could then be used just to pay the administrators.

“And just like that,” Professor Smith says, “the college would be rid of two nuisances at once. Administrators could do what administrators do — hold meetings, codify rules, debate policy, give and attend workshops, and organize social events — without having to deal with whiny students and grumpy professors.”

It’s humorous, and yet it’s not.

The growth in universities’ administrative staff is widespread. Mr. Thompson acknowledges: “As the modern college has become more complex and multifarious, there are simply more jobs to do.” But that’s not always helping universities’ missions. Political scientist Benjamin Ginsberg, who published The Fall of the Faculty: The Rise of the All-Administrative University and Why It Matters in 2014, told Mr. Thompson: “I often ask myself, What do these people actually do? I think they spend much of their day living in an alternate universe called Meeting World.”

Similarly, Professor Smith told Mr., Thompson it’s all about empire building; as Mr. Thompson describes it: “Administrators are emotionally and financially rewarded if they can hire more people beneath them, and those administrators, in time, will want to increase their own status by hiring more people underneath them. Before long, a human pyramid of bureaucrats has formed to take on jobs of dubious utility.”

All of these administrators add to the well-known problem of runaway college tuition inflation, but a more pernicious problem Mr. Thompson points to is that “it siphons power away from instructors and researchers at institutions that are—theoretically—dedicated to instruction and research.”

The result, Mr. Thompson concludes is “goal ambiguity.” Gabriel Rossman, a sociologist at UCLA, told him: “The modern university now has so many different jobs to do that it can be hard to tell what its priorities are.”  Mr. Thompson worries: “Any institution that finds itself promoting a thousand priorities at once may find it difficult to promote any one of them effectively. In a crisis, goal ambiguity may look like fecklessness or hypocrisy.”

So it is with healthcare.

Anyone who follows healthcare has seen some version of the chart that shows the growth in the number of administrators versus the number of physicians over the last 50 years; the former has skyrocketed, the latter has plodded along. One can – and I have in other forums – quibble over who is being counted as “administrators” in these charts, but the undeniable fact is that there are a huge number of people working in healthcare whose job isn’t, you know, to help patients.

It’s well documented that the U.S. healthcare system is by far the world’s most expensive healthcare system, and that we have, again by far, the highest percent spent on administrative expenses. Just as all the college administrators helps keep driving up college tuition, so do all those healthcare administrators keep healthcare spending high.

But, as Mr. Thompson worries about with universities, the bigger problem in healthcare is goal ambiguity.

All those people are all doing something that someone finds useful but not necessarily doing things that directly related to what we tend to think is supposed to be healthcare’s mission, i.e., helping people with their health.  

Think about the hospitals suing patients. Think health insurers denying claims or making doctors/patients jump through predetermination hoops.  Think about the “non-profits” who not only have high margins but also get far greater tax breaks than they spend on charity care. Think about healthcare “junk fees” (e.g., facility fees). Think about all the people in healthcare making over a million dollars annually. Think about pharmaceutical companies who keep U.S. drug prices artificially high, just because they can.

As TV’s Don Ohlmeyer once said in a different context: “The answer to all of your questions is: Money.”

Healthcare is full of lofty mission statements and inspiring visions, but it is also too full of people whose jobs don’t directly connect to those and, in fact, may conflict with them. That leads to goal ambiguity.

Mr. Thompson concluded his article:

Complex organizations need to do a lot of different jobs to appease their various stakeholders, and they need to hire people to do those jobs. But there is a value to institutional focus…The ultimate problem isn’t just that too many administrators can make college expensive. It’s that too many administrative functions can make college institutionally incoherent.

Accordingly, I’d argue that the problem in healthcare isn’t that it has too many administrators per se, but that the cumulative total of all those administrators has resulted in healthcare becoming institutionally incoherent.

Famed Chicago columnist Mike Royko once offered a solution to Chicago’s budget problems. “It’s simple,” he said. “You ask city employees what they do. If they say something like “I catch criminals” or “I fight fires,” them you keep. If they say something like “I coordinate…” or “I’m the liaison…”, them you fire.”

Healthcare should have that kind of institutional focus, and that focus should be around patients and their health, not around money.

Twenty years ago Gerry Anderson, Uwe Reinhardt, and colleagues posited “It’s the Prices, Stupid” when it came to what distinguished the U.S. healthcare system, but now I’m thinking perhaps it’s the administrators.

Kim is a former emarketing exec at a major Blues plan, editor of the late & lamented Tincture.io, and now regular THCB contributor

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Want to get rich in health care? Ditch the startup and run a hospital

By MATTHEW HOLT

Given that I ran a health technology conference for many years, I tend to run in a circle of people who have some ambition to get rich in health care. After all, billions of dollars of VC money have been dropped in lots of startups over the last decade, and a few prime examples have done very well. For example Jeff Tangey of Doximity, Glen Tullman of Livongo,  Chaim Indig of Phressia and many others did fine when their companies IPOed in the late 2010s. But the truth is that many, many more have either started a health tech business that didn’t make it, or were foot soldiers in others that died along the way (Olive, Babylon, Pear, etc, etc). Which has been leading me lately to thinking about whether that’s the right approach to take if you want to make money in health care. Hint: it’s not.

