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Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.

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Speaker 2: Hello and welcome to another episode of The Odd Laws podcast.

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Speaker 2: I'm Joe Wisenthal and I'm Tracy Alloway. Tracy, what do

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Speaker 2: you think about DOGE.

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Speaker 1: The coin or the new Department of Government Efficiency the latter.

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Speaker 1: Here's what I will say. Part of me hates that

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Speaker 1: government efficiency is being politicized in this way, because if

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Speaker 1: you think that government services are a desirable thing to have,

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Speaker 1: then you should definitely be against waste and inefficiency and

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Speaker 1: fraud in that market, because at a minimum, if you're

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Speaker 1: wasting money, you could be using that money to do

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Speaker 1: even more. And then, obviously, if you think that government

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Speaker 1: is just bad in general, then I imagine that you

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Speaker 1: also think that the government wasting money is also bad.

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Speaker 2: There's no constituency there probably shouldn't be an ideological constituency

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Speaker 2: or political constituency for waste, right.

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Speaker 1: Yeah, exactly, Like I feel like we should all agree

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Speaker 1: on this, But also I hate the way it's kind

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Speaker 1: of unrolling. Let's put it that way.

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Speaker 2: I would say I broadly agree waste is bad. Whether

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Speaker 2: the existing makeup of this sort of quasi blue ribbon

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Speaker 2: commission to get rid of waste whatever that means is

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Speaker 2: actually going to do it. Look, I'll be open minded,

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Speaker 2: but I like the premise of cracking down on waste.

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Speaker 2: I will just say that, you know what the other

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Speaker 2: thing is. So first of all, part of the issue

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Speaker 2: here is that waste is probably difficult to define. Fraud

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Speaker 2: in some cases is probably difficult to define. There will

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Speaker 2: probably be political fights over certain types of spending that

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Speaker 2: it's like, you call this waste, I call this a

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Speaker 2: good allocation of whatever. I imagine many of the political fights

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Speaker 2: around this will sort of revolve around some of these questions.

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Speaker 2: There's a lot there. But I'm glad we generally agree

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Speaker 2: that fraud is bad.

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Speaker 1: Fraud is bad, waste is bad. But I think you're

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Speaker 1: absolutely right that the definitions are going to be crucial, right,

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Speaker 1: And this is where I worry about the politicization, because

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Speaker 1: you can just come in and say like, oh, well,

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Speaker 1: I don't like this, so I'm going to call this

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Speaker 1: a waste and go on from there. But I think

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Speaker 1: we should talk about it because you know, this has

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Speaker 1: come up in a number of episodes, specifically on the

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Speaker 1: PPP post pandemic, and so it's clearly something that is

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Speaker 1: on people's minds totally.

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Speaker 2: Right now, we are recording this Thursday, November fourteenth. We

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Speaker 2: are in a moment where resources in the economy are constrained. Right,

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Speaker 2: the unemployment rate is low, inflation continue used to be

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Speaker 2: by some measures above the Fed's goal. It is not

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Speaker 2: crazy from like a macro standpoint to think we need

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Speaker 2: to make things more efficient and better to have a

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Speaker 2: better use of our real resources right now.

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Speaker 1: And one, this is your other middle aged man thing.

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Speaker 1: You know, you've decided to become like a real resource

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Speaker 1: constraint guy.

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Speaker 2: Right yeah, I'm like, oh, we got we got to

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Speaker 2: crack down. You have to make tough decisions, to make

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Speaker 2: tough decisions. We're taking the candy away from the kids.

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Speaker 2: I'm going to be one of those.

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Speaker 1: Here's a tough decision. If you're going to talk to

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Speaker 1: someone about government waste, would you talk to Elon or Vivek.

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Speaker 1: There's two.

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Speaker 2: Well, Elon would get more downloads if we had a podcast,

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Speaker 2: so I would talk to Elon. But we actually have

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Speaker 2: a better guest. I think we have the perfect guest

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Speaker 2: to talk about government waste fraud where we might be

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Speaker 2: able to move the dial in a substantive way on

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Speaker 2: this kind of stuff. We're going to be speaking with

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Speaker 2: Jetson Leader Luis. He is an assistant professor at the

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Speaker 2: Questionroom School of Business at Boston University. He's also a

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Speaker 2: faculty research fellow at the nb ARE and this is

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Speaker 2: what he studies, particularly areas around fraud and how companies

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Speaker 2: defraud the government wasting millions, probably billions of dollars. So, Jetson,

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Speaker 2: thank you so much for coming.

