I did a Q&A about student loans and the transition to residency (as well as a dash of passion is overrated and medical education is toxic) with the fine students across the DFW Metroplex at TCOM this spring, and it’s now available as an episode of the Bedside Business Podcast (Apple | Spotify | Google | Stitcher).
The Zoom recording audio is a smidge choppy at times but not enough to hurt as long as you slow down to 1.5x to account for my speed!
A classic Jeff Bezos quotation:
I very frequently get the question: “What’s going to change in the next 10 years?” That’s a very interesting question.
I almost never get the question: “What’s not going to change in the next 10 years?” And I submit to you that that second question is actually the more important of the two.
You can build a business strategy around the things that are stable in time. In our retail business, we know that customers want low prices, and I know that’s going to be true 10 years from now. They want fast delivery; they want vast selection. It’s impossible to imagine a future 10 years from now where a customer comes up and says, “Jeff I love Amazon, I just wish the prices were a little higher.” Or, “I love Amazon, I just wish you’d deliver a little slower.” Impossible.
So we know the energy we put into these things today will still be paying off dividends for our customers 10 years from now. When you have something that you know is true, even over the long term, you can afford to put a lot of energy into it.”
I recently attended a “leadership” seminar about (radiology) healthcare ecosystems and change. As with all virtual events since early 2020, discussion of the Covid-19 pandemic played an outsized role, and the nature of complexity and change were much pontificated about.
But no one over the course of two days—no one—mentioned the stability of the core mission. The strategic analyses—such as explicit or implicit utilization of SWOT—were happy to focus on anticipation and interception of perceived changes and threats, but no one spared a breath for what they thought wouldn’t change. We talked about trends in corporatization and productivity metrics, group consolidation, encroachment by midlevels and other specialties, downward reimbursement pressure, the push for 24/7 subspecialty staff coverage, lifestyle and burnout, and AI and data science.
To be sure, these and all other big changes are important, but you also can’t lose sight of the underlying purpose of the business in all the pivoting.
What can we say about medicine that is not going to change in 10 years? What is our stability north star?
(Yes this is a rhetorical question cop-out.)
Cal Newport, author of the beloved Deep Work (among others), writing in The New Yorker.
…most workers who are fortunate enough to exert some control over their efforts—such as knowledge workers and small-business entrepreneurs—tend to avoid working way too much, but also tend to avoid working a reasonable amount. They instead exist in a liminal zone: a place where they toil, say, for the sake of fixing a specific number, twenty percent more than they really have time for. This extra twenty percent provides just enough overload to generate persistent stress—there’s always something that’s late, always a message that can’t wait until the next morning, always a nagging sense of irresponsibility during any moment of downtime. Yet the work remains below a level of unsustainable pain that would force a change.
…
If you’re a professor, or a mid-level executive, or a freelance consultant, you don’t have a supervisor handing you a detailed work order for the day. Instead, you’re likely bombarded with requests and questions and opportunities and invites that you try your best to triage. How do you decide when to say no? In the modern office context, stress has become a default heuristic. If you turn down a Zoom-meeting invitation, there’s a social-capital cost, as you’re causing some mild harm to a colleague and potentially signaling yourself to be uncoöperative or a loafer. But, if you feel sufficiently stressed about your workload, this cost might become acceptable: you feel confident that you are “busy,” and this provides psychological cover to skip the Zoom. The problem with the stress heuristic is that it doesn’t start reducing your workload until you already have too much to do. Like Parkinson’s naval bureaucracy, which expanded at a regular rate regardless of the size of the Navy, this stress-based self-regulation scheme ensures that you remain moderately overloaded regardless of how much work is actually pressing.
“The Stress Heuristic” is a great term for people’s default strategy for avoiding more work: being literally too busy for more work.
But while saying ‘no’ is easiest when saying ‘yes’ is impossible, it forces you to live without margin. And margin—space in your life for yourself, serendipity, and the chance to chase down things of interest—is where the magic happens.
Even for academics, consider the words of psychologist Amos Tversky (whose work with Daniel Kahneman yielded the Nobel-prize-winning Prospect Theory and the crazy popular book Thinking, Fast and Slow): “The secret to doing good research is always to be a little underemployed. You waste years by not being able to waste hours.”