There’s still tremendously little transparency about which health care organizations have what amount of money and what people earn. There is though one sector that by law has to publish information about revenue, profits, investments and executive compensation. That is the non-profit hospital/health system sector. Nonprofits are required to file Form 990 with the IRS that has that information and more on it. Having said that, most hospitals are frequently late in filing them, and file them in a very confusing way. The wonderful journalism organization ProPublica maintains a database of all 990 filings and it’s instructive to look around in it.

Some health systems make it relatively easy. UPMC, the huge western PA conglomerate files one 990 for the whole group. Others, not so much. I know that Providence, the huge west coast system, has overall revenue of $28bn but only because Fierce Healthcare told me. Had I tried to piece that together from its 990s, I’d have started with its Washington filing ($6bn), moved on to its Oregon filing (~$5bn) and then started getting confused..

Let’s say you wanted to easily figure out Advocate, the system that was the merger of the huge midwestern system with Atrium, the North Carolina-based one. Good luck. You can find Advocate but Atrium’s seems to be missing. Ditto for Carolinas Health, its previous name. There is a page calling itself Financial Information on the Atrium website, but it doesn’t have any, and tells you to go to a website set up for municipal bondholders. In fact I couldn’t find any evidence of the IRS auditing any large system, or fining them for non-compliance in filing.

The good news is that last year the North Carolina State Employees plan, i.e. a pissed off purchaser, dug into all the N. Carolina hospital systems and found out that Atrium’s CEO pay went up nearly five-fold over six years. But even the state had real trouble finding out the truth:

“It is important to understand that these figures are significant underestimates for three reasons. First, a legal loophole denies the public the right to see how much publicly owned hospitals reported paying their top executives on their tax filings. This failure of oversight hides the tax filings of more than three in 10 nonprofit hospitals in North Carolina, including Atrium and UNC Health. UNC Health did not answer a public records request for executive compensation data until February 13, 2023, two days before this report’s publication and almost three months after its receipt of the request. UNC Health’s system wide data is therefore not included in this report.” 

So the very top dogs are doing well. At UPMC it turns out that seven made more than $3m including the CEO Jeff Romoff –the same one who forgot on 60 Minutes whether he made $6m or $7m. Turns out he didn’t have to remember that number for long as by 2021 he was making $12m.

But the munificence is spreading down the executive ladder. To demonstrate, let me introduce you to Tracey Beiriger Esq. There’s almost no information about Tracey on Linkedin or anywhere else on Google other than it appears he or she is an IP lawyer at UPMC. So why do I bring them up?

Because in 2021–the last year for which UPMC filed a 990 –Tracey was the 118th highest paid executive at UPMC and had the misfortune to only make $499,446.

Which means that 117 executives working at UPMC made more than $500,000. It’s a little tricky figuring out the similar numbers at Providence because of the multiple 990s in 2021 but there are 38 in Washington (not including CEO Rod Hochman who made $9m in 2020 and then vanished from the 2021 990!), 18 in Oregon and another 21 in Southern California. So call it 80+.

I bring this up because $500,000 is a pretty decent individual income. When I asked ChatGPT it estimated about 1.2 million Americans earned that much or more. Given the workforce is 167m, that puts those several hundred hospital execs way into the top 1%.

Now I have no objection to people earning good money. I’m sure they have all worked very hard for it. But if you look at these organizations, they do not seem to be spreading the wealth very far. 

Last year UPMC was accused by unions of suppressing staff wages. There is yet to be an outcome from that complaint to the DOJ, but last week there was one from a formal class action complaint about Providence shortchanging employees by rounding down their pay to the nearest half-hour, even though they were clocking on and off by the minute. Providence was fined $200m which probably isn’t much split between 33,000 employees but at least indicates that their senior management acts just like any other aggressive business in terms of cutting costs on the backs of their employees. And it’s not just their employees. They also just got fined $137m for aggressively suing patients.

Which leads me to two final points.

The first is, is it more likely you’ll make that $500K+ in a hospital system or in a tech startup? Blake Madden at Hospitology has been tracking systems that have more than $1bn in revenue. He’s found 113 so far. Second bottom of the list is Atlanticare in NJ, which has 16 execs making more than $500K.  Which by my wild guess means that the average system has about 50 employees making $500k+  which rounds up to something like 5,000 hospital execs making at least $500K and many of them are making a whole lot more. 

Compare that to a successful health tech startup that actually makes it. Take Phreesia, a VC-backed start-up that went public in 2019 having started way back in 2007. (I know the year because CEO Chaim Indig launched at Health 2.0 in 2008. He was nice enough to let me buy some stock at the IPO and I made a few bucks). Chaim made $300K the year it went public and as CEO of a public company that’s bounced around at being worth between $1Bn and $4Bn, he made $750K last year. No one else made more than $500K. Now yes, he owned 4% of the company at the IPO and got awarded more stock. He is doing very well, but the point is that there were dozens of companies launching at Health 2.0 in 2008 and the vast majority don’t get close to an IPO or making any money for the founders, let alone the staff. 

