Ordinary Abundance is a lovely little visual essay. Go ahead, feel good about human progress today.
From independent interventional radiologist Dr. Kavi Devulapalli’s recent article, “Coming to Terms With Reality“:
I can build a program that did not exist before I got there, develop the referral relationships, create the clinic, generate the patients, perform the procedures and produce millions of dollars of new facility economics. The hospital can measure that value, present it to a board, use it to justify investment and congratulate itself on the growth of the service line. When it comes time to figure out what I should be paid, however, we are suddenly talking about how many wRVUs I produced, what fair market value is for call coverage, how many hours I spent doing administrative work, what other employed IRs make and what percentile my compensation falls into.
This is why I increasingly describe the game as rigged. I don’t mean there is some guy at CMS sitting around trying to figure out how to personally ruin my life. What I mean is that once you understand how the pieces fit together, the result becomes pretty obvious. CMS separates the physician economics from the facility economics. The hospital owns the facility and employs the physician. The physician can then create tremendous additional enterprise value for the hospital, while Stark and AKS make it difficult to simply reconnect physician compensation to the downstream economics that physician helped create. Compensation gets pushed back toward the defensible value of physician labor, which is then informed in part by surveys of what other physicians operating inside the same system are being paid. If that doesn’t strike you as at least somewhat circular, I don’t know what to tell you.
Value has always been hard to define, but even if RVUs were a perfect measure of physician work in direct patient care, they would never, ever tell the complete story. The hospitals know this, even if they pretend not to during negotiations or their usual budgetary gerrymandering.
Back in 2018, fresh out of training, I was asked, along with some physician finance bloggers, to contribute an answer to an article about early-career financial mistakes (my post about that post is here). That site is now defunct, but my answer (referring to my work as a student and pre-moonlighting resident) at the time:
“The ‘correct’ answer is usually to spend less on a car, not buy a house, put more away for retirement, and get disability insurance. But looking back, my biggest real regret is not being more purposeful with my discretionary time. Time outside of work obligations has both a monetary and nonmonetary value, and I was very free with spending too much of it to ‘maximize’ my income at rates that are trivial to me now. I was worth more on a per-hour basis back then, even if I didn’t realize it.”
On the same note, a fantastic short visual essay on writing: Do It 14,000 Times Slower With This One Trick
“Please use AI” is a poem by Shawn Smucker.
In his 1971 book, Extraordinary Tennis for the Ordinary Player, Dr. Simon Ramo (also known as, of all things, the “father of the intercontinental ballistic missile”) popularized the concept of Loser’s Games and Winner’s Games:
The notion is that professional tennis is a Winner’s Game, where you win through high athletic achievement and hitting incredible shots, whereas amateur tennis is a Loser’s Game, where you win by avoiding unforced errors (i.e. you win by “not losing”).
Medicine, and especially radiology—which is especially prone to post-hoc criticism thanks to the permanent unchangeable nature of the images themselves—is much more the art of avoiding unforced errors than the art of hitting home runs. After all, common things are common. Horses, not zebras, as they say.
This is the purpose of search patterns and good habits: to build and achieve consistency.
It remains to be seen if and how and when AI might change what kind of game medicine is.
From Douglas Hoffman’s “Interface Theory of Perception” as described in 2019’s The Case Against Reality:
The purpose of a desktop interface is not to show you the ‘truth’ of the computer — where ‘truth,’ in this metaphor, refers to circuits, voltages, and layers of software. Rather, the purpose of an interface is to hide the ‘truth’ and to show simple graphics that help you perform useful tasks such as crafting emails and editing photos. If you had to toggle voltages to craft an email, your friends would never hear from you. That is what evolution has done. It has endowed us with senses that hide the truth and display the simple icons we need to survive long enough to raise offspring. Space, as you perceive it when you look around, is just your desktop — a 3D desktop. Apples, snakes, and other physical objects are simply icons in your 3D desktop.
Interesting.
Steven Pinker essentially summed up the argument well back in 1997:
We are organisms, not angels, and our minds are organs, not pipelines to the truth. Our minds evolved by natural selection to solve problems that were life-and-death matters to our ancestors, not to commune with correctness.
As British statistician George E.P. Box once famously said, “All models are wrong, but some are useful.”
An attempt to model the ROI of using narrow AI tools like PE and brain bleed detection, from “Efficiency and Financial Gains From Artificial Intelligence Algorithm Implementation” in JACR:
The pulmonary embolism triage algorithm also reduced interpretation time, but the magnitude of savings was insufficient to offset AI-related costs. The observed time savings of 0.83 min per case produced a negative contribution margin change from 6.0% to −10.1% and a negative return on invested capital of −76.6% (Table 2). In sensitivity analysis, the algorithm would require a total reduction in interpretation time of approximately 3.55 min per case, or a decrease in annual AI cost from $200,000 to approximately $47,000, to become financially favorable relative to baseline. These results highlight that even measurable efficiency gains may not be sufficient when algorithm costs are high, case volumes are limited or baseline interpretation times are already relatively efficient.
