We Freeze People, Don’t We?

By KIM BELLARD

Perhaps you’ve heard about the controversial Alabama Supreme Court ruling about in-vitro fertilization (IVF), in which the court declared that frozen embryos were people. The court stated that it has long held that “unborn children are ‘children,’” with Chief Justice Tom Parker – more on him later – opining in a concurring opinion:

Human life cannot be wrongfully destroyed without incurring the wrath of a holy God, who views the destruction of His image as an affront to Himself. Even before birth, all human beings bear the image of God, and their lives cannot be destroyed without effacing his glory.

Seriously.

Many people have already weighed in on this decision and its implications, but I couldn’t resist taking some pleasure in seeing “pro-life” advocates tying themselves in knots trying to explain why, when they legislated that life begins at conception, they didn’t mean this kind of conception and that kind of life.

John Oliver was typically on point, noting that the Alabama ruling was “wrong for a whole bunch of reasons. Mainly, if you freeze an embryo it’s fine. If you freeze a person, you have some explaining to do.”

The case in question wasn’t specifically about IVF, nor did the ruling explicitly outlaw it. It was a case about a patient who removed stored embryos and accidentally dropped them, and the couples whose embryos were destroyed wanted to hold that patient liable under the Wrongful Death of a Minor Act. The court said they could. Note, though, that neither the patient nor the clinic was being charged with murder or manslaughter…yet.

Although the Alabama Attorney General has already indicated he won’t prosecute IVF patients or clinicians, the ruling has had a chilling effect on fertility clinics in the states, with The University of Alabama at Birmingham health system and others indicating they were putting a pause on IVF treatments.

Justice Parker has long been known as something of a theocrat; as The New York Times wrote:

Since he was first elected to the nine-member court in 2004, and in his legal career before it, he has shown no reticence about expressing how his Christian beliefs have profoundly shaped his understanding of the law and his approach to it as a lawyer and judge.

His concurring opinion claimed: the state constitution had adopted a “theologically-based view of the sanctity of life.” Alabama is not alone. Kelly Baden, the vice president for public policy at the Guttmacher Institute, told BBC: “We do see that many elected officials and judges alike are often coming at this debate from a highly religious lens.”

Speaker Johnson has said:

The separation of church and state is a misnomer. People misunderstand it. Of course, it comes from a phrase that was in a letter that Jefferson wrote. It’s not in the Constitution. And what he was explaining is they did not want the government to encroach upon the church — not that they didn’t want principles of faith to have influence on our public life. It’s exactly the opposite.

And here we are.

Many Republicans are backtracking on the ruling.

Alabama Republican Governor Kay Ivey said she was “working on a solution.” Alabama legislators are already working on bills to protect IVF, clarifying that in vitro fertilization doesn’t count, with life only beginning when implanted in a uterus. Oh, OK, then.

Presumed Republican presidential nominee Donald Trump says he “strongly” supports IVF, and Republican Speaker of the House Mike Johnson said: “I believe the life of every single child has inestimable dignity and value. That is why I support IVF treatment, which has been a blessing for many moms and dads who have struggled with fertility,” Alabama Senator Tommy Tuberville somewhat hilariously managed to somehow both support the ruling and the need for IVF.

Eric Johnston, president of the Alabama Pro-Life Coalition, admitted:

It’s a win philosophically for the pro-life movement because it carries on the pro-life recognition of unborn life. But you get into a very difficult situation, where you have this medical procedure that’s accepted by most people, and then how do you deal with it? That’s the dilemma… But I think the pro-life community in general supports IVF, and I’ve known and worked with many people who have had children via IVF. And at the same time, they think abortion is wrong. This issue is so different from abortion, but it has to do with life.

The trouble is, red states are scrambling all over themselves passing ever-more restrictive abortion laws, with the “life begins at conception” mantra, and, despite what Speaker Johnson and other House Republicans say now, 125 of them have cosponsored the Life at Conception Act that makes no exception for IVF.

Gosh, who could have guessed IVF would be impacted by all this?  Well, anyone who thought about it for a half second.

Although IVF only accounts for about 2% of births, it has been around for decades. An untold number of embryos are routinely stored (frozen) and, in some cases, destroyed. Now people like Republican Governor Greg Abbott would have us believe IVF is taking us all by surprise:

These are very complex issues where I’m not sure everybody has really thought about what all the potential problems are and as a result, no one really knows what the potential answers are. And I think you’re going to see states across the country come together grappling with these issues and coming up with solutions.

Once a fetus or an embryo is a person, what rights do they have, when do they qualify for tax credits/welfare/child support, and how do their rights compare to other people? As Jacob Holmes suggested in the Alabama Political Reporter: “Imagine you are in an in vitro fertilization clinic that is on fire, and you have time to save only 100 frozen embryos or a single 2-year-old child.” Do you save the most “lives,” or the only one actually breathing?

I know what I’d do.

I would be remiss if I didn’t note that Alabama has the third highest infant mortality rate in the U.S. (thank you, Arkansas and Mississippi!), and that it was one of 15 (red) states that is rejecting federal funds to help feed hungry children doing the summer (Alabama has some 500,000 such children).  

Evidently, unborn or frozen “people” matter more than live ones.

—————

These are, I admit, complex ethical issues, but trying to legislate them, especially from the standpoint of one particular religious point-of-view, is only going to lead to more outcomes like we’re seeing in Alabama. Democracy demands that we do better to listen than to tell.

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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Putting the ‘value’ in value-based payments

By JOSH SEIDMAN

Like Matthew Holt, I have also been ranting about the fact that “We’re spending way too much money on stuff that is the wrong thing.” As Matthew said, “it’s a rant, but a rant with a point!” And that’s a lot better than most rants these days. In addition to having a point, I’m also bringing a lot of data to my rant.

More specifically, we’ve known for a long time that clinical care only drives 20% (maybe less) of health outcomes, yet we continue to spend more and more on it.

We do that despite the well-documented fact that the U.S. performs worse than most OECD countries despite spending far more. I remember, in my first health care job in 1990, being blown away that the U.S. spent $719 billion on health care (or $1.395 trillion in 2022 dollars). Here we are, trillions of dollars later ($4.465 trillion) doing the same thing and expecting a different result.

After more than 30 years in health CARE, I decided that I really wanted to start doing something about HEALTH, which is why 3 years ago I joined Fountain House, the founder of the clubhouse movement, a psychosocial rehabilitation model for people with serious mental illness (SMI)—a model now replicated by 200 U.S. clubhouses and another 100+ in more than 30 countries around the world. It was actually people living with SMI that launched Fountain House in 1948, realizing long ago that addressing social drivers of health offered a new road to recovery and rehabilitation. Now 75 years later, we’re finally seeing some parts of the health care system come to terms with the necessity of addressing health-related social needs.