My conclusion is, it’s not a rational bet to go the health tech route if instead you can find a regional hospital chain and brown-nose your way up into the exec ranks!

The second point is more fundamental. Remember UPMC and its 117 execs making $500K+? What would a comparable government agency be paying out? I looked at the state of California salaries.There look to be about 50 state employees making more than $500k a year, almost all working for the state investment fund CALPERS. But the top paying one only makes $1.6m a year. I’m not saying that CALPERS should be paying out that much even if it is competing with Wall Street, after all members of the Senate only make $205,000 a year and the state could just put the whole pension into an S&P index fund. But what I am saying is that we should be thinking about paying our big non-profit systems similarly to government employees because they essentially are government employees.

Beckers posted UPMC’s payor mix last year. I highly suspect you’ll find something similar at almost every big system. 

• Medicare 48%
• Medicaid 17%
• UPMC as Insurer 11%–(60% of whom are Medicaid/Medicare patients)
• Commercial, Self Pay, Other 24%

More than 70% of the money comes from the government, and the rest from the suckers who have to buy their insurance on the “open market”–which includes those buying via the ACA exchange, receiving government subsidies, and government employees.

So while these huge systems act like Fortune 100 companies and reward their executives accordingly, almost all the money comes from the taxpayer.

I wish I could say we are getting good value for it.

And yes, I didn’t even mention the for-profits and the big insurers, but that will have to wait for another day….

Matthew Holt is the founder & publisher of THCB

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You Bet Your Life

By KIM BELLARD

America is crazy about gambling. Once you had to gamble illegally with a bookie, or go to Atlantic City or Las Vegas; now 45 states – plus the District of Columbia, Puerto Rico, and the U.S. Virgin Islands – have state lotteries. Since the Supreme Court struck down PASPA, the federal ban on sports betting, 38 states – plus the D.C. and Puerto Rico – offer legal sports betting. I didn’t think we could get any crazier, until I saw last week that arcade chain Dave & Busters was going to allow betting on some of its games.

Honestly, healthcare may be the only industry upon which you can’t bet, and I’m beginning to think that’s too bad.

Dave & Busters are working with Lucra Sports, a “white-label gamification” technology company. “We’re thrilled to work with Lucra to bring this exciting new gaming platform to our customers,” said Simon Murray, SVP of Entertainment and Attractions at Dave and Buster’s. “This new partnership gives our loyalty members real-time, unrivaled gaming experiences, and reinforces our commitment to continuing to elevate our customer experience through innovative, cutting-edge technology.”

“Friendly competition really is a big fuel for our economy, whether you’re playing golf on Sunday with your buddies, or you’re going to play pickleball or video games or even cornhole at a tailgate. There’s so many ways that you can compete with friends and family, and I think gamifying that and digitizing all this offline stuff that’s happening is a massive opportunity,” Lucra CEO Dylan Robbins told CNN.

The companies are careful not to describe what they’re doing as gambling; they avoid terms like “bet” or “wager.” Michael Madding, Lucra’s chief operating officer, told The New York Times that the focus was on “skills-based” games, such as Skee-Ball or shooting baskets: i.e., “recreational activities for which the outcome is largely or entirely dependent on the knowledge, ability, strength, speed, endurance, intelligence of the participants and is subject to the control of those participants.”

This falls into a category I had never heard of: “social betting.” With social betting, there is no third party setting the odds, and more head-to-head competition with people you know. You’re not betting against the house; you’re challenging your friends. It is estimated by gaming research firm Eilers & Krejcik to be a $6b market, and its proponents argue that it is not subject to licenses & regulations that other gambling does.

Not everyone agrees. Marc Edelman, a law professor and the director of sports ethics at Baruch College in New York, told NYT:

If two people are competing against one another in Skee-Ball, presuming that there is nothing unusual done in the Skee-Ball game and physical skill is actually going to determine the winner, there is no problem. If I am taking a bet on whether someone else will win a Skee-Ball game, or whether someone else will achieve a particular score in Skee-Ball, if I myself am not engaged in a physical competition, that very likely would be seen as gambling.

Brett Abarbanel, executive director of the University of Nevada, Las Vegas, International Gaming Institute, went further, telling CNBC: “regardless of the legal classification of the activity as ‘not gambling’ vs. ‘gambling,’ this is an activity in which participants are risking something of value on an outcome that is uncertain. Therefore, there should be consumer protection measures in place for players, particularly when the target audience is skewed toward younger participants.”

Both Illinois and Ohio gambling authorities have already expressed concerns; Illinois State Rep. Daniel Didech, chairman of the Illinois House Gaming Committee,, told CNBC: “It is inappropriate for family-friendly arcades to facilitate unregulated gambling on their premises. These businesses simply do not have the ability to oversee gambling activity in a safe and responsible manner.”

