Chapter 1: What is the main topic discussed in this episode?
Over the past few episodes in this Freakonomics Radio guide to getting better, we've looked at a variety of things that may produce a longer and healthier life. Nutritional supplements, faster drug approvals, figuring out the secrets of the gut microbiome. And today, in the final episode of this series, we'll look at something that intersects with all of those things and maybe a trillion more.
Today's topic, how artificial intelligence will change healthcare. And why is the healthcare system in need of change?
Chapter 2: Why is the healthcare system in need of change?
If you look closely, you'll see a bizarre split. The advances in medicine and medical technology over the past century have been mind-blowing. But the way these advances are delivered to actual patients can be also mind-blowing, but in a bad way.
I have the ability to put a patient on heart-lung bypass where their organs are literally failing and we're able to keep them alive. It's truly some of the most ambitious technology humanity has ever created. And yet the way that I find out that someone had a heart attack is still through a pager. And then I have to go and say, hey, who here is having the heart attack?
The healthcare system has so much technology slop that it can be hard to see just how good the actual medical technology is. But that may be about to end. If you think about it, this is the biggest experiment in the history of medicine. And the experiment is already underway.
The moments where I feel like I'm really doing science is when I genuinely do not know the answer to the question, but I know it's important to answer it.
Today on Freakonomics Radio, AI and a giant leap into the future of healthcare.
This is Freakonomics Radio, the podcast that explores the hidden side of everything with your host, Stephen Dubner.
We could probably make 10 episodes looking at AI in healthcare, but if we want to do it in a single episode, which we do, it's helpful to speak to someone who is able to frame the biggest questions well. Someone like this. I'm Dr. Robert Wachter. Although he says we should call him Bob. and I'm professor and chair of the Department of Medicine at the University of California, San Francisco.
And what does that job entail? What that is is running a large department of about 1,000 doctors, everything from geriatricians and primary care doctors to cardiologists and oncologists, and we do research, education, and take care of lots and lots of patients. You're still a practicing clinician as well. Is that true? Correct.
About one month a year, I do this thing, a field that I actually started called hospitalist. So about one month a year, I take care of very sick people in the hospital. What's your medical specialty by training? I trained in internal medicine, then did fellowship training in epidemiology and policy and ethics.
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Chapter 3: What are the historical challenges of AI in healthcare?
So why is health care delivery still so sloppy? There are a lot of reasons that we are pretty static. The fixed costs are very high to get into the business. It's almost impossible for a startup to build and launch a new hospital. The incumbents are quite powerful, although you could argue that's true for a lot of other industries, but doctors, nurses, et cetera, are powerful.
The economics are really funky. If Amazon or Netflix or you name your favorite disruptor comes up with a better mousetrap, the relationship is largely between a customer and the vendor. And the customer says, this is better or cheaper or whatever, I'm going to buy it. In healthcare, you have this assorted mishmash of insurance companies, businesses, government.
And also because healthcare is so important and we have the capacity to kill people if we don't do it right, it is highly regulated, which is yet another barrier for innovators to come in and disrupt us. We like technology, but we like it in very, very specific ways. We have not embraced it as a mechanism to make care better and safer and less expensive. You call your book A Giant Leap.
I want to understand this concept of the giant leap. My sense is you're arguing that healthcare... Yeah. The quote I like is Hemingway's quote from The Sun Also Rises. Now, 100 years ago, one of the characters goes bankrupt and another character says, how does a man go bankrupt? And famously, he says, two ways, gradually, then suddenly. So that's us.
I mean, I think we have the gradually part down pat. We now have computers, which is great, but we are the largest users of fax machines today. In the country, we finally have ditched the pagers after the drug dealers did. They were way ahead of us. So, yeah, we are very sluggish in adopting new tools, but we have gone digital.
I wrote a book 10 years ago called The Digital Doctor, which was really about our transition from paper to digital. That book is a very grumpy book. It's like, how the hell did we go from paper to digital and in some ways make things worse, in some ways make the lives of both patients and doctors harder? Just digitizing the record helped in certain ways.