From “How to Leverage Language to Cultivate Your Creative Process” by Nicole He in Killscreen.
I had a concept in my mind—maybe I felt it emotionally, I had a feeling about what this thing is supposed to represent. Now what I’m saying makes it real. After that, I started responding to journalists the next morning. What blew my mind was this: the thing I wrote about my project became true about the project. Many of the things I would say, the lazier journalists would just copy and paste. Weirdly, seeing my own ideas in the media suddenly made them even more true—things about a project being about intimacy, about computers knowing more than we know about ourselves. All of this became true, because someone else was saying things about my project, but based on what I had said about my project.
There’s a lot to be said for putting in the effort of distilling a vague idea into a clear concept and writing it down (and, yes, maybe even sharing it). Not just for a specific project, but for you and your whole career.
There is power in storytelling, both internally and externally.
And, perhaps weirdest of all is that—on the whole—you get to define your own narrative.
We all use mental models (heuristics, rules of thumb) across a host of simple and complex problems. They often work; they sometimes don’t. You shouldn’t (and can’t) avoid having and using them, but you should be aware of them (and their limitations).
“The Influence of the Availability Heuristic on Physicians in the Emergency Department” is a cute little paper demonstrating recency bias in real-life practice:
Heuristics, or rules of thumb, are hypothesized to influence the care physicians deliver. One such heuristic is the availability heuristic, under which assessments of an event’s likelihood are affected by how easily the event comes to mind. We examined whether the availability heuristic influences physician testing in a common, high-risk clinical scenario: assessing patients with shortness of breath for the risk of pulmonary embolism.
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The sample included 7,370 emergency physicians who had 416,720 patient visits for shortness of breath. The mean rate of pulmonary embolism testing was 9.0%. For physicians who had a recent patient visit with a pulmonary embolism diagnosis, their rate of pulmonary embolism testing for subsequent patients increased by 1.4 percentage points (95% confidence interval 0.42 to 2.34) in the 10 days after, which is approximately 15% relative to the mean rate of pulmonary embolism testing. We failed to find statistically significant changes in rates of pulmonary embolism testing in the subsequent 50 days following these first 10 days.
Of course, one of the biggest components of the availability heuristic in real life isn’t just how recent the event is (though that’s what’s measurable in this sort of dataset). It’s anything that makes certain events easier to recall. This is, for example, why some of our mistakes or surprise diagnoses can have an outside impact on our practice. We remember that unexpected PE we didn’t see coming more than the many more common examples of the negative CTA.
(Further reading on availability bias: Farnam Street.)
From Brian Alexander’s The Hospital: Life, Death, and Dollars in a Small American Town:
(Phil Ennen, one of the main characters, is the CEO of a struggling small-town community hospital in Bryan, Ohio.)
That was the world where Ennen and the vice presidents now found themselves as they listened to consultants they were auditioning to help create a strategic plan. “Transformational changes dictate that leaders within the physician enterprise focus on enterprise sustainability.” So they drove. They drove at “solutions.” The consultants offered entire suites of solutions. The solutions could be “leveraged” toward “accelerating the journey to risk capability.” There’d be “applied analytics” in “the Achieve solution set,” which was “purposely designed to assist physician enterprise leaders to align compensation models and strategic priorities, maximize productivity.” “The Achieve solution set not only drives current performance improvement but also establishes the forward-looking strategic, financial and operational structures to provide for the future risk capable physician enterprise.” Change was driven. Results were driven. Everything was “forward-looking” and “dynamic.” Zoom!
…
It wasn’t just about style. Ennen thought the world—and especially the world of medical care—was complicated enough without further obscuring meaning and understanding by spouting terms of the business dark arts. Such terms were deliberate obfuscations, thrown up as fortress walls to keep the uninitiated outside and throwing cash over the walls to the mysterious magicians inside so they’d shout down their wisdom. Now, though, like it or not (and he didn’t), Ennen and the others were knocking on the gates of the consultants.
What a great line by Alexander: “Such terms were deliberate obfuscations, thrown up as fortress walls to keep the uninitiated outside and throwing cash over the walls to the mysterious magicians inside so they’d shout down their wisdom.”