With decades of evidence behind us, Fountain House has spent the last year and a half building an economic model to understand clubhouses’ societal economic impact when one takes into account a wide range of costs—mental health, physical health, disability, criminal justice, and productivity or lost wages.

The net impact for the average person served by clubhouses is more than $11,000 per year—and twice that amount for someone with schizophrenia. (We also know that clubhouses have a huge impact on quality of life, agency, self-esteem, and many other important aspects associated with recovery and rehabilitation—which is personally much more important to me, just not the subject of my current rant.)

The medical costs alone are dramatic and, interestingly, it’s a fairly even balance between mental and physical costs. Importantly, for the average clubhouse member, the social costs outweigh the medical cost benefits.

U.S. clubhouses currently serve approximately 60,000 people. That’s a tiny fraction of the more than 15 million people in the U.S. living with SMI. If we could even support 5% of them with clubhouses, an extrapolation of our model suggests that would generate more than $8.5 billion per year in savings to the public, not to mention dramatically changing the life trajectories for so many people.

The broader point here is that we don’t have to make the choices we do from a societal perspective. If you compare the U.S. to other developed countries, you notice a complete flip in emphasis on social support versus clinical care.

Given that it’s unlikely that we’re going to suddenly dramatically shift the balance of resources in the U.S., we need to find new ways to encourage a greater emphasis on addressing health-related social needs. As we push toward new value-based payment models, we need to find ways to reward performance for achieving social outcomes (e.g., employment levels, educational attainment, housing stability) as well as the patient-reported outcomes (e.g., quality of life, loneliness reduction) that we know contribute greatly to recovery and rehabilitation.

Joshua Seidman, PhD, is Chief Research and Knowledge Officer for Fountain House, a national mental health nonprofit working for and alongside people with serious mental illness to support their recovery.

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Lucienne Ide, Rimidi

Lucie Ide is a physician running Rimidi, a company helping health systems manage patients with chronic conditions. They extract data from EMRs and transfer this into workflow for care teams, predominantly at ACOs and other risk bearing organizations, but also increasingly with FFS groups using RPM to manage those patients. Their current moves are to continue to extend from their first patient group (diabetes) to all types of chronic patients. We chatted about her company, but also about the wider move (or lack of it) to better manage patients in the US system–Matthew Holt

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So what can we do about health care costs?

By MATTHEW HOLT

Last week Jeff Goldsmith wrote a great article in part explaining why health care costs in the US went up so much between 1965 and 2010. He also pointed out that health care has been the same portion of GDP for more than a decade (although we haven’t had a major recession in that time other than the Covid 2020 blip when it went up to 19%). However, it’s worth remembering that we are spending 17.3% of GDP while the other main OECD countries are spending 11-12%. Now it’s true that the US has lots of social problems that show up in heath spending and also that those other countries probably spend more on social services, but it’s also clear that we don’t actually deliver a lot more in services. In fact probably the most famous health economics paper of the last 50 years was Anderson & Rienhardt’s “It’s the Prices, Stupid”, which shows we just pay more for the same things. Anyone who’s looked at the price of Ozempic in the US versus in Denmark knows that’s true.

But suspend disbelief and say we actually wanted to do something about health care costs, what would we do?

There are 4 ways to cut health care costs

1. Cut prices
2. Cut overall use of services
3. Reduce only unnecessary services
4. Replace higher priced services with lower priced ones

Number 3 or reducing only unnecessary services is the health policy wonks dream.

The Dartmouth school, originating with Jack Wennberg, has done a pretty good job convincing the health policy establishment that there is massive practice variation across the nation (and even within cities and individual hospitals), and that while this leads to higher costs, it doesn’t result in better outcomes. In fact outcomes where there are more services and spending tend to be worse. Dartmouth does have its critics like Buzz Cooper, and maybe all the explanation of variables in health care spending is caused by well meaning doctors ministering to the inner city poor, but it’s not hard to find overuse bordering on fraud. There have been a ton of well meaning attempts to both educate patients to choose wisely and to get doctors to behave better (or at least report their data), but there’s a new report out showing that Dartmouth had it roughly right every day. (This recent NYTimes one is about cutting babies’ tongues to make them breastfeed more easily).

Overall there have been some reductions in some measures, like hospital admissions but many of those have been replaced with other services, and in general practice variation has not gone away. Could it happen? Maybe, but 50 years of evidence makes it look unlikely. Don’t forget that the Obamacare authors were faithful disciples of Dartmouth but not much of that philosophy ended up in CMS policy.

Number 4 or replacing higher priced services with lower priced ones is the Silicon Valley health tech dream cross-bred with the Dartmouth school’s love of primary care. I will admit to being a fan of this movement. If we can replace higher priced people (doctors) with lower priced people or non-people (AI) we should be able to deliver the same things we are doing today at a lower cost. For example, in the field of psychotherapy there’s currently a great shortage of therapists. One thing that’s being done is replacing therapists with lower qualified coaches. But the end game is to use AI-powered chatbots and avatars to do the same thing. 

A related attempt is to deliver preventative services using technology. This is now paid for by Medicare – it’s called remote physiological monitoring (RPM). While its introduction has been a tad bumpy, it intuitively makes sense. If you can start tracking the care of relatively sick people while they are at home and relatively healthy, surely you can pick up issues before they get worse, intervene with medication changes and other services in their homes, and therefore prevent hospital admissions and improve outcomes. In fact, given how cheap tracking technology is, and the advances in AI, can’t you monitor everyone (based on their level of acuity) and give them a personal AI health coach? I call this the “continuous clinic” and it’s a great idea if I say so myself. The problem is that it’s not going to happen easily in a medical world that manages its process in terms of office visits and hospital admissions and gets paid on those metrics. We simply don’t have the right type of new organizations to put this together. And if you believe John Glaser and Sara Vaezy’s recent piece in the HBR called Why the Tech Industry Won’t Disrupt Health Care, we’re unlikely to get them. (I think John & Sara hope that the incumbents will reform themselves, but they would say that, wouldn’t they!)

Which leaves us with 1, cutting prices, and 2, reducing overall use of services. 1 & 2 are what the rest of the OECD does. 

Virtually every country in the OECD has some form of central price controls. Even if they have multiple paying entities, like Germany, there’s one agreed price schedule. Or, as in the UK and Scandinavia, there’s a regional or national budget. The US also has such a national price control, but only for some people over 65, given that Medicare Advantage now covers half of that population, and only for some services. Notably it doesn’t cover drugs, although that will slightly change in the near future given CMS’ new ability to negotiate the prices of some drugs. 

