Preparing for standardized tests by yourself using high-quality resources is both effective and a little bit soulless. There’s a reason why much of medical education could be streamlined in both time and cost to what amounts to an old-time correspondence course—that’s because it’s long been sold as an information problem, and the core question for schools has been how to best transmit the holy information to the student.

This was probably historically at least partially true, but in the 21st century, this conception misses the point: information is no longer the core skill that needs cultivation. It is social intelligence, human skills, and the bringing of information to bear for problem-solving. It’s critical thinking, and doing it while working with people. And yes, the information is important (it really, really is!)—but the information is not where very expensive medical schools really shine. With the advent of better qBanks, Anki decks, and commercial lecture products, we are increasingly choosing a factory schooling model over one that prioritizes a social experience of working with caring teachers, motivated peers that are important to you, and patients with meaningful tasks to provide motivation and centering.

The schools and students spend lots of money on pieces of paper and question banks and other forms of curated video content, and these are all wonderful things. My point is not to suggest that using better lectures and better questions is a bad thing for education—it’s certainly not. It’s that those things do not preclude the need for the other part—not in some token problem-based learning format or anything so prescriptive—but real, meaningful, non-tedious, in-person work.

Whatever it once was, it is no longer mainly an information problem. As the cliche goes, you can lead a horse to water, but you can’t make it drink. We need to encourage people to be motivated (or find motivated people), and we need to help curate and support sustaining that curiosity—that people can do and internalize the need to continue doing that type-2 fun of hard work. The fact that students would rather be at home studying instead of working in the hospital shows the fruits of our system: the triviality of many students’ clinical experiences (the clerkship as performance art) combined with the pressure of Shelf exams as the defining feature of their grades.

The world is full of heavy objects, and yet most people are not ripped. We are the limiting factor, and we need systems to help us and support us to be our best selves. Burnout is a system that snuffs out our soul’s flickering flame.

There was a time in my radiology training where people were encouraged to incorporate multiple-choice questions with audience response into their lectures instead of taking hot-seat cases where people were put on the spot. That is more comfortable, to be sure, but I can tell you the fear and anxiety of wanting to perform for your colleagues and mentors was much more inspiring. Not every time you’re wrong in public is unfair pimping/humiliation.

An ability to take those hits on the chin when you flub a case is also important. Psychological safety doesn’t mean never being challenged—it means being supported enough that you can bounce back from your inevitable failures. We have forgotten how important resiliency is, and we’ve allowed undergraduate medical education to remain dominated by the factory information paradigm while neutering the chance for more students to become respected members of the care team more of the time.

I would posit that good healthcare is more analogous to a restaurant than most large corporations. From Michael E. Porter’s Competitive Strategy: Techniques for Analyzing Industries and Competitors:

If close local control and supervision of operations is essential to success the small firm may have an edge. In some industries, particularly services like nightclubs and eating places, an intense amount of close, personal supervision seems to be required. Absentee management works less effectively in such businesses, as a general rule, than an owner-manager who maintains close control over a relatively small operation. Smaller firms are often more efficient where personal service is the key to the business. The quality of personal service and the customer’s perception that individualized, responsive service is being provided often seem to decline with the size of the firm once a threshold is reached. This factor seems to lead to fragmentation in such industries as beauty care and consulting.

Healthcare has seen those fragmentation factors dissolve since the 1990s and especially since the ACA.

In Redefining Health Care: Creating Value-based Competition on Results, Porter then argues:

Competition has taken place at the wrong levels, and on the wrong things. It has gravitated to a zero-sum competition, in which the gains of one system participant come at the expense of others. Participants compete to shift costs to one another, accumulate bargaining power, and limit services. This kind of competition does not create value for patients, but erodes quality, fosters inefficiency, creates excess capacity, and drives up administrative costs, among other nefarious effects.

Over the years since I returned to the area, the local university medical center has progressively moved away from individualized service into a predictably depressing corporate marketshare grab. If this is “The Future of Medicine Today,” the future is bleak.

If policymakers want to improve US healthcare, the easiest lever to pull first is to enable physician ownership and make it feasible to stay small without needing to opt out of the system entirely by going direct pay.

Different doesn’t mean right or wrong. Best practices are sometimes best, and sometimes they’re just a mediocre consensus that a small group agrees to.

Institutions are so often large and stupid because herds of humans are difficult to manage at scale, and the lessons learned by individuals often don’t become institutionalized without significant baggage in the form of procedural debt. So many individual actions and the proliferation of useless bureaucracy can be ascribed to the pursuit of plausible deniability. The fewer things that can be pinned on us, the safer our jobs. No one is fired for being rational, even if the outcome is suboptimal.