The book came out in March of this year and is a meticulous deep dive and narrative take of modern American healthcare through the lens of small-town America as a community hospital struggles to stay independent and survive.
It’s often said that large organizations are difficult to steer and slow to change course, but that’s only part of why they sometimes act in seemingly inexplicable ways. There’s another more insidious reason, and that is conflicts of interest, not just within leadership but also in the changing demographics of the membership.
A passage from “Value Chain: Where Radiologists Should Put Their Focus in Threats Against Income” by Seth Hardy MD MBA in Applied Radiology:
So, while private/public equity firms can use leverage to amplify profits to the upside, leverage has an opposite effect when gross income is in decline. Any cuts to reimbursement would be truly devastating to these firms’ employees; since the debt holders get paid before the radiologists, the impact on employed radiologists’ salaries may be significant. As equity-employed radiologists make up a greater share of dues-paying members within organized medical societies, it is easy to understand why the proposed CMS cuts were characterized as draconian by those societies. But a clear understanding of value chain by physicians is increasingly critical to evaluate the rhetoric of our medical society leadership.
I am now a partner in a physician-owned independent radiology practice. A CMS paycut would mean that we earn commensurately less money—not that we will become insolvent.
That should count for something when choosing where to work.
In 2003, Charlie Munger gave a lecture titled ‘Academic Economics: Strengths and Weaknesses, after Considering Interdisciplinary Needs,’ at the University of California at Santa Barbara.
It’s a pretty good read.
He mostly discusses problems with economics as a soft science that desperately wants to be a hard science.
Medicine is also surprisingly soft. I’ve replaced some words with medicine in several paragraphs to illustrate how cross-domain the problems with medical practice can be:
Yet medicine, like much else in academia, is too insular.
The nature of this failure is that it creates what I always call, “man with a hammer syndrome.” And that’s taken from the folk saying: To the man with only a hammer, every problem looks pretty much like a nail. And that works marvelously to gum up all professions, and all departments of academia, and indeed most practical life. The only antidote for being an absolute klutz due to the presence of a man with a hammer syndrome is to have a full kit of tools. You don’t have just a hammer. You’ve got all the tools. And you’ve got to have one more trick.
This is an argument for a broad foundation in medicine before specialization. The more siloed we are, the less we can draw on different toolsets to help patients.
This is also an argument against fee-for-service. If doctors and hospitals can generate the most money with a certain hammer, that hammer is likely to be used disproportionately.
A special version of this “man with a hammer syndrome” is terrible, not only in economics but practically everywhere else, including medicine. It’s really terrible in medicine. You’ve got a complex system and it spews out a lot of wonderful numbers that enable you to measure some factors. But there are other factors that are terribly important, [yet] there’s no precise numbering you can put to these factors. You know they’re important, but you don’t have the numbers. Well practically everybody (1) overweighs the stuff that can be numbered, because it yields to the statistical techniques they’re taught in academia, and (2) doesn’t mix in the hard-to-measure stuff that may be more important. That is a mistake I’ve tried all my life to avoid, and I have no regrets for having done that.
This gives rise to the classic Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure.
We shouldn’t confuse measurability with importance. In many cases, the measure is a poor surrogate for what we really care about or can be gameable to ultimately negative downstream effects. An example? Patient satisfaction.
Too little attention in medicine to second-order and even higher-order effects. This defect is quite understandable, because the consequences have consequences, and the consequences of the consequences have consequences, and so on. It gets very complicated. When I was a meteorologist I found this stuff very irritating. And medicine makes meteorology look like a tea party.
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Extreme economic ignorance was displayed when various experts, including Ph D. economists, forecast the cost of the original Medicare law. They did simple extrapolations of past costs. Well the cost forecast was off by a factor of more than 1000%. The cost they projected was less than 10% of the cost that happened. Once they put in place all these new incentives, the behavior changed in response to the incentives, and the numbers became quite different from their projection. And medicine invented new and expensive remedies, as it was sure to do. How could a great group of experts make such a silly forecast? Answer: They over simplified to get easy figures, like the rube rounding Pi to 3.2! They chose not to consider effects of effects on effects, and so on.
Short-term thinking is bad at both micro and macro levels.