Got rid of doctor's handwriting, the kind of perennial joke. You know, when I do an electronic prescription, it can land at Walgreens or CVS. That is massively better and safer. Two people can look at the chart at the same time. There are lots of good things about it, but it was not enough to transform medicine. And in some ways, as I said, it made it worse.
The giant leap really is the combination of the magic of the new AI meeting a healthcare system that's in desperate need of change and everybody knows it.
We really are about to have our suddenly moment when healthcare is actually transformed after tiptoeing our way toward this over the last 10 or 15 years to make it better and safer, more accessible, more satisfying for everybody, both patients and clinicians. And I think eventually less expensive, although that's hard to ask.
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Chapter 4: How is AI currently being implemented in healthcare?
administrators, tech firms, investors, et cetera, et cetera, et cetera. Can you just talk about what this journey slash process was like for you, why you decided to undertake it, and then who you actually did spend time speaking with? The things I was reading were written by technologists, and I don't think they understood the big picture, the policy, the politics, the economics.
And so my wife, who's a journalist and writes for The New York Times, said, the only way you're going to get this right is to do it journalistically. And I said, what does that mean? She said, you're going to have to go and talk to a lot of people. Who did I talk to? I tried to find interesting companies and interesting people doing cool stuff.
And when I spoke to them, I asked them, who else should I speak to? And they told me about other interesting people. I know the world of clinical medicine well. I know the world of academic medicine well and medical education. I live in San Francisco, so I'm surrounded by technologists. I advise a bunch of tech companies.
So in each of those areas, I knew a fair amount to get started and knew some of the players, but I had to go deeper. The first chapter in the book is called An Overnight Revolution, 50 Years in the Making. Can you just talk for a moment about what happened during those 50 years, the successes, the failures, and why it's been such a slow boil?
Yeah, I can do it quickly if we talk successes, and it'll take longer to do failures. Slow boil is a couple of things. One is people are treating AI in healthcare like it's new. It is not. In the 70s and 80s, AI became a thing, and there was a lot of interest in medicine and artificial intelligence. If you think about it, what does a doctor do?
What did I spend eight or 10 years going to school and residency and fellowship learning to do? It's be intelligent to take a whole body of information and symptoms and lab tests and all that, match it against a body of information, the medical literature and textbooks, and come up with a diagnosis and a treatment.
So AI was very exciting, but the AI of the day was not ready for prime time for a few reasons. First of all, it was the old if-then AI. If a patient has a sore throat and swollen lymph nodes and a fever, they probably have strep throat or mononucleosis. That works fine for very simple problems, falls apart very quickly, faced with the complexity of real medicine.
The second was all of our data was on paper. Therefore, if you wanted to use these fancy new AI machines, you had to go to a separate computer and type everything in. So both of those caused the field to flame out, and AI went away from medicine for about 40 years. Was this imaging as well or no? It was early for imaging. Imaging started in the 80s and 90s.
It was largely around the cognitive work of doctors. Part of the problem was they started on the hardest problem, and the hardest problem is diagnosis. I remember speaking to one of the early leaders at the time who was a professor at Stanford. These are not dummies.
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Chapter 5: What are the risks of sharing medical records with AI?
These are MDs and PhDs in computer science. He said, why did you focus on diagnosis as the first thing to tackle? He said, we weren't naive about the complexity. It was just the most interesting problem. You could understand that. These were innovators. They were at the cutting edge. They really weren't thinking about practicality. That was an important lesson for today.
You don't start on the hardest problem and one with the highest stakes and one if you get it wrong, you can kill somebody. You start on low-hanging fruit. You need to get buy-in and get trust from everybody, patients and doctors and nurses. I think we're not making that mistake this time, but that flamed out. Then IBM Watson beat the Jeopardy champions in 2011.