There are also numerous “social sportsbooks,” including Flitt, PrizePicks, and Underdog Fantasy, that are blurring the line between online sports gambling and social betting, between fantasy leagues and plain old gambling. And they do it with users as young as 13 and with little or no state oversight. Keith Whyte, executive director of the National Council on Problem Gambling, told The Washington Post: “What a lot of these social gaming — social casinos, social sportsbooks — have found is that the regulators … either don’t feel like they have the jurisdiction or the time or energy to go after every single app that springs up.” 

Whether we like it or not, people are going to bet. “People will place a bet on ‘Will we have rainfall?’, or ‘How much snow will a certain place get?’, or ‘What will be the first day of snowfall?’” sports policy expert John Holden, JD/PhD, associate professor at Oklahoma State University, told Fox 5 NY last year.

So why shouldn’t they bet on health care?

Let’s face it: we all already bet on health care.

We bet that the doctor we pick is well trained, competent, and of the highest ethical standards. We bet that the hospital we go to won’t kill us or make us worse. We bet that the prescriptions we take do far more good for us than they harm us. We bet on all these things, spending trillions of dollars, even though we know the odds are against us: in aggregate, Americans are getting sicker and dying younger.  That’s those other people, we tell ourselves; my doctor/hospital is the “best.”

What makes healthcare different from other areas that one might bet on is the paucity of data. I always remember a colleague told me years ago: “I can know more about the performance of every MLB player than I can about any physician.” And that was before legal sports betting.

If we were to bet on health care – either our own (social betting) or others’ (online gambling) – there’d be more data. We’d insist on it. We’d analyze it. We’d use it. It’d get better and more detailed over time. And, I daresay, healthcare would become better for it.

Personally, I don’t like to gamble. I don’t buy lottery tickets. I don’t go to casinos. I don’t even bet on the Super Bowl or March Madness. So I’m tired of gambling so much on healthcare without knowing more about the risks/rewards, without the data I need and should have. If betting is the only way to ensure the data, then I say: let’s roll the dice.

Maybe Lucra could develop a gamification platform for us to bet with our doctors and hospitals.

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Will AI Revolutionize Surgical Care? Yes, But Maybe Not How You Think

By MIKE MAGEE

If you talk to consultants about AI in Medicine, it’s full speed ahead. GenAI assistants, “upskilling” the work force, reshaping customer service, new roles supported by reallocation of budgets, and always with one eye on “the dark side.”

But one area that has been relatively silent is surgery. What’s happening there? In June, 2023, the American College of Surgeons (ACS) weighed in with a report that largely stated the obvious. They wrote, “The daily barrage of news stories about artificial intelligence (AI) shows that this disruptive technology is here to stay and on the verge of revolutionizing surgical care.”

Their summary self-analysis was cautious, stating: “By highlighting tools, monitoring operations, and sending alerts, AI-based surgical systems can map out an approach to each patient’s surgical needs and guide and streamline surgical procedures. AI is particularly effective in laparoscopic and robotic surgery, where a video screen can display information or guidance from AI during the operation.”

The automatic emergency C-Section in Prometheus–Coming, but not quite yet!

So the ACS is not anticipating an invasion of robots. In many ways, this is understandable. The operating theater does not reward hyperbole or flash performances. In an environment where risk is palpable, and simple tremors at the wrong time, and in the wrong place, can be deadly, surgical players are well-rehearsed and trained to remain calm, conservative, and alert members of the “surgical team.”

Johnson & Johnson’s AI surgery arm, MedTech, brands surgeons as “high-performance athletes” who are continuous trainers and learners…but also time-constrained “busy surgeons.” The heads of their AI business unit say that they want “to make healthcare smarter, less invasive, more personalized and more connected.” As a business unit, they decided to focus heavily of surgical education. “By combining a wealth of data stemming from surgical procedures and increasingly sophisticated AI technologies, we can transform the experience of patients, doctors and hospitals alike. . . When we use AI, it is always with a purpose.”

The surgical suite is no stranger to technology. Over the past few decades, lasers, laparoscopic equipment, microscopes, embedded imaging, all manner of alarms and alerts, and stretcher-side robotic work stations have become commonplace. It’s not like mAI is ACS’s first tech rodeo.

Mass General surgeon, Jennifer Eckoff, MD,  sees the movement in broad strokes. “Not surprisingly, the technology’s biggest impact has been in the diagnostic specialties, such as radiology, pathology, and dermatology.” University of Kentucky surgeon, Danielle Walsh MD also chose to look at other departments. “AI is not intended to replace radiologists. – it is there to help them find a needle in a haystack.” But make no mistake, surgeons are aware that change is on the way. University of Minnesota surgeon, Christopher Tignanelli, MD’s, view is the future is now. He says, “AI will analyze surgeries as they’re being done and potentially provide decision support to surgeons as they’re operating.”

AI robotics as a challenger to their surgical roles, most believe, is pure science fiction. But as a companion and team member, most see the role of AI increasing, and increasing rapidly in the O.R. The greater the complexity, the more the need. As Mass General’s Eckoff says, “Simultaneously processing vast amounts of multimodal data, particularly imaging data, and incorporating diverse surgical expertise will be the number one benefit that AI brings to medicine. . . Based on its review of millions of surgical videos, AI has the ability to anticipate the next 15 to 30 seconds of an operation and provide additional oversight during the surgery.”

