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.

From The Laws of Medicine, a short book by Siddhartha Mukherjee:

My medical education had taught me plenty of facts, but little about the spaces that live between facts. I could write a thesis on the physiology of vision. But I had no way to look through the fabric of confabulation spun by a man with severe lung disease who was prescribed “home oxygen,” but gave a false address out of embarrassment because he had no “home.” (The next morning, I got an irate phone call from the company that had attempted delivery of three canisters—to a Boston storefront that sold auto parts.)

I had never expected medicine to be such a lawless, uncertain world. I wondered if the compulsive naming of parts, diseases, and chemical reactions-frenulum, otitis, glycolysis—was a mechanism invented by doctors to defend themselves against a largely unknowable sphere of knowledge. The profusion of facts obscured a deeper and more significant problem: the reconciliation between knowledge (certain, fixed, perfect, concrete) and clinical wisdom (uncertain, fluid, imperfect, abstract).

Medicine is a soft science involving the application of limited knowledge amidst uncertainty. Historically, it was wholly unscientific. In the modern era, we apply the philosophy of Karl Popper:

In his 1934 book, The Logic of Scientific Discovery, the philosopher Karl Popper proposed a crucial criterion for distinguishing a scientific system from an unscientific one. The fundamental feature of a scientific system, Popper argued, is not that its propositions are verifiable, but that its propositions are falsifiable-i.e., every theory carries an inherent possibility of proving it false. A theory or proposition can only be judged “sci-entific” if it carries within it a prediction or observation that will prove it false. Theories that fail to generate such “falsifiable” conjectures are not scientific. If medicine is to become a bona fide science, then we will have to take up every opportunity to falsify its models, so that they can be replaced by new ones.

Mukherjee posits three “laws”:

LAW ONE
A strong intuition is much more powerful than a weak test.

LAW TWO
“Normals” teach us rules; “outliers” teach us laws.

LAW THREE
For every perfect medical experiment, there is a perfect human bias.

These amount to: 1) The conditional probability of Bayesian reasoning means that bad tests done indiscriminately yield more noise than information. 2) Outliers shouldn’t be ignored, but should be studied. The rare exceptions often drive our discoveries. 3) Cognitive biases are an inescapable part of the human condition.

Regarding medicine as presaged by Lewis Thomas, who in 1983 published the most popular book about the dawn of modern medicine in The Youngest Science:

Thomas would not have predicted this stickiness of uncertainties and constraints; the future of medicine that Thomas had imagined was quite different. “The mechanization of scientific medicine is here to stay,” he wrote optimistically in The Youngest Science. Thomas presaged a time when all-knowing, high-precision instruments would measure and map all the functions of the human body, leaving little uncertainty and even fewer constraints or gaps in knowledge. “The new medicine works,” he wrote. “The physician has the same obligations that he carried, overworked and often despairingly, fifty years ago-but now with any number of technological maneuvers to be undertaken quickly and with precision…. The hospitalized patient feels, for a time, like a working part of an immense, automated apparatus.

He is admitted and discharged by batteries of computers, sometimes without even learning the doctors’ names. Many patients go home speedily, in good health, cured of their diseases…. If I were a medical student or an intern, just getting ready to begin, I would be more worried about this aspect of my profession. I would be apprehensive that my real job, taking care of sick people, might soon be taken away, leaving me with the quite different occupation of looking after machines.”

In reality, things have panned out quite differently: despite the increasing accuracy of tests, studies, and equipment, the doctors of today have to contend with priors, outliers, and biases with even deeper and more thoughtful engagement than doctors of the past. This is not a paradox.

Ironically, Thomas’s passage from 1983 feels more prescient about current fears than Mukherjee’s from 2015.

Some career advice from the archives of an otherwise defunct blog from venture capital billaionaire Marc Andresseen:

In my opinion, it’s now critically important to get into the real world and really challenge yourself—expose yourself to risk—put yourself in situations where you will succeed or fail by your own decisions and actions, and where that success or failure will be highly visible.

By failure I don’t mean getting a B or even a C, but rather: having your boss yell at you in front of your peers for screwing up a project, launching a product and seeing it tank, being unable to meet a ship date, missing a critical piece of information in a financial report, or getting fired.