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Speaker 3: On odd Lots, Thanks so much for having me.

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Speaker 2: Why do you describe your research? I kind of described it,

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Speaker 2: but why did you describe what you do in your background?

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Speaker 3: Yeah? That's great. So I'm an assistant professor. I study economics,

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Speaker 3: and in particular I'm interested in questions about fraud in

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Speaker 3: government spending. I receive my PhD from MIT Economics in

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Speaker 3: twenty twenty, and I've written a number of papers trying

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Speaker 3: to explore the mechanisms the government can and does use

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Speaker 3: to cut out fraud in it in public expenditure.

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Speaker 1: Was there a particular moment or reason that drew you

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Speaker 1: to this particular field.

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Speaker 3: I think fraud in government spending has historically been underanalyzed,

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Speaker 3: and in particular in the healthcare system, where I've done

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Speaker 3: a lot of work. There are really big and impactful

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Speaker 3: policies that are being used to try to eliminate fraud,

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Speaker 3: but historically we didn't really understand what worked and what

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Speaker 3: didn't work and why it worked, and so I think,

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Speaker 3: you know, there was a big opportunity in the research there.

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Speaker 3: And I've always been interested in questions of bad behavior.

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Speaker 3: I have a paper that I started early in my

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Speaker 3: career on fraud in the World Bank that we just

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Speaker 3: got accepted at the journal World Development. And so overall,

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Speaker 3: I'm really interested in this question about, you know, where's

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Speaker 3: the money going and how can we you know, fix

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Speaker 3: that to make sure that the federal funds are being

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Speaker 3: used for people who need them.

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Speaker 2: I definitely want to talk a little bit about fixing

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Speaker 2: the problem and identifying the problem and so forth, But

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Speaker 2: actually I really want to start on how to defraud

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Speaker 2: the government because no, for real, because I sometimes we'll

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Speaker 2: see headlines it's like so and so arrested for insurance

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Speaker 2: fraud of some sort, and you know that's bad, But

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Speaker 2: I also like have there's like weird thing that happens

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Speaker 2: in my head where I'm like, I wouldn't even know

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Speaker 2: how to defraud.

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Speaker 1: Tell us what's the most accessible way to defraud the government.

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Speaker 2: So, like, give us an example, or when we talk about, okay,

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Speaker 2: fraud in the medical system, what are people doing? What

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Speaker 2: is a classical form of defrauding to government in the

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Speaker 2: world of healthcare.

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Speaker 3: So, in the world of healthcare, there are some really

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Speaker 3: obvious frauds that have persisted for years and that I

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Speaker 3: think we're finally maybe starting to wrap our hands around.

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Speaker 3: And one that comes to mind immediately is the ambulance market.

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Speaker 2: Okay, say more so.

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Speaker 3: This was actually described to me when I first heard

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Speaker 3: it by a colleague friend who works for the federal

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Speaker 3: government as the perfect healthcare fraud. And so ambulance services

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Speaker 3: are paid for by Medicare. Medicare is the old age

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Speaker 3: health insurance program for Americans. We spend more than eight

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Speaker 3: hundred billion dollars a year on this program. We spend

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Speaker 3: another seven hundred billion dollars a year on Medicaid. So

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Speaker 3: we're talking about one point five trillion dollars of outlays

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Speaker 3: to these programs. It's very hard for the government to

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Speaker 3: ensure that every dollar that's going out is legitimate, right,

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Speaker 3: It's a volume problem. Ambulance services are highly reimbursed and

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Speaker 3: low overhead. If you want to start an ambulance company,

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Speaker 3: you need to buy an ambulance. That's like thirty grand

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Speaker 3: used online. You can actually go and google it yourself.

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Speaker 3: You can go buy an ambulance, and then you need

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Speaker 3: a couple of employees. It's actually a super low overhead business,

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Speaker 3: which means it's easy for people to start starting around.

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Speaker 3: We think two thousand and three, the market for ambulance services,

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Speaker 3: and in particular repetitive non emergency ambulance services, started getting

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Speaker 3: saturated by intentional fraudulent actors.

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Speaker 1: Wait, what's a non emergency ambulance?