The actual modeling the authors used contains many assumptions that may or may not generalize, but the broader point they make is that we should be able to measure whether any tool is worth it. Both in and out of medicine, many AI projects are outright failures. (The brain bleed tool they tested was worth it, in their analysis.)
A generally insufficiently addressed question for all AI-makes-you-more-efficient discussions is what is the leakage of time saved? What fraction of that expensive deep breath gets applied to the next case? Is the increase in productivity really just taking that time and cranking the hamster wheel perfectly to add in the exact amount of work to fill that gap? If so, does that scale as more and more tools are added? Is that effect durable over time, or do we partially revert after the Hawthorne effect wears off? Do we enjoy our tool-assisted work more or less, and how will that change over time?
A broader financial question for these types of narrow, mission-critical tasks is what parts of the task/job are expensive and/or inefficient? Doing the work or verifying the work or both? AI might help you avoid some mistakes as a second reader, but that’s not the economic model anyone is interested in.
Three separate quotes from different parts of Nikhil Suresh’s “AI Mania Is Eviscerating Global Decision-Making” that work together:
But the broader reality is so much worse: people who have no background in the technology at all actually believe what they are saying. As a general rule you should avoid getting into business with a liar, but if you must, you can at least reason with them even if only in private. A true believer is much more threatening because they are impervious to even inducement by self-interest.
[…]
This is to say that we’re facing a coordination problem around executives being honest around the AI gains they’ve witnessed – if they co-operate, they keep their jobs. If they defect, they will possibly be fired by their embarrassed peers (who have now been implicitly called liars, cowards, or incompetents) and then replaced with someone that will toe the line anyway. If they could all admit the truth at once there might be some hope, but there is no way to coordinate that event.
[…]
The net result of this is that almost every large organisation that I am aware of is no longer able to focus on anything important, unless they are one of the (very) few organisations where AI happens to address their highest priorities. They cannot buy sensible software, hire competent talent, communicate honestly with executives about the state of projects, or undertake any sort of sensible initiative.
The whole thing is very much worth reading. (Those of you who read too many articles on the internet may remember his 2024 gem, “I Will !$%!!$% Piledrive You If You Mention AI Again”)
At least the cryptomania BS a few years ago was unintuitive enough that it was ignored by most businesses despite the desperate pivots and grifting. Adding a useless AI chat feature or calling your platform “AI native” is an easier sales pitch and management obsession than “It’s the same thing…but on the blockchain.”
Most leaders barely understand their own business, let alone the real impact of AI in recent years on their industry, the current contours of AI abilities, or the likelihood of purchased solutions and implementations maturing gracefully in a rapidly changing field. Most people don’t even know if the hard part of the job is generating the answer or verifying it. AI is magical, and yet we’re still firmly in the everyone-is-lying part of the hype cycle.
He writes a cogent summary in the middle of the article:
“Please dear God, do not let it be on earth as it is on LinkedIn.”
In “The People Who Will Thrive in the AI Age,” David Brooks argues that the tolerance/desire for mental effort is the distinguishing characteristic amongst people.
One group:
The Reluctant Optimizers. People with a medium need for cognition will understand that AI might hollow them out. That prospect will really bother them. They will resolve, earnestly and with good intentions, to not let themselves fall victim. But in the crowded and stressful rush of everyday life, they will get sucked in. Their resolve will fail and they’ll become overreliant on the bots.
It seems to me that modern medicine has essentially primed basically everyone involved in healthcare to fall into this camp.
Later:
The crucial task before us is to cultivate people’s desire to seek out cognitive complexity. Not to go all Joseph Campbell on you, but the essential challenge is: How do we train people to see their life as a hero’s journey in which they take on difficult missions that they may fail at and that will certainly involve pain and suffering? How do we form people so they have an explorer’s heart, a willingness to endure, an ability to struggle on, even when their body and mind are telling them to give up, to reach new destinations and figure stuff out?
…
Many of our schools do a decent job of crushing students’ desire for mental effort. Every minute that a kid sits bored in a classroom crushes their desire. Extrinsic rewards, such as grades, do so because extrinsic desires tend to crowd out intrinsic ones. Grade inflation crushes desire by making everything too easy. Many of our systems have been created by rationalists to focus on the declarative level of the mind, the part that learns facts and considers arguments; they are often oblivious to the damage they are doing in the dark forests, the deeper levels of the mind where motivations emerge.
Fortunately, schools and organizations can also inflame desire. The most straightforward theory of motivation is known as self-determination theory, founded by Edward Deci and Richard Ryan. People feel motivated when they are put in situations that give them autonomy (I’m in control of my choices), competence (I’m developing my skills), and relatedness (people here care about me). In my experience, motivation increases with admiration, such as when students are confronted with great people or great works of art. Motivation also increases with apprenticeships, such as when a mentor not only teaches a person how to engineer, but also how to be the kind of person who loves engineering.
It also seems to me that human relationships and a truly forged identity are the only way most people will have any chance of embracing that helpful, crucial friction and combating the siren call of easy mediocrity.