To this point in the US, any attempt to squeeze down on Medicare prices produces two effects. One is violent disagreement on behalf of provider organizations, which spend more money lobbying than basically any other industry in America. Almost always this means that Congress balks at imposing any real cuts. The other is that providers find ways to transfer those costs onto patients unable to negotiate. You’d think that the patients’ representatives (insurers and employers) would resist that but RAND has shown that they are basically price takers, paying more than double what Medicare pays for the same thing. Again this could change, and there’s some recent legislative activity that has a few people very excited, and has spurred some lawsuits about fiduciary responsibility – ironically one from an employee of a drug company. But we remain a long long way from a German/Japanese/French style price schedule.

Which leave us with 2, reducing overall use of services. The name for this in US health political  (if not policy) circles begins with another R, rationing. The stories of Canadians flooding across the border to access American health care were always basically bullshit, but like today’s stories of critical race theory, transgender drag queens corrupting our youth, and millions of migrants invading the southern border, it doesn’t take much to wind up the Fox News crowd as the Democrats found out. In 2009 the very wonky issue of when women should get mammograms became death panels very quickly. (BTW if you want to read a lot more about Canada, here’s a classic THCB piece I wrote in 2003. Not that much has changed)

This all means that the obviously and transparently reducing services, presumably by creating a UK style cost-benefit analysis commission, is unlikely to happen. We have tried outsourcing that to the private sector, particularly in Medicare Advantage. But the combination of naked greed and stupidity from the MA plans and the use of scary AI, will probably put paid to that soon enough now the trial attorneys have got hold of it.

So to summarize, we pay about double what most other countries pay in $$ terms and about 50% more as a share of our (much bigger) GDP. And of course we lead the league (still) in the number of uninsured people and those who are practically uninsured, or facing bankruptcy from medical bills. There are four ways we could fix it, but none of them seem that promising.

And I don’t see a way this changes any time soon.

Matthew Holt is the publisher of The Health Care Blog

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The 7 Decade History of ChatGPT

By MIKE MAGEE

Over the past year, the general popularization of AI orArtificial Intelligence has captured the world’s imagination. Of course, academicians often emphasize historical context. But entrepreneurs tend to agree with Thomas Jefferson who said, “I like dreams of the future better than the history of the past.”

This particular dream however is all about language, its standing and significance in human society. 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.”

Well before ChatGPT became a household phrase, there was LDT or the laryngeal descent theory. It professed that humans unique capacity for speech was the result of a voice box, or larynx, that is lower in the throat than other primates. This permitted the “throat shape, and motor control” to produce vowels that are the cornerstone of human speech. Speech – and therefore language arrival – was pegged to anatomical evolutionary changes dated at between 200,000 and 300,000 years ago.

That theory, as it turns out, had very little scientific evidence. And in 2019, a landmark study set about pushing the date of primate vocalization back to at least 3 to 5 million years ago. As scientists summarized it in three points: “First, even among primates, laryngeal descent is not uniquely human. Second, laryngeal descent is not required to produce contrasting formant patterns in vocalizations. Third, living nonhuman primates produce vocalizations with contrasting formant patterns.”

Language and speech in the academic world are complex fields that go beyond paleoanthropology and primatology. If you want to study speech science, you better have a working knowledge of “phonetics, anatomy, acoustics and human development” say the  experts. You could add to this “syntax, lexicon, gesture, phonological representations, syllabic organization, speech perception, and neuromuscular control.”

Professor Paul Pettitt, who makes a living at the University of Oxford interpreting ancient rock paintings in Africa and beyond, sees the birth of civilization in multimodal language terms. He says, “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.  Google chair, Sundar Pichai, maintains a similarly expansive view when it comes to language. In his December 6, 2023, introduction of their ground breaking LLM (large language model), Gemini (a competitor of ChatGPT), he described the new product as “our largest and most capable AI model with natural image, audio and video understanding and mathematical reasoning.”

Digital Cognitive Strategist, Mark Minevich, echoed Google’s view that the torch of human language had now gone well beyond text alone and had been passed to machines. His review: “Gemini combines data types like never before to unlock new possibilities in machine learning… Its multimodal nature builds on, yet goes far beyond, predecessors like GPT-3.5 and GPT-4 in its ability to understand our complex world dynamically.”

GPT what???

O.K. Let’s take a step back, and give us all a chance to catch-up.

What we call AI or “artificial intelligence” is a 70-year old concept that used to be called “deep learning.” This was the brain construct of University of Chicago research scientists Warren McCullough and Walter Pitts, who developed the concept of “neural nets” in 1944, modeling the theoretical machine learner after human brains, consistent of multiple overlapping transit fibers, joined at synaptic nodes which, with adequate stimulus could allow gathered information to pass on to the next fiber down the line.

On the strength of that concept, the two moved to MIT in 1952 and launched the Cognitive Science Department uniting computer scientists and neuroscientists. In the meantime, Frank Rosenblatt, a Cornell psychologist, invented the “first trainable neural network” in 1957 termed by him futuristically, the “Perceptron” which included a data input layer, a sandwich layer that could adjust information packets with “weights” and “firing thresholds”, and a third output layer to allow data that met the threshold criteria to pass down the line.

Back at MIT, the Cognitive Science Department was in the process of being hijacked in 1969 by mathematicians Marvin Minsky and Seymour Papert, and became the MIT Artificial Intelligence Laboratory. They summarily trashed Rosenblatt’s Perceptron machine believing it to be underpowered and inefficient in delivering the most basic computations. By 1980, the department was ready to deliver a “never mind,” as computing power grew and algorithms for encoding thresholds and weights at neural nodes became efficient and practical.

The computing leap, experts now agree, came “courtesy of the computer-game industry” whose “graphics processing unit” (GPU), which housed thousands of processing cores on a single chip, was effectively the neural net that McCullough and Pitts had envisioned. By 1977, Atari had developed game cartridges and microprocessor-based hardware, with a successful television interface.

With the launch of the Internet, and the commercial explosion of desk top computing, language – that is the fuel for human interactions worldwide – grew exponentially in importance. More specifically, the greatest demand was for language that could link humans to machines in a natural way.

With the explosive growth of text data, the focus initially was on Natural Language Processing (NLP), “an interdisciplinary subfield of computer science and linguistics primarily concerned with giving computers the ability to support and manipulate human language.” Training software initially used annotated or referenced texts to address or answer specific questions or tasks precisely. The usefulness and accuracy to address inquiries outside of their pre-determined training was limited and inefficiency undermined their usage.