You may remember that moment when Watson, a supercomputer trained to play Jeopardy, competed against a pair of human Jeopardy champions, including Ken Jennings.
I've never said this on TV. Chicks dig me for 200, please, Jimmy. Kathleen Kenyon's excavation of this city mentioned in Joshua showed the walls had been repaired 17 times. Watson. What is Jericho? Correct. 400, same category. This mystery author and her archaeologist hubby dug in hopes of finding the lost Syrian city of Irkesh. Watson. Who is Agatha Christie? Correct.
Watson won $77,000 in that competition. That was a nice payday, but of course Watson cost billions to develop and IBM had much higher ambitions for it than winning at Jeopardy. I remember watching that and thinking, well, we're all toast. And when Watson then tried its hand in healthcare, which was the first industry that it tried to work on, it completely flamed out.
IBM did enter Watson into some high-end partnerships with MD Anderson Cancer Research Center, for instance. But Watson just didn't turn out to be very useful. Some of its answers were obvious, others dubious. And it was very expensive. In the end, IBM dismantled Watson, keeping some parts and selling off the rest. Again, not ready for prime time.
And then the sort of big deal in medicine was about 15 years ago, we all went from paper records to these huge software systems called electronic health records. In 2008— fewer than one in 10 American hospitals or doctor's offices had an electronic health record. By 2016, fewer than one in 10 did not.
In the space of a very short amount of time, we went from basically a paper-based industry where the idea of using advanced data analytics and machine learning and all that was impossible because all the data was on pieces of paper to an industry that had its information in digital form. What was disappointing about that was many of us, including me, naively thought that's the ballgame.
If we get our data in digital form, we'll be ready to innovate and do all this stuff like Amazon and Netflix and Apple and medicine will be better and safer and cheaper. It didn't work. The lesson that I took from that era was a term coined by Eric Bernoffson at Stanford called the productivity paradox of IT.
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Chapter 6: How does AI enhance patient diagnosis and treatment?
And why did that happen? Because the computer became this enabler of all of these outside entities who used to have no ability to influence what the doctor did because I was scribbling on a piece of paper, now had a way of making me check 12 boxes about did I examine nine body parts and did I ask you if you wear seatbelts? Do you exercise and all that?
All noble questions, but now there was a forcing function that you could make the doctor record all this stuff, and so people did. And importantly, when we send a bill off to the insurance company, the amount of money we get paid is partly related to the nuances of how I record the note. Which creates some perverse incentives right there.
Totally, totally ridiculous incentives to say the right words in order to get the best bill. And then a few years after that, federal legislation mandated that patients could not only see their basic information and maybe their medications, but actually could read my note and see their x-ray results and see their lab results.
There was absolutely no information to help the patient figure out what any of that meant or even to make an appointment. Other than to maybe forward the results back to the doctor and say, I'd like an explanation, please, which just sludges up your inbox even more. The companies did what seemed logical.
They put a little button at the bottom of the screen that said, send a message to your doctor. Lo and behold, patients being normal human beings click that button all the time. Electronic health records have led to a huge increase in what is called pajama time for physicians. We talked about that in an episode called The Doctor Won't See You Now, number 650.
The American Medical Association, in a recent survey, found that roughly 20 percent of physicians spend eight or more hours a week outside the office wrestling with electronic health records. But it seems that a new day may finally be dawning.
The first really widespread use of AI in healthcare now, and really the one that took over very quickly in a year or two, is what's called an AI scribe or ambient intelligence. Every doctor at UCSF now has access to a tool where if you're coming in to see me, I put my phone down on the desk, say, is it okay if I use this to create my note, press a little button, And it records our conversation.
At the end of a conversation, I press a button and there is your note. And this is not just a transcript. This is an assimilated transcript, you might say, yeah? A transcript would be worthless because you said, well, doctor, I'm having chest pain. And maybe 10 minutes later, you would tell me you're having shortness of breath and your right leg hurts. Those things go together in the note.