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Speaker 3: Non emergency ambulance is a patient who needs to go

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Speaker 3: to a service because they are sick enough that they

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Speaker 3: can't ride in a taxi or take the train, and

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Speaker 3: they the only safe way for them to get to

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Speaker 3: a service is in an ambulance. And in particular, this

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Speaker 3: really blew up in the dialysis industry. Dialysis patients, there

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Speaker 3: are about half a million of them. We actually spend

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Speaker 3: I think you know this, one percent of the federal

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Speaker 3: budget on the dialysis program.

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Speaker 2: Incredible, set not.

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Speaker 3: One percent of medicare. One percent of the federal budget

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Speaker 3: is the dialysis program. We do not in general pay

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Speaker 3: for ambulance rides or taxi rides for these people to

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Speaker 3: go to and from the visits. They are responsible for

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Speaker 3: getting themselves to the clinic every day, three times a week,

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Speaker 3: generally for a few hours, and that's in perpetuity. It's

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Speaker 3: very challenging to get a kidney and therefore to get

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Speaker 3: off of diale. So we had this system, and this

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Speaker 3: is sort of the canonical Medicare fraud. We build in

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Speaker 3: a little thing for the few people who need it,

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Speaker 3: and that turns into a loophole through which bad actors

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Speaker 3: drive a truck. So we built in this provision, which

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Speaker 3: is if the only safe way that you can get

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Speaker 3: to the dialysis clinic is in an ambulance, Medicare will

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Speaker 3: pay for an ambulance, and they pay for it at

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Speaker 3: a competitive rate for the ambulance companies, at say two

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Speaker 3: hundred and fifty dollars for a one way ride. Now

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Speaker 3: that's not that much money for a real ambulance, but

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Speaker 3: it's a heck of a lot of money for a taxi.

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Speaker 3: And what happened is thousands of firms around the country

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Speaker 3: opened with the express intention not of giving people serious

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Speaker 3: medical care, but of becoming an expensive ambulance taxi and

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Speaker 3: build the government. We have one hundred percent data from

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Speaker 3: the dialysis system. We can see all of these payments

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Speaker 3: more than seven billion dollars for non emergency ambulance transportation

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Speaker 3: over the following ten years, seven point seven billion dollars,

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Speaker 3: and a lot of it was fraud, and the government

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Speaker 3: cracked down. The government really tried to crack down, and

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Speaker 3: in particular, the used a number of tools. The first

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Speaker 3: one is they started throwing people in prison. This is

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Speaker 3: what you say, you see in the headlines. Yeah, but

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Speaker 3: it's so easy to start an ambulance company that we'd

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Speaker 3: see these stories where, you know, someone gets busted and

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Speaker 3: their family member goes and opens a company next door

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Speaker 3: the next day. And this persisted for years, with thousands

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Speaker 3: of companies and billions of dollars of spending just down

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Speaker 3: the drain.

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Speaker 1: I imagine some of the difficulty is also deciding who genuinely

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Speaker 1: needs an ambulance ride and who doesn't. Right, So this

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Speaker 1: is something that I never quite understand about US healthcare

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Speaker 1: in general. The first ten years of my adulthood were

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Speaker 1: in the UK, and there's a national health service there,

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Speaker 1: and if the doctor told you you needed something. You know,

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Speaker 1: you got that something. Maybe it would take a while,

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Speaker 1: but eventually you would get it, whereas in the US

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Speaker 1: you seem to have all these decision makers in the process,

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Speaker 1: and yet we're talking about medical care, which you would

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Speaker 1: think would need to be dictated by like highly trained

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Speaker 1: medical doctors.

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Speaker 3: This is super interesting that that's your intuition, because that's

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Speaker 3: exactly how they fixed it. So the way that Medicare

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Speaker 3: closed this loophole was by requiring what's called prior authorization.

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Speaker 3: Now if instead of going to an ambulance company and saying,

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Speaker 3: you know, please give me this ride, or the even worse,

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Speaker 3: the ambulance company coming to the patient and saying do

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Speaker 3: you want a taxi ride, which is actually what was happening,

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Speaker 3: instead they required that a physicians signed off and say, look,

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Speaker 3: this patient is actually sick, they're bedridden. There's no other

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Speaker 3: way that they can get to dialysis this week. And

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Speaker 3: if you didn't have the doctor's note, Medicare didn't pay.

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Speaker 3: And we estimate that around the timing of them implementing

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Speaker 3: this prior authorization requirement, which they rolled out in different

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Speaker 3: states at different times. This is like what economists love

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Speaker 3: in our research and actual test a difference, and difference

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Speaker 3: exactly when they rolled this out in different places, in

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Speaker 3: different times. We see a sixty seven percent drop in

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Speaker 3: spending the next month, persistent, and not only that, we

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Speaker 3: can then trace the patients and say were these patients harmed?