But computing power had now advanced far beyond what Warren McCullough and Walter Pitts could have possibly imagined in 1944, while the concept of “neural nets” couldn’t be more relevant. IBM describes the modern day version this way:

“Neural networks …are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another… Artificial neural networks are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer…Once an input layer is determined, weights are assigned. These weights help determine the importance of any given variable, with larger ones contributing more significantly to the output compared to other inputs. All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it “fires” (or activates) the node, passing data to the next layer in the network… it’s worth noting that the “deep” in deep learning is just referring to the depth of layers in a neural network. A neural network that consists of more than three layers—which would be inclusive of the inputs and the output—can be considered a deep learning algorithm. A neural network that only has two or three layers is just a basic neural network.”

The bottom line is that the automated system responds to an internal logic. The computers “next choice” is determined by how well it fits in with the prior choices. And it doesn’t matter where the words or “coins” come from. Feed it data, and it will “train” itself; and by following the rules or algorithms imbedded in the middle decision layers or screens, it will “transform” the acquired knowledge, into “generated” language that both human and machine understand.

In 2016, a group of tech entrepreneurs including Elon Musk and Reed Hastings, believing AI could go astray if restricted or weaponized, formed a non-profit called OpenAI. Two years later they released a deep learning product called Chat GPT.  This solution was born out of the marriage of Natural Language Processing and Deep Learning Neural Links with a stated goal of “enabling humans to interact with machines in a more natural way.”

The GPT stood for “Generative Pre-trained Transformer.” Built into the software was the ability to “consider the context of the entire sentence when generating the next word” – a tactic known as “auto-regressive.” As a “self-supervised learning model,” GPT is able to learn by itself from ingesting or inputting huge amounts of anonymous text; transform it by passing it through a variety of intermediary weighed screens that jury the content; and allow passage (and survival) of data that is validated. The resultant output? High output language that mimics human text.

Leadership in Microsoft was impressed, and in 2019 ponied up $1 billion to jointly participate in development of the product and serve as their exclusive Cloud provider.

The first ChatGPT-1 by OpenAI was first introduced by GPT-1 in 2018, but not formally released publicly until November 30, 2022.

It was trained on an enormous BooksCorpus dataset. Its’ design included an input and output layer, with 12 successive transformer layers sandwiched in between. It was so effective in Natural Language Processing that minimal fine tuning was required on the back end.

OpenAI released version two, called GPT-2, next, which was 10 times the size of its predecessor with 1.5 billion parameters, and the capacity to translate and summarize. GPT-3 followed. It had now grown to 175 billion parameters, 100 times the size of GPT-2, and was trained by ingesting a corpus of 500 billion content sources (including those of my own book – CODE BLUE). It could now generate long passages on verbal demand, do basic math, write code, and do (what the inventors describe as) “clever tasks.” An intermediate GPT 3.5 absorbed Wikipedia entries, social media posts and news releases.

On March 14, 2023, GPT-4 went big language, now with multimodal outputs including text, speech, images, and physical interactions with the environment. This represents an exponential convergence of multiple technologies including databases, AI, Cloud Computing, 5G networks, personal Edge Computing, and more.

 The New York Times headline announced it as “Exciting and Scary.” Their technology columnist wrote, “What we see emerging are machines that know how to reason, are adept at all human languages, and are able to perceive and interact with the physical environment.” He was not alone in his concerns. The Atlantic, at about the same time, ran an editorial titled, “AI is about to make social media (much) more toxic.

Leonid Zhukov, Ph.D, director of the Boston Consulting Group’s (BCG) Global AI, believes offerings like ChatGPT-4 and Genesis have the potential to become the brains of autonomous agents—which don’t just sense but also act on their environment—in the next 3 to 5 years. This could pave the way for fully automated workflows.”

Were he alive, Leonardo da Vinci, would likely be unconcerned. Five hundred years ago, he wrote nonchalantly, “It had long come to my attention that people of accomplishment rarely sat back and let things happen to them. They went out and happened to things.”

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

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Why Not, Indeed?

By KIM BELLARD

Recently in The Washington Post, author Daniel Pink initiated a series of columns he and WaPo are calling “Why Not?” He believes “American imagination needs an imagination shot.” As he describes the plan for the columns: “In each installment, I’ll offer a single idea — bold, surprising, maybe a bit jarring — for improving our country, our organizations or our lives.”

I love it. I’m all in. I’m a “why not?” guy from way back, particularly when it comes to health care.

Mr. Pink describes three core values (in the interest of space, I’m excerpting his descriptions):

• Curiosity over certainty. The world is uncertain. Curiosity and intellectual humility are the most effective solvents for unsticking society’s gears.
• Openness over cynicism: Cynicism is easy but hollow; openness is difficult but rich.
• Conversation over conversion: The ultimate dream? That you’ll read what I’ve written and say, “Wait, I’ve got an even better idea,” and then share it.

Again, kudos. One might even say “move fast and break things,” but the bloom has come off that particular rose, so one might just say “take chances” or “think different.” Maybe even “dream big.”

Around the same time I saw Mr. Pink’s column I happened to be reading Adam Nagourney’s The Times: How the Newspaper of Record Survived Scandal, Scorn, and the Transformation of Journalism. In the early 1990’s The Times (and the rest of the world) was struggling to figure out if and how the Internet was going to change things. Mr., Nagourney reports how publisher Arthur Sulzberger (Jr) realized the impact would be profound:

One doesn’t have to be a rocket scientist to recognize that ink on wood delivered by trucks is a time consuming and expensive process.

I.e., contrary to what many people at The Times, and many of its readers, thought at the time, the newspaper wasn’t the physical object they were used to; it was the information it delivers. That may seem obvious now but was not at all then.  

Which brings me to health care. Contrary to what many people working in healthcare, and many people getting care from it, might think, healthcare is not doctors, hospitals, prescriptions, and insurance companies. Those are simply the ink on wood delivered by trucks that we’re used to, to use the metaphor.

And it doesn’t take a rocket science to recognize that what we call health care today is a time consuming and expensive process – not to mention often frustrating and ineffective.

Why not do better?

I also thought about health care when reading Mr. Nagourney’s book when he described the conflict between the journalism side of the company versus the business side: was the newspaper about the articles it published, with the advertising just there to support them, or was it really an advertising platform that needed the content the journalists created to bring eyeballs to it? In healthcare, is it about helping patients with their health, or is it a way to provide income to the people and organizations involved in their care?

I.e., is it about the mission or the margins?

If you think that’s too cynical, I’ll point to Matthew Holt’s great article in The Health Care Blog arguing that many hospitals systems are now essentially hedge funds that happen to provide some care, while also creating scads of rich executives. Or to how an actual hedge fund is buying a hospital. Or to how, indeed, private equity firms are buying up health care organizations of all types, even though many experts warn the main impact is to raise costs and adversely impact care. Or to how Medicare Advantage plans may be better at delivering insurer profits than quality care.