It’s not the goals but rather the incentives created by those goals that shape outcomes. It’s the BS that sounds good and reasonable in isolation versus what works in practice. The most important tactic for achieving any strategy is removing the friction, roadblocks, and unintended negative consequences of doing the actual desired behavior.

There is no broadly acceptable present value function to gauge political acts the way we can estimate the effects of economic decisions in terms of monetary value. The time horizon of politics, social media, and fame are all much shorter than the downstream consequences of most decisions, which incentivizes leaders of all stripes to do stupid shit that looks good now—even if it is, on the whole, harmful.

This is a problem because we often conflate “rational” behavior with optimization of easily measurable outcomes.

The creation of red tape is understandable, but the incremental costs are almost never considered and are challenging to undo, as every policy is obviously there for a reason—even if, on balance, that reason does not justify the cost. Nothing is easier than thinking of more things we can do to make something better or safer. What is harder is asking the tough question: at what cost? What are we willing to forgo in order to achieve this addition? Intentions matter, but the consequences matter more.

Bigger often means more inbound data and more layers of management to deal with it. Size isn’t always good, and growth shouldn’t always be the goal. Yes, size can help you fight a bully, and it can also help you be a bully. And there are efficiencies of scale when it comes to both high fixed costs as well as volume-based discounts on costs like IT. But we would be foolish to only look at those measurable costs and lose sight of the downsides: increasing bureaucracy, weak culture, lack of accountability, and a diluted mission.

Step into a large hospital and tell me if you think everybody feels like they’re on the same team. The fragmented care in your average mega-hospital is so often removed from a well-oiled machine. These organizations are essentially too large to have a mission or a really strong, behavior-shaping positive culture. Decision makers and rank-and-file are often separated by helplessness-inducing layers of human flotsam.

The mission of a large healthcare organization is not actully patient care. Healthcare is the industry, and patients are the vehicle for the business model. They are the sine qua non of healthcare profit. But the proliferation of bureaucrats and policies because of the need to “manage people” tells us that it’s not working.

If people were really working together, we wouldn’t need so much bloat.

The announcement of ChatGPT Health and then immediately Claude for Healthcare (just for “informational purposes,” of course) is big news, obviously. But the other big news from last week was Doctronic’s new pilot in Utah:

In a first for the U.S., Utah is letting artificial intelligence — not a doctor — renew certain medical prescriptions. No human involved.

The state has launched a pilot program with health-tech startup Doctronic that allows an AI system to handle routine prescription renewals for patients with chronic conditions

The program is limited to 190 commonly prescribed medications, so no pain or ADHD refills will be happening here. Some fighting words from the CEO:

“The AI is actually better than doctors at doing this,” said Dr. Adam Oskowitz, Doctronic co-founder and an associate professor of surgery at the University of California San Francisco. “When you go see a doctor, it’s not going to do all the checks that the AI is doing.”

In medicine, there’s always going to be potential issues that patients have,” said Oskowitz. “Whether it’s caused by the AI or not — we will take the risk. I think this is going to be infinitely safer than a human doctor.”

It’s worth pointing out that it’s much safer and easier for this product to practice medicine in the limited sense of renewing the previous decision of a human than it is to work de novo.

But the access problem is real, and 24/7 telehealth for a variety of urgent care-type problems is going to be a powerful argument in the near future, especially in rural areas.

The tools are improving, but the great irony is that clinical medicine has set the stage for this in two important ways.

1. Medicine is increasingly algorithmic.

The codification of guidelines and best practices means that large swaths of medicine are not just cognitively routine but are supposed to work within narrow variants. Picking from a few defensible (“correct”) options is easier than crafting the tasting menu from scratch.

When we want flesh-and-blood humans to utilize algorithms in the treatment of routine conditions, then a machine can utilize those same algorithms just as well—if not better per the evangelists—especially since a machine currently always has the opportunity to punt to a human if it’s confused. If there are hard stops in place for ethically fraught or problematic prescribing, then the most blatant jailbreaking concerns go down. It’s also worth pointing out that we already have online pill mills these days, no AI needed.

The product is going to punt whenever screener questions suggest medication intolerance or new meds in the reconciliation have interactions. Presumably, anything with nuance or that might require an alternative.

This is simply allowing a computer to refill a prescription that was already provided by a human for a medication taken by a human who still wants more. The algorithm can simply give the refill unless the patient tells it some contraindication has arisen in the intervening months that should change the picture, like a new medication interaction.