As the powerful profit center for most hospitals, dollars are likely to keep up with visioning as long as the “dark side of AI” is kept at bay.  That includes “guidelines and guardrails” as outlined by new, rapidly forming elite academic AI collaboratives, like the Coalition for Health AI. Quality control, acceptance of liability and personal responsibility, patient confidence and trust, are all prerequisite. But the rewards, in the form of diagnostics, real-time safety feedback, precision and tremor-less technique, speed and efficient execution, and improved outcomes likely will more than make up for the investment in time, training, and dollars.

Mike Magee MD is a Medical Historian and regular contributor to THCB. He is the author of CODE BLUE: Inside the Medical Industrial Complex (Grove/2020)

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Is getting people off weight loss medications the right move?

By RICHARD FRANK

Demand for GLP-1 medications soared last year and shows no signs of stopping in 2024. Employers and health plans are understandably anxious about how long they should expect to pay for these pricey drugs. They’re itching for an easy off-ramp.

Some solutions are cropping up to pave the way. Many of them claim they can help patients reap the benefits of GLP-1s within a short time frame, and get them off the drugs within 12 months to save costs. But the data doesn’t support that promise. In fact, studies suggest some patients may need to stay on the drugs indefinitely to sustain outcomes while other patients may be able to discontinue the drugs and at least maintain their cardiometabolic risk reduction even if they cannot maintain all of their weight loss. 

A better strategy to control costs is to more accurately pinpoint those who really need the drugs—and keep those who don’t off of them from the start. Of course, there will be times when deprescribing is appropriate, and we need to clinically support patients through that process. But one-size-fits-all solutions centered on medication as a silver bullet to obesity are only setting up patients and payers for failure. Similarly, those whose sole promise is to deprescribe, don’t follow the evidence.

Prescribing GLP-1s with the goal to deprescribe is foolhardy

GLP-1s treat obesity, but they don’t cure it. GLP-1 agonists increase the body’s own insulin production and slow the movement of food from the stomach to the small intestine. The drugs help people eat less by curbing cravings and boosting satiety. Studies show that once people go off semaglutide, the cravings come back in full force—and so does much of the weight.

While GLP-1 medications produce nearly miraculous outcomes in some people, they’re no quick fix. Obesity is a complex chronic disease. Drugs alone can’t solve for genetic predisposition, behaviors, mental and emotional components, social determinants of health, and other compounding elements that contribute to obesity. In the right circumstances, drugs can give people a solid leg up in better managing those contributing factors—but they’re not for everyone.

Keto is not a sustainable replacement for GLP-1s

Highly restrictive diets like the keto diet aren’t for everyone either. Keto requires a drastic reduction in carbohydrate intake, which can be difficult to maintain long-term. Not to mention, the high-fat content of keto diets can also lead to other health issues and isn’t conducive to tapering off of GLP-1 medications. Side effects from the drugs can make a high-fat diet difficult to tolerate.

It’s good to be wary of solutions that promise an off-ramp by way of highly restrictive diets. While a keto diet may help people lose weight in the short term, studies show that weight loss is rarely sustained over the long run and may be detrimental to overhaul health. The diet is associated with many complications that often lead to hospital admissions for dehydration, electrolyte disturbances, and hypoglycemia.

Triage the right care to the right people at the right time

Obesity’s complex nature requires a personalized approach to treatment that delivers the right care to the right people at the right time. That takes a whole care team of specialized providers—like registered dietitians, health coaches, and prescribing physicians to help people at various stages of the disease. And since obesity often occurs alongside other cardiometabolic conditions like hypertension, diabetes, COPD, and more, patients need the help of specialists who understand how those different conditions interact.

Behavioral interventions that focus on eating patterns, sleep hygiene, and exercise routines can be highly effective for many people. Studies show that people who participate in behavioral weight loss programs for over 12 sessions, lose approximately 5-10% of their body weight. That might not seem like a lot, but just 5% percent of weight loss is associated with healthier biomarkers. If the goal is better health—and not just quick cosmetic fixes—behavioral interventions can work really well.

Others may need to supplement behavior change with proven weight loss drugs like Contrave or Topomax that have been around for decades. These will work for the vast majority of patients who need help losing weight. About 10-20% of a population may need even more intensive drugs like GLP-1 medications, but they’re the exception, not the rule.

Optimize results for those already on the drugs

Significant side effects impede the progress of many people on GLP-1s. In order to see the best results from the drugs, people need wraparound support from expert providers. Registered dietitians can help strategize ideal eating times and nutrition-dense food that patients tolerate well. In fact, the FDA only approves the use of GLP-1 medication when prescribed in combination with calorie restriction and behavior change.

If we’re investing in these costly, though life-changing, treatments, we should ensure their success with medical nutrition therapy and other personalized care.

Maximize success when deprescribing

No matter how much support is given, there will be some people who simply can’t tolerate the drugs or choose to go off them for a variety of reasons. Deprescribing may also be necessary when a patient has a change in their health circumstances: a pregnancy, major surgery, or other condition where going off the drug is advisable in their short or long-term care plan. But we shouldn’t forcefully encourage patients dependent on GLP-1s to go off of them simply to save costs. That’s not how ethical medicine is practiced.