I could go on and on, but it seems clear to me that healthcare has lost its way, mistaking how it does things from what it is supposed to be for. If healthcare has become more about making a small number of people rich than about making a lot of people healthier, then I say let’s blow it up and start from first principles.

There’s a “Why Not?”

Mr. Holt’s “Why Not?” is to take a measly $38b from the $300b he estimates those hospitals are sitting on, and invest it in primary care, such as the Federally Qualified Health Centers (FQHCs). Primary care needs the money; the hospitals/hedge funds, not so much. Amen to that.

A couple years ago I proposed an even wilder idea: let’s give every physician $2 million – maybe even $2.5 million – annually. We say we value them, so let’s reward them accordingly. The caveat: from that they’d have to pay for all of their patients’ health care needs – referrals, prescriptions, hospital stays, etc. I posited that they’d negotiate much better deals with their compatriots than we seem to be able to do. Lots of details to be worked out, but it falls into the “Why Not?” category.

Here’s another audacious Why Not: it’s fairly well known that CEO to worker pay ratios have skyrocketed from a modest 20-1 in the 1960’s to something like 344-1 now. There’s no evidence I’ve seen that the ratios are any better in healthcare. Since no profession in healthcare is more respected and relied on than nurses, I propose – maybe making it a condition for receiving any federal funds — that no healthcare organization should have an executive compensation  to nurse compensation ratio that exceeds 20 (and I do mean compensation rather than salary, to avoid the bonus/stock shenanigans that executives have relied on). 

If that sounds low, I’d pity the executive who wants to argue with straight face that he/she is more than twenty times more important than nurses. I bet they couldn’t find many patients who’d agree, or any nurses.

———–

If you work in healthcare, you should ask yourself: is what I do the ink, the wood, or the delivery truck, or is it truly integral to what healthcare should be in 2024?  If you think your job should be more about health and less about the business of health, why not make it so?

And the rest of us should be asking ourselves: is the healthcare we get still the equivalent of a print newspaper? We don’t have to be rocket scientists to recognize that, in 2024, we should be expecting something better – cheaper, faster, more interactive, more personal, and much more impactful.

Why not, indeed?

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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Supporting innovations in cancer treatment and prevention for our nation’s most vulnerable

By KAT MCDAVITT and LESLIE KIRK

Innsena has made a $100,000 contribution to CancerX, making Innsena the public-private partnership’s first Impact Supporter.

Why? There are few conditions in which the disparity in innovations benefiting underserved communities is more apparent than in the treatment and prevention of cancer.

Patients without insurance are more likely to present with more advanced cancers, and the cancer death rate for people of color is significantly higher than for white patients. More people die from cancer in rural communities than in urban settings. 

In CancerX, we found a community of partners taking on hard problems to equitably deploy innovative solutions that can reduce the risk of, and cure cancer for all patients. Even—and especially—when financial incentives do not otherwise exist for the private sector to solve those problems.  

Innsena is committed to improving equitable access, treatment and outcomes for the most vulnerable among us. We focus on supporting improved outcomes for Medicaid members and underserved communities. The disparity caused by the absence of incentives and funding for innovators to enter the Medicaid market can’t be overstated. 

But innovators, and the investors who fund these pioneers, are exactly what our industry needs to change health outcomes in underserved communities. 

We decided that, if the incentives to innovate in cancer care for vulnerable populations don’t exist, then we would create them. Our financial commitment to CancerX is a step forward that we hope will start a broader movement. 

Our team’s $100,000 contribution will help the team at CancerX to accelerate programs underway—including its effort to improve equity and reduce financial toxicity in cancer care and research—and to more rapidly launch new initiatives. 

We’re particularly proud to support the public-private partnership’s efforts to improve equity and reduce financial toxicity. Cancer deaths are inequitably distributed across the United States—and those patients who do survive are 2.5 times more likely to declare bankruptcy than those without disease. 

Likewise, a key component of CancerX is a start-up accelerator for companies bringing more digital solutions for the treatment and prevention of cancer, with special attention given to organizations that focus on disadvantaged populations. We’re honored to support the start-ups selected for the first CancerX accelerator cohort with both mentorship and financial support. 

And to that end, as individuals, we’ve gone one step further to support start-ups focused on preventing and curing cancer for vulnerable patients. We’ve also partnered with Ben Freeberg and his team at Oncology Ventures to ensure that digital health start-ups innovating for all patients in the oncology space have funding available to advance their causes. 

Innsena is joining more than 150 organizations already working together to make a difference for all patients in the prevention and treatment of cancer. CancerX is co-hosted by the Moffitt Cancer Center and Digital Medicine Society, alongside the US Department of Health and Human Services Office for the National Coordinator for Health Information Technology and Office of the Assistant Secretary for Health. 

We need more innovators working to improve care for the underserved. Join us in supporting CancerX. As a community we’ll make a difference. 

Kat McDavitt is President and founding partner of Innsena. Leslie Kirk is CEO and managing partner of Innsena.

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The 2024 Word of the Year: Missense

By MIKE MAGEE

Not surprisingly, my nominee for “word of the year” involves AI, and specifically “the language of human biology.”

As Eliezer Yudkowski, the founder of the Machine Intelligence Research Institute and coiner of the term “friendly AI” stated in Forbes:

“Anything that could give rise to smarter-than-human intelligence—in the form of Artificial Intelligence, brain-computer interfaces, or neuroscience-based human intelligence enhancement – wins hands down beyond contest as doing the most to change the world. Nothing else is even in the same league.” 

Perhaps the simplest way to begin is to say that “missense” is a form of misspeak or expressing oneself in words “incorrectly or imperfectly.” But in the case of “missense”, the language is not made of words, where (for example) the meaning of a sentence would be disrupted by misspelling or choosing the wrong word.

With “missense”, we’re talking about a different language – the language of DNA and proteins. Specifically, the focus in on how the four base units or nucleotides that provide the skeleton of a strand of DNA communicate instructions for each of the 20 different amino acids in the form of 3 “letter” codes or “codons.”

In this protein language, there are four nucleotides. Each “nucleotide” (adenine, quinine, cytosine, thymine) is a 3-part molecule which includes a nuclease, a 5-carbon sugar and a phosphate group. The four nucleotides unique chemical structures are designed to create two “base-pairs.” Adenine links to Thymine through a double hydrogen bond, and Cytosine links to Guanine through a triple hydrogen bond. A-T and C-G bonds  effectively “reach across” two strands of DNA to connect them in the familiar “double-helix” structure. The strands gain length by using their sugar and phosphate molecules on the top and bottom of each nucleoside to join to each other, increasing the strands length.