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Speaker 3: Did they actually miss their dialysis visits and end up

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Speaker 3: in the hospital, And we find no evidence at all

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Speaker 3: of negative patient health effects, And so we actually saved

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Speaker 3: billions of dollars by putting in something that was so basic,

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Speaker 3: which is this the doctor sign?

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Speaker 1: Why didn't they do it before?

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Speaker 3: The structure of Medicare is largely disaggregated, where individuals are

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Speaker 3: able to go to different services as long as they

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Speaker 3: qualify for them, and some of those are doctor's visits, hospitals,

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Speaker 3: medical equipment, pharmaceuticals. We pay for a lot of things,

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Speaker 3: and there is a real worry that requiring too much

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Speaker 3: paperwork can burden the system, and we don't want to

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Speaker 3: turn the Medicare system into an even more heavily administrative

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Speaker 3: burden system. So there are different qualification rules, but largely

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Speaker 3: what happens is the qualification rules are not always enforced upfront.

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Speaker 3: We do a lot of requiring people to follow the rules,

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Speaker 3: maybe we do some audits, maybe we chase after them

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Speaker 3: with criminal lawsuits afterwards. But largely there are circumstances where

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Speaker 3: nefarious actors, it's a relatively high trusystem. Nefarious actors will

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Speaker 3: find these loopholes and drive a truck through them. And

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Speaker 3: I can talk about a million examples of this.

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Speaker 2: Well, let's talk about so Okay, it sounds like the

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Speaker 2: ambulance from taken care of more or less.

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Speaker 3: So the ambulance fraud is less than it used to be.

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Speaker 3: And that's the dialysis ambulance fraud. Oh yeah, this paper

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Speaker 3: was just accepted at the General Political Economy, So we're

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Speaker 3: super happy. Shout out to my.

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Speaker 2: Congratulations who did a great job there.

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Speaker 3: The ambulance market itself has other frauds, right, we're talking

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Speaker 3: about one type, which is this repetitive dialysis fraud. There

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Speaker 3: are still lots of unnecessary ambulance rides, ghost ambulance rides

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Speaker 3: where patients don't even get in the ambulance and bills

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Speaker 3: are sent. One question is what can the government do?

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Speaker 3: And part of it is I think that there needs

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Speaker 3: to be a better focus on using data to detect

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Speaker 3: and stop the fraud.

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Speaker 1: Yeah, talk to us about the data because I imagine,

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Speaker 1: like you're talking about government spending and programs, plus in

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Speaker 1: some instances the medical industry, there must be interesting data

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Speaker 1: available to you.

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Speaker 3: So I have fantastic access to data. I use one

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Speaker 3: hundred percent sample Medicare claims data from nineteen ninety nine

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Speaker 3: through twenty nineteen for all inpatient and outpatient services, durbal

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Speaker 3: medical equipments. I can see twenty percent of physician office

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Speaker 3: visits and party pharmaceuticals. So it's an ridiculous volume. And

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Speaker 3: you know, I'm very equipped with data. I teach data

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Speaker 3: analysis and I have you know, PhD students and other

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Speaker 3: professors who work with me. And even for us, it's

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Speaker 3: a big problem. How do we actually wrap our hands

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Speaker 3: around this? Yeah, the government has not historically invested very

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Speaker 3: well in its data analysis for anti fraud. Part of

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Speaker 3: the reason is that the organizations that are responsible for this,

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Speaker 3: which are the Department of Justice and the Office of

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Speaker 3: the Inspector General. Those career lawyers are fantastic. I cannot

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Speaker 3: say enough positive things about my colleagues at the Department

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Speaker 3: of Justice and the Office of the Inspector General, but

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Speaker 3: there are too few of them. We do not pay

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Speaker 3: them very well, and they are not data analysts. They

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Speaker 3: are lawyers.

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Speaker 2: Okay, so you mentioned there's still some ambulance the dialysis specifically,

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Speaker 2: sounds like that was mostly taken care of. There's other

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Speaker 2: ambulance fraud out there. You mentioned that some people that

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Speaker 2: there's billing ghost ambulances or people never even ride the ambulance.

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Speaker 2: What's hot right now, what's the new ambulance fraud?