We need to set patients up for as much success as possible even when deprescribing is necessary. Highly restrictive diets aren’t likely to work for the majority of people who go off GLP-1s. They’ll need more sustainable approaches to maintaining a calorie deficit and managing behaviors around eating, including emotional aspects, while still getting adequate nutrition. Supporting patients with dietitian-led medical nutrition therapy and health coaches can help ensure patients get the best nutrition and care as they manage this transition.

Richard Frank, MD, MHSA is the Chief Medical Officer of Vida Health

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Nvidia’s AI Bot Outperforms Nurses: Here’s What It Means for You

By ROBBIE PEARL

Soon after Apple released the original iPhone, my father, an unlikely early adopter, purchased one. His plan? “I’ll keep it in the trunk for emergencies,” he told me. He couldn’t foresee that this device would eventually replace maps, radar detectors, traffic reports on AM radio, CD players, and even coin-operated parking meters—not to mention the entire taxi industry.

His was a typical response to revolutionary technology. We view innovations through the lens of what already exists, fitting the new into the familiar context of the old.

Generative AI is on a similar trajectory.

As I planned the release of my new book in early April, “ChatGPT, MD: How AI-Empowered Patients & Doctors Can Take Back Control of American Medicine,” I delved into the promise and perils of generative AI in medicine. Initially, I feared my optimism about AI’s potential might be too ambitious. I envisioned tools like ChatGPT transforming into hubs of medical expertise within five years. However, by the time the book hit the shelves, it was clear that these changes were unfolding even more quickly than I had anticipated.

Three weeks before “ChatGPT, MD” became number one on Amazon’s “Best New Books” list,  Nvidia stunned the tech and healthcare industries with a flurry of headline-grabbing announcements at its 2024 GTC AI conference. Most notably, Nvidia announced a collaboration with Hippocratic AI to develop generative AI “agents,” purported to outperform human nurses in various tasks at a significantly lower cost.

According to company-released data, the AI bots are 16% better than nurses at identifying a medication’s impact on lab values; 24% more accurate detecting toxic dosages of over-the-counter drugs, and 43% better at identifying condition-specific negative interactions from OTC meds. All that at $9 an hour compared to the $39.05 median hourly pay for U.S. nurses.

Although I don’t believe this technology will replace dedicated, skilled, and empathetic RNs, it will assist and support their work by identifying when problems unexpectedly arise. And for patients at home who today can’t obtain information, expertise and assistance for medical concerns, these AI nurse-bots will help. Although not yet available, they will be designed to make new diagnoses, manage chronic disease, and give patients a detailed but clear explanation of clinician’ advice.

These rapid developments suggest we are on the cusp of technology revolution, one that could reach global ubiquity far faster than the iPhone. Here are three major implications for patients and medical practitioners:  

1. GenAI In Healthcare Is Coming Faster Than You Can Imagine

The human brain can easily predict the rate of arithmetic growth (whereby numbers increase at a constant rate: 1, 2, 3, 4). And it does reasonably well at comprehending geometric growth (a pattern that increases at a constant ratio: 1, 3, 9, 27), as well.

But even the most astute minds struggle to grasp the implications of continuous, exponential growth. And that’s what we’re witnessing with generative AI.

Imagine, for example, a pond with just one lily pad. Assuming the number of lilies will double every night, then the entire pond will be covered in just 50 days. Yet, on day 43, you would barely notice the green plants with only 1% of the pond’s surface covered. It seems almost impossible to imagine that just seven days later, the lily pads will completely obscure the water.  

Experts project that AI’s computational progress will double roughly every year, if not faster. But even with conservative projections, ChatGPT and similar AI tools are poised to be 32 times more powerful in five years and over 1,000 times more powerful in a decade. That’s equivalent to your bicycle traveling as fast as a car and then, shortly after, a rocket ship.

This rate of advancement proves challenging for both healthcare providers and patients to comprehend, but it means that now is the time to prepare for what’s coming.

2. GenAI Will Be Different Than Past AI Models

When assessing the transformative potential of generative AI in healthcare, it’s crucial not to let past failures, such as IBM’s Watson, cloud our expectations. IBM set out ambitious goals for Watson, hoping it would revolutionize healthcare by assisting with diagnoses, treatment planning, and interpreting complex medical data for cancer patients. 

I was highly skeptical at the time, not because of the technology itself, but because Watson relied on data from electronic medical records, which lack the accuracy needed to make reliable “narrow AI” diagnoses and recommendations.

In contrast, generative AI leverages a broader and more useful array of information sources. It not only pulls from published, peer-reviewed medical journals and textbooks but also will be able to integrate real-time information from global health databases, ongoing clinical trials, and medical conferences. It will soon incorporate continuous feedback loops from actual patient outcomes and clinician input. This extensive data integration will allow generative AI to continuously stay at the forefront of medical knowledge, making it fundamentally different from its predecessors.