The A’s and T’s and C’s and G’s are the starting points of a code. A string of three, for example A-T-G is called a “codon”, which in this case stands for one of the 20 amino acids common to all life forms, Methionine. There are 64 different codons – 61 direct the chain addition of one of the 20 amino acids (some have duplicates), and the remaining 3 codons serve as “stop codons” to end a protein chain.

Messenger RNA (mRNA) carries a mirror image of the coded nucleotide base string from the cell nucleus to ribosomes out in the cytoplasm of the cell. Codons then call up each amino acid, which when linked together, form the protein. The protein’s structure is defined by the specific amino acids included and their order of appearance. Protein chains fold spontaneously, and in the process form a 3-dimensional structure that effects their biologic functions.

A mistake in a single letter of a codon can result in a mistaken message or “missense.” In 2018, Alphabet (formerly Google) released AlphaFold, an artificial intelligence system able to predict protein structure from DNA codon databases, with the promise of accelerating drug discovery. Five years later, the company released AlphaMissense, mining AlphaFold databases, to learn the new “protein language” as with the large language model (LLM) product ChatGPT. The ultimate goal:  to predict where “disease-causing mutations are likely to occur.”

A work in progress, AlphaMissense has already created a catalogue of possible human missense mutations, declaring 57% to have no harmful effect, and 32% possibly linked to (still to be determined) human pathology. The company has open sourced much of its database, and hopes it will accelerate the “analyzes of the effects of DNA mutations and…the research into rare diseases.”

The numbers are not small. Believe it or not, AI says the 46-chromosome human genome theoretically harbors 71 million possible missense events waiting to happen. Up to now, they’ve identified only 4 million. For humans today, the average genome includes only 9000 of these mistakes, most of which have no bearing on life or limb.

But occasionally they do. Take for example Sickle Cell Anemia. The painful and life limiting condition is the result of a single codon mistake (GTG instead of GAG) on the nucleoside chain coded to create the protein hemoglobin. That tiny error causes the 6th amino acid in the evolving hemoglobin chain, glutamic acid, to be substituted with the amino acid valine. Knowing this, investigators have now used the gene-editing tool CRISPR (a winner of the Nobel Prize in Chemistry in 2020) to correct the mistake through autologous stem cell therapy.

As Michigan State University physicist Stephen Hsu said, “The goal here is, you give me a change to a protein, and instead of predicting the protein shape, I tell you: Is this bad for the human that has it? Most of these flips, we just have no idea whether they cause sickness.”

Patrick Malone, a physician researcher at KdT ventures, sees AI on the march. He says, this is “an example of one of the most important recent methodological developments in AI. The concept is that the fine-tuned AI is able to leverage prior learning. The pre-training framework is especially useful in computational biology, where we are often limited by access to data at sufficient scale.”

AlphaMissense creators believe their predictions may:

“Illuminate the molecular effects of variants on protein function.”

“Contribute to the identification of pathogenic missense mutations and previously unknown disease-causing genes.”

“Increase the diagnostic yield of rare genetic diseases.”

And of course, this cautionary note: The growing capacity to define and create life carries with it the potential to alter life. Which is to say, what we create will eventually change who we are, and how we behave toward each other.

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

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The Optimism of Digital Health

By JONATHON FEIT

Journalists like being salty.  Like many venture investors, we who are no longer “green” have finely tuned BS meters that like to rip off the sheen of a press release to reach the truthiness underneath. We ask, is this thing real? If I write about XYZ, will I be embarrassed next year to learn that it was the next Theranos?

Yet journalists must also be optimistic—a delicate balance: not so jaded that one becomes boooring, not so optimistic that one gets giddy at each flash of potential; and still enamored of the belief that every so often, something great will remake the present paradigm.

This delicately balanced worldview is equally endemic to entrepreneurs that stick around: Intel founder Andy Grove’s famously said “only the paranoid survive,” a view that is inherently nefarious since it points out that failure is always lurking nearby. Nevertheless, to venture is to look past the risk, as in, “Someone has to reach that tall summit someday—it may as well be our team!” Pragmatic entrepreneurs seek to do something else, too: deliver value for one’s clients / customers / partners / users in excess of what they pay—which makes they willing to pay in excess of what the thing or service costs to produce. We call that metric “profit,” and over the past several years, too many young companies, far afield of technology and healthcare, forgot about it.

Once upon a time, not too many years ago, during the very first year that my company (Beyond Lucid Technologies) turned a profit, I presented to a room of investors in San Francisco, and received a stunning reply when told that people were willing to pay us for our work.  “But don’t you want to grow?” the investor asked. 

Flabbergasted, I replied that we felt it was more important to deliver enough value that people were willing to pay enough that we could operate in the black, whereas the typical “growth at all costs” model is essentially about subsidizing enough adoption using outside capital that winning a market becomes a game of chicken with one’s competitors: the one who can lose the most for longest wins…and when the other guy is dead and desiccated, having used up all its venture money driving prices and margins to zero, the winner gets to raise prices. Like a victorious seal, lion, or bison, the winner controls the beach, the savannah, the prairie.

According to Business Insider, Matthew Wansley, a professor at Yeshiva University’s Cardozo School of Law said, “Progressive economists had long understood that tech companies, backed by gobs of venture capital, were effectively subsidizing the price of their products until users couldn’t live without them. Think Amazon: Offer stuff cheaper than anyone else, even though you lose money for years, until you scale to unimaginable proportions. Then, once you’ve crushed the competition and become the only game in town, you can raise prices and make your money back. It’s called predatory pricing, and it’s supposed to be illegal.”

Happily, cynical ways of doing business don’t work forever or in all contexts. Once interest rates rise, every contender has a handicap—but it is the biggest, strongest, most willing to go to the mat who find themselves vulnerable in a new and unhappy way. Profitable companies have both hands free to fight, and their weapons of choice are real metrics to show value and efficiency. By contrast, firms whose growth was fueled by “free” money are fighting with their hands chained to cement that is getting heavier. Using the language of the Great Recession, the teaser rate on their mortgage just skyrocketed, and those payments…yeesh.

But profit is more than just a financial metric—it is also a powerful and pragmatic signal. The renewed, overdue focus on profit’s second, more esoteric importance was on full-peacock display during the first day of the Digital Health Innovation Summit (DHIS) West earlier this week, where the main takeaway from seemingly every presenter was: Can you prove your value, and convince me that I cannot go another day without you?