Why? If you’re going to be a high achiever, you’re going to be in lots of situations where you’re going to be quickly making decisions in the presence of incomplete or incorrect information, under intense time pressure, and often under intense political pressure. You’re going to screw up—frequently—and the screwups will have serious consequences, and you’ll feel incredibly stupid every time. It can’t faze you—you have to be able to just get right back up and keep on going.

That may be the most valuable skill you can ever learn. Make sure you start learning it early.

This is one of the things about a career in medicine that is very hard to do early on without going outside the school-industrial complex. In the olden days, medical students did more stuff. In many cases, contemporary students are essentially not permitted to have ownership of anything.

Cultivating the skill to deal with responsibility, decision-making admist uncertainty, and dealing with suboptimal interactions & results is probably the biggest differentiator between a happy and unhappy approach to practicing medicine.

As is the case with so many things in medicine, our selection process—for medical school, residency, and nearly every stage until a person has the requisite skills to practice—is riddled with suboptimal, if not entirely meaningless or counterproductive, proxy measures.

When we select college students for medical school, or medical students for certain residencies, the presumed goal is to select people who will become good doctors. On the first pass, we want to find those who will make good doctors generally, and on the second, those who will excel in specific fields. But if we’re honest, we’re terrible at this. We are absolutely abysmal at identifying the right people for the job.

Magical Metrics

The metrics required for field competitiveness are a reflection of supply/demand (i.e. the relative scarcity of training spots) more than the cognitive firepower, personality/disposition, or physical skills required for competency.

Our testing systems are, at best, inefficient measures of general intelligence and hard work. While those traits are important, the process we use to assess them requires students to spend countless hours learning material of dubious utility, often at the expense of other potentially valuable endeavors.

Economics teaches us that when resources are limited, the question isn’t whether something has value—most things do— but whether it’s the best use of those resources. There is no empirical evidence that our current testing paradigm or medical school curriculum broadly is the best use of applicants’ time, or that it effectively selects for the traits we actually care about.

On top of flawed testing—and accelerated by pass/fail Step 1—we have an obsession with CV-padding. Whether due to laziness, limited resources, or a lack of better alternatives, we’ve created a system that rewards applicants for amassing measurable activities that give the illusion of merit, even when those activities are empty gestures.

This leads to students wasting their free time on meaningless research, instead of pursuing hobbies, passions, or other fulfilling endeavors. To this, we’ve added a layer of well-intentioned but often questionable community service and outreach activities—nice in theory, but of dubious impact for both the person doing them and the community receiving them. None of these activities are inherently bad. Research isn’t bad. Helping people certainly isn’t bad. But when these tasks are reduced to tokenized, measurable units, we have to ask ourselves: are they being done in service of actual good, and is this the best way to accomplish that good? Is it possible for the outsider observer to distinguish the real deal from the slop?

Time & Money

Another valid criticism is that these demands favor students of means. Those with time, money, and connections are better positioned to engage in these resume-building activities, take gap years, or pursue additional schooling to improve their applications. This creates barriers for students without these resources. As if four years of undergraduate education weren’t enough preparation for a medical career, we’ve created a system where applicants need even more time, money, and effort just to qualify for medical school.

The irony is that other countries, and even parts of our own, have occasionally managed to streamline this process. Medicine faces a “good problem”: more people want to enter the field than there are spots. But we also face a deeper issue: many who do get those spots end up unhappy in practice or with no intention to practice in the first place. Some of this is due to the inherent difficulties of working in our broken healthcare system, but part of it likely reflects poor selection among applicants. Our laziness comes at a cost: we rob students of the full breadth of life when we force them into a narrow mold and make them compete in proxy contests trying to accumulate  “experience.”

What makes a good doctor?

What actually makes a good doctor? Hard work, kindness, and resilience are likely more important than test scores or resume padding. Yet, as Peter Drucker famously said, “What’s measured gets managed.” When we measure test scores and activities, we optimize students to achieve those outputs. But those outputs come at a cost. And too often, that cost includes losing good, happy, and fulfilled students, trainees, and practicing physicians.

Laws of unintended consequences combined with a crappy system, this can’t be the right way for people to spend their time collecting brownie points:

When I see the graphs showing the medical student research arms race, it seems to me that the numbers between unmatched and matched students are pretty similar in most fields.