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Speaker 3: So what's amazing here is that it seems like it's

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Speaker 3: a constantly evolving marketplace. Right We have to think fraud

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Speaker 3: is a technology where people figure out a loophole and

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Speaker 3: then they tell their friends and these things spread and

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Speaker 3: eventually the government catches up, and so we're playing cat

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Speaker 3: and mouse every year. Right now. I think it's wound care.

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Speaker 2: Okay, say more about wound care.

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Speaker 3: There's been a rise in these expensive treatments for patients

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Speaker 3: with non healing wounds. So, if you're diabetic, you're likely

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Speaker 3: to have neuropathy, and one of the things that comes

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Speaker 3: with diabetic neuropathy is that you often have wounds, particularly

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Speaker 3: on their feet, where the patient doesn't heal. There are

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Speaker 3: some modern technology skin substitute things you can graft onto

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Speaker 3: these wounds that seem like they pay pretty well and

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Speaker 3: potentially even work, and then a few doctors have just

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Speaker 3: started spending millions of dollars on that. But that's a

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Speaker 3: flash in the pan. Historically, we see fraud really rife

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Speaker 3: in the durable medical equipment industry. There's been fraud in

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Speaker 3: basically everything that healthcare touches. Another thing right now that's

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Speaker 3: really popular. We're seeing a lot of fraud in vascular

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Speaker 3: care that is helping patients who have collapsed vain be

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Speaker 3: able to receive intravenous treatments. But again, it's just like

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Speaker 3: just to.

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Speaker 2: Drill into specifics, let's just durable medical equipment fraud. Yes,

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Speaker 2: I want to get into it. What am I doing so?

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Speaker 3: Or you want to get into durable medical equipment fraud,

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Speaker 3: Well you're in luck because it's you know, durable medical

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Speaker 3: equipment has been the wild West of the healthcare system

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Speaker 3: for twenty years with billions of dollars of fraud. I

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Speaker 3: have to admit I'm writing a paper on it right now.

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Speaker 3: That's I'm excited. I know a lot about it. So

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Speaker 3: you remember the scooter store they used to like advertise

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Speaker 3: on late night TV. Are you an old person?

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Speaker 1: Number.

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Speaker 3: Would you like a free wheelchair? Yeah? So there's been

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Speaker 3: some excellent kind of investigative journalism on this so durable

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Speaker 3: medical equipment. I want people to think walkers, wheelchairs, oxygen pumps,

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Speaker 3: hospital beds in their home. Things that people need that

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Speaker 3: are supposed to be permanent. You know, seatpat machines, yep.

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Speaker 3: And these are largely given by suppliers that are sometimes

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Speaker 3: big national firms and sometimes small mom and pop shops.

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Speaker 3: I want you to think about Flora. This is sort

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Speaker 3: of a Florida story. If you're interested in selling someone

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Speaker 3: a you know, a fraudulent walker or pomp or something

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Speaker 3: like that, it's actually.

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Speaker 2: Wait, what does it mean? A fraudulent walker one that

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Speaker 2: doesn't work well.

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Speaker 1: That you don't need.

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Speaker 3: I guess this is super interesting, right, So what do

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Speaker 3: we mean when we say fraud? Healthcare fraud has different types, right,

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Speaker 3: and I can break it. There are three types of

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Speaker 3: health care fraud. There's upcoding. That's where I sell you

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Speaker 3: a little push wheelchair, but I go build a government

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Speaker 3: for a super lux automatic wheelchair. We call that upcoding.

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Speaker 3: There's medical necessity fraud. That's where we say that a

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Speaker 3: patient needs something and they don't. And then there's substandard care.

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Speaker 3: That's where we have a patient who actually does need

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Speaker 3: something and we give them junk. And all of them

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Speaker 3: happen in all forms of medicine, but in particular and

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Speaker 3: durable medical equipment. I think it's a lot of medical

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Speaker 3: necessity fraud. I think we have patients who are getting

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Speaker 3: a knock on the door, high do you want this

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Speaker 3: fancy new device free to you? Now, what's really interesting

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Speaker 3: is we're actually supposed to collect a twenty percent copay

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Speaker 3: for the durable medical equipment products. But the fraud and

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Speaker 3: that's designed by medicare to make sure that patients aren't

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Speaker 3: getting stuff that they don't need, so they.

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Speaker 2: Have to have some skin in the game.

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Speaker 3: They're supposed to do. But if you're a fraudulent firm,

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Speaker 3: you just don't collect it. You're very happy to have

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Speaker 3: the government's eighty percent, and the patient wouldn't take it

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Speaker 3: if they had to pay.

