Left-Digit Bias and Other Random Acts of Medicine
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Left-Digit Bias and Other Random Acts of Medicine

; Abraham Verghese, MD; Anupam B. Jena, MD, PhD

Disclosures

September 07, 2023

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This transcript has been edited for clarity.

Eric J. Topol, MD: Hello. This is Eric Topol, here with my co-host, Abraham Verghese, and this is the Medicine and the Machine podcast. We're pleased today to have as our guest Bapu Jena, a physician and economist at Harvard who has written, with his colleague Christopher Worsham, a fascinating new book called Random Acts of Medicine. Bapu, welcome.

Anupam B. Jena, MD, PhD: Thank you for having me.

Topol: This is a very interesting book. I think the whole medical community, no less the public, will find it extraordinarily interesting. You start off with what you call a natural experiment. Some people will say, "Oh, that's just an observational study." Can you tell us what you mean by "natural experiments" and how they differ from — or are potentially better than — randomized studies?

Jena: Randomized experiments are quite common in medicine. When someone takes a drug, if we're lucky, it's been subject to a randomized trial, where investigators randomly assign a group of people to one drug, and another group to another drug or a placebo. That allows us to say something about the causal effect of getting that drug, because everything else is being held constant by virtue of the randomization.

The opposite of that is what we think of as a straightforward observational study, in which you look at people who take that drug in the real world, and you compare them with people who take the other drug. You hope that the factors that led them to take the drug are not correlated in some way with the outcome of interest, and that any differences are related to the mechanism of action of the drugs.

That usually is not the case because selection is involved. People choose to take certain medications — or someone recommends a medication to them — for a host of reasons, some of which a researcher can observe but many of which a researcher cannot observe. We call that confounding. That's why observational studies are not often relied upon by the US Food and Drug Administration.

Now, there's something in between, which we call natural experiments — a scenario where nature essentially randomly assigns people to one intervention vs another. We use the term "quasi-randomized" because it doesn't occur at the hands of an investigator. With this method, we think we're getting closer to the causal effect of the intervention.

It's observational in the sense that we are looking back at data that already exist. That part is true. But it's not the same as an observational study because we're taking this issue seriously, that the way a person happens to be given an intervention in the real world is typically not random. So, we want to find situations where it is as good as random.

Abraham Verghese, MD: I want to say how much I enjoyed your book. To me, it was a succession of fascinating stories. I'm marveling at the way your mind works. It made me think of this not very well-known tradition of MD economists. We had Alan Garber here as our colleague for a while, and then he had a cadre of MD-PhD economists. You're clearly in that tradition. Talk a bit about what led you into getting an MD with a PhD in economics.

Jena: That was actually quite random. I'd spent some time in college working in a basic science lab, and I wanted to do an MD and a PhD in something like immunology or cell biology. But I also studied economics in college. I thought to myself, Wow, I'm applying to medical school. I need to round out my background a little bit and study a "humanity." Economics is about as far from the humanities as I could think of, but that was my logic at the time.

Fast-forward a few months when I'm applying to medical schools. I visited the University of Chicago, and the director of that MD-PhD program noticed that I had studied economics. He asked me, in sort of an offhand way, "Would you want to do your PhD in economics instead?"

That's how it happened. It was sort of random in that respect, and it's taken my life down a completely different path from where it otherwise would have gone.

Topol: My favorite chapter was on left-digit bias. In fact, the name of the chapter is "What Do Cardiac Surgeons and Used-Car Salesmen Have in Common?" The car is priced $1 less, illustrating left-digit bias.

You presented many graphs of what has been known as discontinuity, whether it's bypass surgery, kidney transplants, or opiate prescribing; one after another, you show this left-digit bias. Can you talk about that? How come we're so stupid?

Jena: I think it's human nature. Humans aren't perfect and doctors are humans, so there's the transitive property — doctors aren't perfect.

At the grocery store you see a bag of Doritos priced at $1.99. The reason it's $1.99 is because the mind looks at that leftmost digit (a "1") and that seems cheaper than $2.00, even though it's only a penny cheaper. You don't see stores, for example, relying on pricing of $1.63 vs $1.64 to try to shift consumers to purchase a product. It's something specific about that left digit.

It's not surprising that you could trick a human being into purchasing a bag of Doritos at the margin that they may or may not have purchased had the price not been $1.99. What surprised me, though, is that you would see the same sort of thing happening in a high-stakes decision, one that is arguably well thought out and for which the implications are enormous.

In the book, we talk about some of our own work, which looked at people who came to the hospital with a heart attack. And for some of these people, cardiac bypass surgery makes sense. We show that if you happen to come to the hospital just a week shy of your 80th birthday — let's say 79 years and 50 weeks old — you are more likely to receive cardiac bypass surgery than if you came 3 weeks later, when you are 80 years and 1 week old.

This occurs because the doctor looks at the patient and says, "They're in their 70s" or "They're in their 80s." The older patients are, the less likely doctors want to do invasive things to them. That was our finding. It is interesting to me because it shows that the same sort of behavioral heuristics that apply in other parts of our lives also happen in high-stakes settings.

It also shows us how behavioral economics can create these quasi-experiments where people are, by chance, randomized to undergoing coronary artery bypass graft surgery or not. Then you can look at the outcomes a year later and see what happens.

Topol: At many points in the book you talk about the features that define best-performing physicians. I'd like you to comment on two of these. And then later we'll get to the cardiology convention.

Can you comment on your findings about women and age, and the differences between hospitalists and surgeons where older may be better?

Jena: Sure. Let me start with the age study. If you're a patient and you see a young doctor walk into the room, you might be concerned that the young doctor doesn't have a lot of experience in the practice of medicine.

If I'm being honest with myself, as I think about trying to find medical professionals for my family or friends, I'm not typically looking at doctors who are fresh out of residency training or fellowship. I'm looking years out because I have the perception that experience matters. But ultimately, that does become an empirical question.

It's not an easy one to solve, because if patients believe that older, more senior doctors are more experienced and will provide better outcomes for them when they are particularly sick, who are those doctors going to attract in the real world? They're going to attract patients who are sicker. So, you can't simply look at older vs younger doctors and say anything about the causal effect of being seen by one or the other.

You must construct a scenario where people are seen by older or younger doctors by chance. That's where the hospitalists come in. In that scenario, if you have a 45-year-old doctor who works on Mondays, a 35-year-old doctor on Tuesdays, and a 65-year-old doctor on Wednesdays, that's all random. The patient going to the hospital with chest pain or shortness of breath doesn't go there knowing that the 35- or the 65-year-old doctor happens to be in the hospital that day. It's random.

That allows us to uncover a causal effect, and we see two things. One is that the older the doctor is, the worse the outcome of 30-day mortality. How likely are you to survive 30 days after that hospitalization?

That effect starts pretty soon after residency. It's not as if we're comparing 80-year-old and 35-year old internists who work in the hospital. For every 5 years beyond residency, you see a bump in mortality.

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