Is anyone actually fooled by the low-quality fluff? Are students just doing this to themselves? pic.twitter.com/ei6hfSpJns

— Ben White, MD (@benwhitemd) August 25, 2025

(chart via NRMP’s Charting Outcomes)

(I apparently shared a similar but different chart a couple of years ago as well.)

The prevailing belief is that in the era of pass/fail Step 1, students need to compete on research to stand out. I think that is probably not quite the reality, as we still have a measurable Step 2. I think we’re really seeing is not the need to stand out because of P/F Step 1 but rather the combination of relatively increased available time and greater uncertainty:

Time & Pressure:

With the pressure of Step 1 removed for strong students (who are in no danger of failing), pass/fail Step 1 has enabled many students to spend more time generally polishing their applications. This has been compounded by pass/fail curricula more broadly. Learning enough to pass simply doesn’t take the same amount of time as aiming for a perfect score.

Research is typically felt to be “more important” than other extracurriculars and it’s easy to quantify, but people are also certainly also checking boxes for volunteer opportunities and clubs. Everyone seems to have been 1 of 4 co-presidents of their local Magical Interest Group.

Schools went pass/fail for a variety of good reasons, but nature abhors a vacuum. It’s been filled with measurable trash.

Uncertainty:

We already had our longstanding competition due to the scarcity of “desirable” residency spots, but other unintended consequence of all these pass/fail components is that it delays knowing how competitive you really are for your desired field.

It used to be that you received a disappointing score on Step 1, and—before clerkships even started—you adjusted your dreams of dermatology.

Now that you can’t know if your Step 2 score will be competitive until you’ve already essentially entered application season. It makes intuitive sense to do everything else in your power to polish your potential turd if you want to maximize your chances for your desired specialty + location combination.

Step 2 is the new Step 1; it’s just harder to plan a career around.

So what?

Reasonableness at the n=1 level aside, I think this is a problem.

The research slop is largely meaningless. The work itself is mostly garbage, and people are wasting time, money, and resources filling the dregs of pay-to-publish journals. We’re also incentivizing volume over quality so that students are incentivized to pretend that random surveys and opinion pieces are research instead of spending real time doing real work that could have a meaningful impact on other people or actually develop valuable skills. Most work is read by no one except AI bots, and the last thing we need are the LLMs internalizing a bunch more fake research and observational BS.

Time is zero-sum. The question can therefore never just be: is there value? Despite the mockery and dismissal above, of course there is some value. The question has to be: is this the best use of limited resources to achieve the goals of graduating good doctors?

Building a true meritocracy with holistic application selection is an incredible challenge. Matching people to a limited prospective jobs based on both their desires and their aptitudes is truly hard, and the desperation to shine is just as reasonable here as high school students buffing their resumes for college admissions.

Easy Mitigation Steps

We can’t change the overall game, but we can adjust the rules to nudge the behaviors to our desired outcomes.

The NRMP needs to at least start reporting the median and not the mean. Even better, we should split application success into quartiles. Students currently see this data and are mislead, because long tail outliers are dragging the mean up. Many who are “below average” for their field aren’t actually below the median. Break these things down by quartile and maybe then we’ll see how “required” and impactful research really is in most fields.

We should probably also limit the ERAS length and separate posters/abstracts/presentations from publications. We need to limit the double-counting that distorts the averages and change the incentives to promote diving deep to do meaningful work.

The Great Filter

After posting that chart and out of curiosity, I did a 100% unscientific poll on Twitter with 71 responses:

Residency program directors and staff, do you filter medical student residency applications by research experiences?

— Ben White, MD (@benwhitemd) August 26, 2025

So, I can’t pretend that the students are wrong to play the game. It just means even more that, as a field, we need to adjust our systems and incentives to drive our actual desired behavior and improve our actual observable outcomes.

Of course, how widespread this type of filtering really is and the actual impact for different specialities would make for a great research project.

Deskilling and automation bias will be big problems with useful AI, but what do you call it when someone never has to develop skills in the first place?

Unskilling?

(Apparently, a at least one new paper describes it as “never-skilling”)

Residency faculty, do you have an AI usage policy for your trainees? Why or why not?

— Ben White, MD (@benwhitemd) August 23, 2025

My son’s middle school has a detailed AI usage policy. It’s hard to believe medical training doesn’t require some thought on how to ensure robust, resilient skill acquisition.