Hospital and health insurance executives—whose names I do not need to recite here; you can find the agenda online—speaking frankly and alongside firms whose services they have hired, addressed questions about how to break through the noise of too many emails, too polished emails, too little focus on building real relationships. Then they acknowledged that they are slammed-busy and lack the time to build them while also traveling to conferences to talk about relationship-building…which means finding another way through the noise. That is the entrepreneur’s mission, and trick. One executive basically said, “Don’t call us, we’ll call you” if we want what you have to offer (Remember people, this is San Diego, not Hollywood!).

Another confessed that so many young companies are coached about the “right” way to phrase an opening salvo that the pitches begin running together, filled with plenty of heart and dripping with mission but still lacking individuality. In other words, a bit of roughness-around-the-edges may not be a bad thing when some organizational leaders highlighted their interest in building collaboratively.  Because I would be remiss not to, I asked how Mobile Medical services can engage with hospitals to expand their role and showcase all the good they can do beyond transport—for example, Community Paramedicine. The advice was to sit down with the agency’s emergency department contact and straightforwardly say, “We’d like to help out more.” No fluff. No pussyfooting. Tactic #1: have a discussion. The worse anyone can say is “No.” Here’s something telling: I had a chance to explain some of the good that Community Paramedicine programs already do, and some of the interoperability wins that Mobile Medical services have already notched. Some of these executives did not even know about them—which just goes to highlight the noise. Both ventures and those who use them to do great things need to sing more about success….but, it seems, not necessarily more loudly.  Rather, in a more targeted fashion that all the willing, listening ears can hear.

Which goes back to profit: More than raising another round of funding, or winning an award, or stacking a slide deck with logos, being able to say “people are willing to pay for this work—presumably more than once—more than it costs to make, and you should consider it to, and here is why” is curious to those who may not have yet been aware that such a solution exists.

One hospital executive here described their employer’s new ethos: “We don’t need to do everything ourselves.” But with the willingness to look beyond the walls of the institution is a Monkey’s Paw kind of change: careful what you wish for. The price for such willingness is a focus on accountability—those rising interest rates put on pressure everywhere, which means investments have to perform. Now they cost money in excess of people’s time (which they are getting paid for anyway). As every minute becomes more expensive, the last thing these executives asked for is more waste.

I arrived at the DHIS West prepared to meet old friends and hear old tropes.  Perhaps I would even have been able to confirm that—as CEO of a company that is unusual by Bay Area standards, working in the world of Mobile Medicine that too few understand (“The sirens sound and your people show up…right?”)—there would be nothing to see because all the oxygen would have been spent talking about a hot new topic without fundamentals (or in the case of A.I. with declining fundamentals). Of course A.I. would be a bingo buzzword (“Take a shot!”) but I also expected boldface speakers reciting platitudes.

Boy was I wrong! Color me impressed! By dinner, my salty journalistic crust had washed away clean.  Instead, I confessed to my tablemates—an entrepreneur, an insurance professional, and Michelle Snyder, a lovely, ever-curious person who I first met a decade ago (wow!)—that DHIS West almost immediately inspired me to look back at the arc of our profession, and in so doing, to recognize how much change has really happened—even though, like so many fleeting loves in life, on a daily basis we are too close to see it. As Michelle said, it’s not moving fast enough—but it never will be for someone who is committed to improving the status quo. I suspect that for her, the deadline to achieve impact at scale in American and global healthcare will always be yesterday.

I later described to Ilana Brand, a business development executive in the area of digital health for the law firm Cooley, my own mental wellness and mission-motivation trick, which I have done for years and recommend to anyone who has been venturing for as long as I have: look back on those old slide decks from time to time to see how much has changed—and what remains the same. The through-line orientation to address problems in the market should ideally be consistent until they are solved—but a company cannot be stubborn either, lest an asteroid come. It must adaptive to changing realities while keeping its soul. Ideally, in hindsight, one sees ups, downs, fumbles and tackles, but always progressing toward the goal (and sometimes a Hail Mary pass is just what the digital doctor ordered).  I am writing this just days before Super Bowl LVIII (Go Niners!), so perhaps football offers an ideal entrepreneurial analogy after all.

What’s magical is to look back on the arc of change with a sense of wonder and gratitude for how far we have come when seen at a distance (as opposed to while in the trenches of innovation). It’s like watching the horizon bend in the distance while flying toward the sunset: we all know that the Earth is round, and if we get high enough, we can see so for ourselves. Yet that knowledge still pales against “Oh my gosh, look in the distance! The colors…the curve of our planet…how amazing to think we’re up so high.  No strings!”

Finally: we spoke, of course, of artificial intelligence—but not of generative A.I. per se. A dichotomy is forming: some think A.I. will be relegated, for the foreseeable future, to administration, where it will automate the paperwork that everyone hates and so it becomes both expensive and neglected. This approach has the added benefit of delaying the introduction of perceived “replacement” technologies into clinical settings (with pushback anticipated just like it was in Hollywood and elsewhere). The delay may serve to our collective benefit because A.I. has not yet come close to solving its hallucination problem.

Others (including me) believe we may be selling ourselves short—and I was further inspired by investor Ryan McCrackan, CFA, who described an optimistic future: as soon as something extraordinary proves itself, the instinctual corporate risk aversion, which often blocks great things from happening, will be proven to have overblown. Attention will quickly shift to all that could be possible. Then we’re off to the races, together, seeking and supporting meaningful improvements to under-attended sectors (“White spaces”) of health, safety, and life in general. Until then, we’ll embrace the most excellent irony that emerged post-pandemic, in conjunction with the Dawn of Artificial Intelligence: In both medicine and business, “relationships still matter.”

Jonathan Feit is the CEO of Beyond Lucid Technologies

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Who to Blame for Health Costs: The Poisoned Chalice of “Moral Hazard”

By JEFF GOLDSMITH

How the Search for Perfect Markets has Damaged Health Policy

Sometimes ideas in healthcare are so powerful that they haunt us for generations even though their link to the real world we all live in is tenuous. The idea of “moral hazard” is one of these ideas.   In 1963, future Nobel Laureate economist Kenneth Arrow wrote an influential essay about the applicability of market principles to medicine entitled “Uncertainty and the Welfare Economics of Medical Care”.    

One problem Arrow mentioned in this essay was “moral hazard”- the enhancement of demand for something people use to buy for themselves that is financed and purchased through third party insurance. Arrow described two varieties of moral hazard: the patient version, where insurance lowers the final cost and inhibitions, raising the demand for a product ,and the physician version- what happens when insurance pays for something the physician controls by virtue of a steep asymmetry of knowledge between them and the patient and more care is provided than actually needed.

Moral hazard was only one of several factors Arrow felt would made it difficult to apply rational economic principles to medicine. The highly variable and uniquely threatening character of illness was a more important factor, as was the limited scope of market forces, because government provision of care for large numbers of poor folk was required.  