2. Healthcare is Terrible and Hard to Get

Another contributing factor is that healthcare is terrible, expensive, and hard to get. Patients wait for a long time and travel far and wide for short appointments that run behind schedule. It’s mostly not the fault of practitioners that they are overly busy, squeezing patients into tiny slots, but that’s the baseline reality. We are increasingly living in an era where access for routine clinical medicine is limited by cost. Those with the financial ability can opt for direct/concierge style care, and the rest are increasingly shuffled into a different tier, increasingly ministered by nonphysician practitioners operating autonomously.

Regardless of the details, it’s easier to replace something that isn’t good.

And let’s be clear with this initial salvo in the doctor-replacement process: this is not an LLM providing comprehensive care—doing an H&P or a new-patient evaluation and deciding how best to treat someone’s hypertension. That is a heavier lift and one prone to a hell of a lot more liability.

This is a machine taking some low-hanging fruit.

Consequences

Harder Average Work

One downstream problem of this approach is that quick med refill visits are some of the ways that a clinic actually makes money, and that facetime can still lead to important, unpredictable, impromtu care. Because so much clinical care is underpaid, you need some easy, straightforward work to keep from falling too far behind in a busy clinic schedule.

Patients disappearing from the clinic rolls, getting refills for a few years, and then coming back when they have a problem will be a challenge.

A possible early hole in the “Human in the Loop” argument

You could argue a human should be in the loop. But I think the reality of automation bias is that this is a difficult argument. A human being reviewing tons and tons of these types of AI decisions probably isn’t actually going to pay enough attention to catch errors at a meaningful frequency. One could reasonably debate how often these mistakes are currently caught during 15-minute med checks. But in many cases, in many systems, many, many refill requests populate in the EHR and just require a button press anyway.

A patient doesn’t always have to come in for an appointment just to check a box and get a refill at baseline, but on the whole a pilot like this will result in fewer upfront visits, shorten turnaround time, save time, money, and physician computer clicks. Some of the negative consequences will be anecdotes, and the real second-order effects will probably be missed. By taking on the minimal liability for doing this work, the AI company can charge for what amounts to MyChart messages.

Liability Isn’t the Magic Moat

People often talk about liability as if only human beings, doing the work of humans, could ever be insured. But that is obviously, manifestly untrue. Many of our current consumer insurance products (e.g. home insurance) are designed to deal with a variety of bad outcomes, not just bad human causes.

If an insurance company does the math and wants to underwrite a venture, it can. If a large company is willing to pay its own damages, it can self-insure.

What makes something worth doing is a risk-benefit ratio where, if the profits are high enough to counteract the risks and still generate a return, it’s something pursuable in our current system.

If regulators don’t mind, a company or even a health system could choose to implement any variety of these products and self-insure, even without an overhaul of our whole medical-legal system.

In the End

There is no doubt that some parts of telehealth are, frankly, inadequate for care today. We also won’t really get to the next level of the AI version without a robust multimodal approach that actually incorporates computer vision to look at a patient and see how they’re doing, auditory analysis for tone and voice changes, and real-time conversational language analysis.

This product can skip all that. It’s not providing human-level care. It’s acknowledging the reality that a lot of medicine is routine with a low bar.

In radiology, for example, people have previously argued that we would have machines read the normal cases autonomously and involve human radiologists to check the abnormal ones. If this were plausible, it would mean that the average complexity per case goes up, and the actual work per case gets harder.

It may make sense in some cases, but operating at the top of your license, so to speak, can also be exhausting.

It remains to be seen if there is a feasible way for any field of practice to do this in a sustainable way, even if it might be an economically viable one.

From Michael Porter’s Competitive Strategy:

We usually think of suppliers as other firms, but labor must be recognized as a supplier as well, and one that exerts great power in many industries. There is substantial empirical evidence that scarce, highly skilled employees and/or tightly unionized labor can bargain away a significant fraction of potential profits in an industry.

This is one of the core tensions in modern healthcare. Healthcare workers are the key source of revenue, but—because they want to be paid pesky competitive wages—also erode the potential profits of their employers, and many of those companies (regardless of their technical nonprofit vs profit status) really, really want more profit.

Private practice is not a panacea, but as most healthcare workers are not unionized, plausible alternatives to large entity employment are critical to enable workers to bargain for fair compensation. I suspect this is an increasing tension we should continue to expect with the greater use of AI. Employed physicians should desperately want their private practice colleagues to succeed.

From Porter’s follow-up, Redefining Health Care: Creating Value-based Competition on Results:

The fundamental problem in the U.S. health care system is that the structure of health care delivery is broken. This is what all the data about rising costs and alarming quality are telling us. And the structure of health care delivery is broken because competition is broken. All of the well-intended reform movements have failed because they did not address the underlying nature of competition.

Things have gotten so, so much worse since that was published in 2006.