That said, generative AI will require a couple more generations before it can be widely used without direct clinician oversight. But Nvidia’s bold entry into healthcare signals a long-overdue willingness among tech companies to navigate the legal and regulatory hurdles of healthcare. Once an AI clinician chatbot is available, multiple other companies will quickly follow.

3. GenAI In Healthcare Will Be Ubiquitous (Hospital, Office And Home)

Just as my father never imagined that his iPhone (stored in his trunk) would evolve into an essential tool for navigating life, many Americans struggle to envision the transformative impact generative AI will have on healthcare.

The concept of accessing medical advice and expertise continuously—affordably, reliably, and conveniently around the clock—represents such a departure from current healthcare models that it’s easy for our minds to dismiss it as far-fetched. Yet it’s becoming increasingly clear that these capabilities are not just possible, but likely.

Daily, I receive feedback from both clinicians and patients who have interacted with current generative AI tools. Nearly all report that the responses, particularly when prompted effectively, align closely with clinician recommendations. This is a testament to the evolving accuracy and reliability of generative AI in healthcare settings, and it promises a revolution in medical care delivery in the near future.

A decade from now, we will look back at today’s skepticism in much the same way I think about my dad’s initial underestimation of his iPhone. We are on the cusp of a major shift, where generative AI will become as integral to healthcare as smartphones have become to daily life. The only question is whether clinicians will lead the way or cede that opportunity to others.

Robert Pearl MD is former CEO of The Permanente Medical Group, writes the “Monthly Musings newsletter and hosts two podcasts Fixing Healthcare and Medicine The Truth. His latest book is ChatGPT, MD: How AI-Empowered Patients & Doctors Can Take Back Control of American Medicine

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What Walmart said & What Walmart Did: Not the same thing

Walmart surprised us all and changed its mind about primary care yesterday. It’s out.

Because so few people have seen it I want to show what Walmart‘s head of health care said just 18 months ago (Nov 2022). Today they are finally killing off the 6th different strategy they’ve had (maybe it was 4). I guess (unlike CVS & Walgreens) they don’t have to write down investment in Oak Street or VillageCare, but they never worked out that primary care is only profitable if it’s 1) very low overhead 2) a loss leader for more expensive services (as most hospitals run it) or 3) getting a cut of the $$ for stopping more expensive services (Oak Street, Chenmed, Kaiser).

At HLTH 18 months ago I interviewed Cheryl Pegus who was then running Walmart and I asked why anyone should trust them, given how often they changed. Sachin H. Jain, MD, MBA Jain answered for her and said, “because they have Cheryl!” — Cheryl then said, “at Walmart the commitment to delivering health care is bigger than anywhere I have ever worked”. “Right now I have 35 centers in 3 years I’ll have 100s”  see 11.00 onwards in the video below, although the whole thing is worth a look

Cheryl though left Walmart THE NEXT WEEK!

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What’s behind all these assessments of digital health?

By MATTHEW HOLT

A decent amount of time in recent weeks has been spent hashing out the conflict over data. Who can access it? Who can use it for what? What do the new AI tools and analytics capabilities allow us to do? Of course the idea is that this is all about using data to improve patient care. Anyone who is anybody, from John Halamka at the Mayo Clinic down to the two guys with a dog in a garage building clinical workflows on ChatGPT, thinks they can improve the patient experience and improve outcomes at lower cost using AI.

But if we look at the recent changes to patient care, especially those brought on by digital health companies founded over the past decade and a half, the answer isn’t so clear. Several of those companies, whether they are trying to reinvent primary care (Oak, Iora, One Medical) or change the nature of diabetes care (Livongo, Vida, Virta et al) have now had decent numbers of users, and their impact is starting to be assessed. 

There’s becoming a cottage industry of organizations looking at these interventions. Of course the companies concerned have their own studies, In some cases, several years worth. Their  logic always goes something like “XY% of patients used our solution, most of them like it, and after they use it hospital admissions and ER visits go down, and clinical metrics get better”. But organizations like the Validation Institute, ICER, RAND and more recently the Peterson Health Technology Institute, have declared themselves neutral arbiters, and started conducting studies or meta-analyses of their own. (FD: I was for a brief period on the advisory board of the Validation Institute). In general the answers are that digital health solutions ain’t all they’re cracked up to be.

There is of course a longer history here. Since the 1970s policy wonks have been trying to figure out if new technologies in health care were cost effective. The discipline is called health technology assessment and even has its own journal and society, at a meeting of which in 1996 I gave a keynote about the impact of the internet on health care. I finished my talk by telling them that the internet would have little impact on health care and was mostly used for downloading clips of color videos and that I was going to show them one. I think the audience was relieved when I pulled up a video of Alan Shearer scoring for England against the Netherlands in Euro 96 rather than certain other videos the Internet was used for then (and now)!

But the point is that, particularly in the US, assessment of the cost effectiveness of new tech in health care has been a sideline. So much so that when the Congressional Office of Technology Assessment was closed by Gingrich’s Republicans in 1995, barely anyone noticed. In general, we’ve done clinical trials that were supposed to show if drugs worked, but we have never really  bothered figuring out if they worked any better than drugs we already had, or if they were worth the vast increase in costs that tended to come with them. That doesn’t seem to be stopping Ozempic making Denmark rich.