They don't go 10 minutes apart in the note. Maybe between them, you told me about your focaccia recipe or how much you love your grandchildren and how Tommy's soccer game went last week. That generally does not go in the note. So the note has to weave all of that together into a template that we are comfortable with. And these tools now do it extraordinarily well.
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Chapter 7: What role do physicians play in the age of AI?
And finally, I found where this history of a pulmonary embolism, which we often shorten as PE, came from. The other thing we shorten as PE sometimes is physical exam.
Okay.
And the patient, 20 years ago, had a physical exam, which the doctor labeled as PE and wrote the patient's physical exam under that. The next doctor, probably in a rush, looked, saw the initials PE, and on the patient's problem list, now the patient had a pulmonary embolism. And that stuck to the patient like gum on a shoe for the rest of their life if I hadn't caught it.
So for all our concern about hallucinating or bullshitting by AI, human intelligence is quite fallible, we should say. Intelligence and time. This was not a matter of someone being not intelligent. It's just there's no way to get the work done that needs to be done. Is it cutting down on pajama time? Absolutely. Absolutely.
And in a way that is very meaningful for physicians to the point that it has led them to be open to, OK, that was great. What's the next thing? Okay, so what is the next thing for AI in healthcare?
We were just told in medical school, we can't detect these forms of cardiovascular disease using this test. But we asked ourselves, could AI do exactly that?
That's coming up after the break. I'm Stephen Dubner. This is the Freakonomics Radio Guide to Getting Better. If you haven't heard the earlier episodes in our series, they are sitting right behind this one in your podcast queue. And we will be right back.
Bob Wachter, who is chair of the Department of Medicine at the University of California, San Francisco, has been telling us that AI has recently been proven super helpful to health care providers by acting as a digital scribe and cutting down on other paperwork. So that's great. But how about some more ambitious uses of AI in health care? For that, we will go to Pierre Elias.
He did research with Wachter at UCSF, took leave from medical school to work for an AI healthcare startup, then went back and got his medical degree in 2016. And what is Elias up to now?
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Chapter 8: What is the future of AI in healthcare?
The thing I became convinced of was if we had just known about this disease, we would have been able to do something about it. He could have gotten a same-day outpatient procedure, and I think he'd be alive today.
And it's hard to imagine that you couldn't have known about the disease when he had been A, in an emergency department at a different hospital, and then B, sent to you.
So how rare or difficult to detect is this disease? This is the fundamental challenge in all of medicine, is you can't treat the patient you don't know about. Oftentimes we're waiting until patients develop symptoms, but for many diseases, symptoms are a late presenting case.
I became obsessed with this question, which is we don't have a screening test for the most common cause of death in the world, which is most forms of cardiovascular disease. And the reason for that's relatively straightforward. The way that we diagnose most forms of cardiovascular disease today is either too invasive or too expensive to do at a population level. Too invasive would be what?
Cardiac catheterization, where we poke you in your arm or your groin and then we shoot some dye into the vessels of the heart to take a look at them.
How often is that performed on a healthy patient?
Never. You would never do a cardiac catheterization on a healthy patient. Patients would be presenting with symptoms like chest pain or inability to exercise before you would consider doing a cardiac catheterization. Okay. And the too expensive option then would be what? It would be an echocardiogram, which is an ultrasound of the heart. An echocardiogram costs a few thousand dollars.
It's an hour-long procedure where they look at the heart in a bunch of different angles. And so we end up in a situation where the way we diagnose cardiovascular disease is either too expensive or too invasive to do at a population level. So we wait until patients oftentimes present with symptoms, which is late in their disease course, and patients have worse outcomes because of that.
Okay, so all you need to do is what then? All you need to do is magically find a way to create a cheap, ubiquitous test that can screen for the most common cause of death in the world. And I became obsessed with this. I asked myself, well, is there anything that we're doing today that could fill that role? And what I came up with was the humble electrocardiogram.
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