One key to the durability of Arrow’s thesis was timing:  it was published just two years before the enactment of Medicare and Medicaid In 1965, which dramatically expanded the government’s role in financing healthcare for the elderly and the categorically needy. In 1960, US health spending was just 5% of GDP, and a remarkable 48% of health spending was out of pocket by individual patients.   

After 1966, when the laws were enacted, health spending took off like the proverbial scalded dog.  For the next seven years, Medicare spending rose nearly 29% per year and explosive growth in health spending rose to the top of the federal policy stack. By 2003, health spending had reached 15% of GDP!   Arrow’s  moral hazard thesis quickly morphed into a “blame the patient” narrative that became not only a central tenet of an emerging field of health economics, as well as in the conservative critique of the US health cost problem.  

Fuel was added to the fire by Joseph Newhouse’s RAND Health Insurance Experiment in the 1980s,  which found that patients that bore a significant portion of the cost of care used less care and were apparently no sicker at the end of the eight-year study period. An important and widely ignored coda to the RAND study was that patients with higher cost shares were incapable of distinguishing between useful and useless medical care, and thus stinted on life-saving medications that diminished their longer term health prospects. A substantial body of consumer research has since demonstrated  that patients are in fact terrible at making “rational” economic choices in re: their health benefits. 

The RAND study provided justification for ending  so-called first dollar health  coverage and, later,  high-deductible health plans. Today more than half of all Americans have high deductible health coverage.  Not surprisingly, half of all Americans also report foregoing care because they do not have the money to pay their share of the cost!   

However, a different moral hazard narrative took hold in liberal/progressive circles, which blamed the physician, rather than the patient, for the health cost crisis.

The Somers (Anne and Herman) argued that physicians had target incomes, and would exploit their power over patients to increase clinical volume regardless of actual patient needs to meet their target income. John Wennberg and colleagues at Dartmouth later indicted  excessive supply of specialty physicians for high health costs. (Wennberg’s classic analysis of New Haven vs. Boston’s healthcare use was later shredded by Buz Cooper for ignoring the role of poverty in Boston’s much higher use rates.)

The durable “blame the physician” moral hazard thesis has led American health policy on a  futile five-decade long quest for the perfect  payment framework that would damp down health cost growth -first capitation and  HMOs, then, during Obama years, “value-based care” – a muddy term for incentives to providers that will eliminate waste and unnecessary care. Value-based care advocates assume that  physicians are helpless pawns of whatever schedule of financial rewards are offered them, like rats in a Skinner box. If policymakers can just get the “operant condition schedule” right,  waste will come tumbling out of the system. 

The end result of this narrative: thanks in no small part to the festival of technocratic enthusiasm that accompanied ObamaCare, (HiTECH, MACRA, etc), physicians and nurses now spend as much time typing and fiddling with their electronic health records to justify their decisions as they do caring for us. Controlling physician moral hazard thru AI driven claims management algorithms has become a multi-billion business. The biggest “moral hazard mitigation” company, UnitedHealth Group, has a $500 billion market cap.

Thus the poisonous legacy of Arrow’s “moral hazard” thesis has been two warring policy narratives that blame one side or the other of the doctor-patient relationship for rising health costs. It has given us a policy conversation steeped in mistrust and cynicism. You can tell if someone is a progressive or conservative merely by asking who they blame for rising health costs! 

There were credible alternative explanations for the post-Medicare cost explosion. Recall that the point of expanding health coverage in the first place was that better access to care DOES in fact improve health.   Medicare lifted tens of millions of seniors out of poverty, improving both their nutrition and living conditions. Medicaid dramatically broadened access to care for tens of millions in poverty. This expansion of coverage, and the added costs, deserve much credit for the almost nine year improvement in Americans’ life expectancy from 1965 to 2015. 

It is also worth recalling that the two most explosive periods of inflation in the post-WWII US economy were the late 1960’s, the so-called Guns and Butter economy that financed the Vietnam War, and the mid 1970’s to 1981, fueled by the Arab oil embargo. These periods of hyperinflation coincided with the coverage expansion, amplifying their cost impact.

And of course, the 1980’s also saw a flood of optimistic, high energy baby boomer physicians, the result of a dramatic federally funded expansion of physician supply begun by Congress during the 1970’s. The reason for this surge: we did not have enough physicians to meet the demands of the newly enfranchised Medicare and Medicaid populations. 

This surge in aggressive young physicians coincided with dramatic expansion in the capabilities of our care system- non-invasive imaging technologies such as MR, CT and ultrasound, ambulatory surgery, which dramatically lowered both the risks and costs of surgical care, the advent of effective cancer treatments, which cut the cancer death rate by one-third from its 1991 peak,  and the advent of statins, and less invasive heart treatment, which has reduced mortality from heart disease by 4% per year since 1990, despite the rise in obesity!    

Medicine today is of an different order of magnitude of clinical effectiveness, technical complexity and, yes, cost, than that on offer in 1965. No one would trade that health system for the one we have today.    

However, the biggest problem with the moral hazard theses- both of them- was the assumption that the physician and the patient are primarily motivated by “maximizing their utility” in the healthcare transaction. Arrow knew better. He emphasized the role that fear and existential risk played in their interaction, given that illness, particularly serious illness, is, as he put it, “an assault on personal integrity”.  

By reducing the physician-patient interaction to a mutual quest of the proverbial free lunch, economists have not only insulted both parties, but grossly oversimplified this complex interaction. Is a sick person really “consuming” medical care, like an ice cream bar or a movie? Is the physician really “selling” solutions regardless of their effectiveness, unconstrained by pesky professional ethics, or rather groping through fraught uncertainty to apply their knowledge to helping their patient recover? 

In contrast to virtually every other Western country, American health policy has been obsessed for nearly sixty years with fighting moral hazard, and in the process, saddling almost 100 million Americans with $195 billion in medical debts (the vast majority of which are uncollectible). Isn’t it ironic that those other wealthy countries that provide their citizens care free at the point of service spend between 30-50% less per capita on healthcare than we do? And that both physician visits and hospitalization rates are far lower in the US than most of these countries.   

There is no question that healthcare in the US today is very expensive. But health costs have been dead flat as a percentage of US GDP for the past thirteen years. The explosive growth in health costs is over. Increasingly, attention is turning to the real culprit–socially determined causes of illness, and the inadequacy of our policies toward nutrition, shelter, mental health, gun violence and investment in public health. It’s time for the economists to eat some humble pie, and acknowledge that medicine will probably never fit in their cartoon universe of “Pareto optimality in perfect markets”.

Jeff Goldsmith is a veteran health care futurist, President of Health Futures Inc and regular THCB Contributor. This comes from his personal substack

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