Likewise, new surgical procedures get introduced and trialed long before anyone figures out if systematically we should be doing them or not. My favorite tale here is of general surgeon Eddie Jo Riddick who discovered some French surgeons doing laparoscopic gallbladder removal in the 1980s, and imported it to the US. He traveled around the country charging a pretty penny to  teach other surgeons how to do it (and how to bill more for it than the standard open surgery technique). It’s not like there was some big NIH funded study behind this. Instead an entrepreneurial surgeon changed an entire very common procedure in under five years. The end of the story was that Riddick made so much money teaching surgeons how to do the “lap chole” that he retired and became a country & western singer.

Similarly in his very entertaining video, Eric Bricker points out that we do more than double the amount of imaging than is common in European countries. Back in 2008 Shannon Brownlee spent a good bit of her great book Overtreated explaining how the rate of imaging skyrocketed while there was no improvement in our diagnosis or outcomes rates. Shannon by the way declared defeat and also got out of health care, although she’s a potter not a country singer.

You can look at virtually any aspect of health care and find ineffective uses of technology that don’t appear to be cost effective, and yet they are widespread and paid for.

So why are the knives out for digital health specifically?

And they are out. ICER helped kill the digital therapeutics movement by declaring several solutions for opiod use disorder ineffective, and letting several health plans use that as an excuse to not pay for them. Now Peterson, which is using a framework from ICER, has basically said the same thing about diabetes solutions and is moving on to MSK, with presumably more categories to be debunked on deck.

One of the more colorful players in this whole arena is Al Lewis, who is the worst type of true believer–a convert. Back in the 1990s Al Lewis was the head cheerleader for something called Disease Management, which was kind of like “digital health 0.5”. In the mid-2000s CMS put a bunch of these disease management programs into a study called Medicare Health Support. The unpleasant answer was that disease management didn’t work and cost more than it saved. Much of the problem was that these programs were largely phone-based and not integrated with the physician care the patients were receiving. Meanwhile Al Lewis (I’m using his full name so you don’t think Al is AI!) has since taken his analytical sword to disease management, prevention and wellness programs, and now several digital health companies, proving that many of them don’t save the money they claim. He does this usually in a very funny way, along with lots of $100k bets which he never pays out on (and never wins either)!

Which leads me to another skeptical player coming at this from a slightly different angle. Brian Dolan, in his excellent Exits & Outcomes newsletter, pointed out that there was something rather strange about the Peterson study. Dolan noted that Peterson picked one study about Livongo about A1c reduction (not the one it did itself which was well critiqued by Al Lewis) and extrapolated the clinical impact from that one study as being the same for all the companies’ solutions–even though Livongo had previously done very few studies compared to say Omada Health.

Peterson then pulled a different random study from the literature to extrapolate the financial impact of that A1c reduction. What it didn’t do is pull the claims data from patients actually using these solutions, even though Peterson’s advisory board is a who’s who list of health insurers. So of course we could get better real world data, but why bother when we can effectively guess and extrapolate? Also worth a mention that many of those insurers, including Aetna & United have competitive diabetes products too. 

So you might think that the very well-funded Peterson Institute could or should have done rather more, and certainly might have included some of the solutions being marketed by the health insurers on its advisory board too.

This is not to say that the digital health companies have done great studies. Like everyone else in health care, their reporting and studies are all over the map and plenty of them make claims that are pushing the limits, clearly because they have commercial reasons to do so.

But it’s also true that many haven’t needed those studies to commercially grow. The poster child here is Livongo, which grew its number of employer clients and members from nothing in 2015 to over 500 employers and 350,000 patients by the time it went public in 2019–all while publishing only one study right at the end of the period. The reason for that growth was that Livongo cost the same as what the employer was already paying for diabetes strips (which it included as a loss leader), it lined up favorable business arrangements with Mercer and CVS to get to employers, and in general the patients liked it. Al Lewis doesn’t agree with that last part (pointing to a few bad Amazon reviews), but Peterson actually noted lots of positive user reviews of the diabetes solutions on its “patient perspective” section–which had no impact on its overall negative evaluation.

My assessment is that, while the individual health service researchers at Peterson et al mean well, we are witnessing another power struggle. The current incumbents have done things one way. Several of these new digital health approaches are providing new more continuous and more comprehensive patient care approaches–which some patients seem to like. Of course the incumbent providers and insurers could have tried these approaches over the decades. It’s not as if we had data that showed everything was hunky dory over the last 40 years. But America’s hospitals, doctors and insurers did what they always did, and continued to get rich. 

Now there’s a new set of tech-enabled players and there’s a choice that potentially could be made. Should we move to a system with comprehensive, constant monitoring of chronically ill patients, and see how we can improve that? Or should we let the incumbents determine the pace of that change? I think we all know the incumbents’ answer, and to me that puts all these analyses of digital health in perspective.

After all, would those incumbents be happy with similar levels of rigor being assessed of their current activities?

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