On the micro level, the patient’s care doesn’t end when they leave the clinic or hospital. It keeps going throughout their life. And each episode of care from each different provider doesn’t exist in a vacuum. It interfaces with every other bit of care they get. The combination of direct patient care, socioeconomic factors, and education is a complicated mess at baseline.
But decisions lead to decisions, and outcomes further affect outcomes. We treat at the n of 1 and often at the timepoint of right this second. Missing the forest for the trees is easy to do when your patient is usually coming to you for a tree and you are also paid to look at the tree.
On the macro level, Munger’s Medicare example above. Or, the more recent news, approving a multibillion-dollar a-year Alzheimer’s drug with no evidence that it works: It won’t just cost billions of upfront, it will result in other companies diverting resources in a rush for me-too drugs that also may not work to get a slice of a massive market and likely cost still billions more while potentially resulting in less novel drug development. We think in linear terms but systems often work exponentially.
Here’s a little exercise adapted from “You Really Need to Quit Twitter” in The Atlantic:
Step 1: Take the Simone Weil essay “On the Abolition of All Political Parties” and replace the word “parties” with something that maybe shouldn’t exist, like social media:
The mere fact that social media exists today is not in itself sufficient a reason for us to preserve it. The only legitimate reason for preserving anything is its goodness. The evils of social media are all too evident; therefore, the problem that should be examined is this: Does it contain enough good to compensate for its evils and make its preservation desirable?
Step 2: (Oh well I still have my Twitter account).
A passage about limited resources and optimizing imaging from The Emergency Mind: Wiring Your Brain for Performance Under Pressure by Dan Dworkis MD PhD:
Within the broader context of your responsibility however, there frequently will be significant variability in the relative urgencies of individuals being imaged. Some patients—like a person seemingly experiencing an acute stroke—do need to be scanned immediately. Others—such as a patient with abdominal pain, stable vitals, and a reassuring physical exam—while no less “deserving” of those resources, would receive nearly equal benefit from being scanned now as in an hour from now. Optimizing care across the field in this context would involve prioritizing CT scans for those patients who would receive outsized benefits from immediate imaging, even if this makes some other patients wait longer.
Put a different way: If everything is stat, nothing is stat.
Stat abuse is one of those crimes especially tempting to inpatient teams in busy hospitals. It’s natural to want answers (and dispo) as soon as possible, and we assume that we will get them faster if we increase the priority of the exam.
All a clinician knows is that sometimes something ordered routine takes forever and that ordering stat should generally result in it being performed faster. They may not even care if the read is prioritized in all cases so long as the patient is freed from the waiting and future transport.
It’s also human nature for there to be a distribution with certain individuals ordering an outsize proportion of “stat” exams. The negatives of over-ordering or inappropriate priority are almost always placed on other staff. In a zero-sum game, selfish behavior may be an optimal choice for individual success even if it makes the system less efficient overall. Hospitals very rarely scold their staff for such abuses.
I don’t think most clinicians even have any idea where along the spectrum their behavior falls. Knowledge of outlier performance one hopes might curb excesses, and that data would certainly be helpful for individuals to know (presuming those individuals are capable of feeling shame and said shame functions as a deterrent). Such information would have to be long-term and stratified well to be meaningful (we should expect different levels of stat exams as a fraction of orders from different hospital units, for example). Otherwise, data are dismissible.
Ultimately, pleading and punishment are often ineffective and/or undesirable.
A more helpful approach would include data to guide decision-making on a case by case basis:
The EMR should show in real-time the expected wait for different study types based on the current queue and exam types pending, both inpatient and outpatient (i.e. how many unnecessary exams are obtained during an inpatient stay due to fears of long delays for outpatient follow-up?). Yes, a routine study may unexpectedly get bumped further down the line, but a smart system would incorporate predictions based on the current patient census, admission diagnoses, time of year, and whatever else some machine learning algorithm would include its impenetrable black box of Skynet code.
It would be extremely helpful for all parties to know if an MRI should be expected today or tomorrow, sometime this afternoon or more likely at 3 am.
And so, yes, of course, people are working on this in the machine learning world. But hurry up. I for one will continue to welcome our AI overlords and their promised efficiency gains, but I’m still waiting.