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Speaker 1: Yeah, what's been the I'm trying to think how to

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Speaker 1: frame this, but I guess what's been the cultural or

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Speaker 1: incentive approach in government to stamping out fraud. If I

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Speaker 1: am a government official and I design a poor social

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Speaker 1: service program of some sort that has a bunch of

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Speaker 1: loopholes that ends up costing lots of money. Do I

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Speaker 1: get in trouble or do I get rewarded if I

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Speaker 1: managed to tweak the program so that it doesn't have

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Speaker 1: a lot of fraud in it.

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Speaker 3: The government generally underinvests and miss prices. It's anti fraud investments,

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Speaker 3: by which I mean when we consider how we are

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Speaker 3: measuring what the government is doing to stop fraud. You're

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Speaker 3: talking about these career civil servants and how we reward them. Historically,

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Speaker 3: the focus has been on how much are you getting back?

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Speaker 3: And I've made this point in a bunch of research,

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Speaker 3: and I recently released a white paper through the Center

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Speaker 3: for Medicare and Medicare Medicaid Services saying how much money

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Speaker 3: you get back is irrelevant. That is the wrong number.

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Speaker 3: That is really the number that everyone in government is

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Speaker 3: focused on. When you say anti fraud recovery. We caught

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Speaker 3: this many people, we put this many people in jail,

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Speaker 3: and we got a billion dollars back this year. And

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Speaker 3: the point that I've made is the money you're getting

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Speaker 3: back is just a small share of the effect of

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Speaker 3: your anti fraud efforts. What you should really care about

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Speaker 3: is your deterrence effect. And I've shown in research deterrence

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Speaker 3: effects are in many cases like ten times larger than

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Speaker 3: these recovery dollars. So you go to the Partner of

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Speaker 3: Justice and you talk to their healthcare fraud people. They

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Speaker 3: write a report to Congress every year. It's called the

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Speaker 3: Healthcare Fraud and Abuse Report, and they put a number

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Speaker 3: there for return on investment, and Congress asks them, tell

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Speaker 3: us how much money did you spend and how what

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Speaker 3: was your return on investment? And they say the number

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Speaker 3: is four, And okay, first of all, four ex return

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Speaker 3: on investment already very good. That immediately means that we

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Speaker 3: should spend more resources there. But I think that's actually

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Speaker 3: the wrong number. I think the number is forty because

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Speaker 3: the four is only counting money that they're getting written

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Speaker 3: to them in terms of checks back. But if you

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Speaker 3: count to terrence, and you have to count to terrence,

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Speaker 3: the value of these anti fraud efforts is huge. So

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Speaker 3: do we reward people in terms of the value. They

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Speaker 3: bring this to Tracy's question in some sense, yes, right, Look,

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Speaker 3: there's a press release they trot out the attorney general,

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Speaker 3: maybe the civil servant gets exact and.

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Speaker 1: A minimum I could go to my boss and be like,

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Speaker 1: I saved US twenty million dollars.

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Speaker 3: But largely no, we don't pay these people. Well, we

403
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Speaker 3: don't retain them. Well, if you look at your average

404
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Speaker 3: assistant US attorney, they go to the government for a

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Speaker 3: few years, do it fantastic work, and then realize that

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Speaker 3: private industry pays three times as much.

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Speaker 2: In the league, when we were talking about the original

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Speaker 2: dialysis ambulance fraud, it sounded like there was a very

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Speaker 2: elegant solution, just get a doctor sign off. When you

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Speaker 2: look at some of these other emerging frauds, and you

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Speaker 2: mentioned wound care for example, or you mentioned someone gets

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Speaker 2: a really nice scooter that they didn't really need, or

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Speaker 2: something like that, because and you mentioned that the requirement

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Speaker 2: of putting up twenty percent copay is not that effective

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Speaker 2: because the seller is just like, you know what, We'll

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Speaker 2: take the hit and only take the eighty percent. Is

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Speaker 2: no big deal. How much of the challenge is on

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Speaker 2: the data and identification side, which we should talk a

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Speaker 2: little bit more, versus the versus the mechanism which you

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Speaker 2: use to crack down on it.

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Speaker 3: So I'm actually really in favor of this program. We

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Speaker 3: use the big whistleblower program called the False Claims Act. Okay,

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Speaker 3: and I've written extensively. Do you guys know how this works?

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Speaker 2: No?

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Speaker 3: No, it's the weirdest, coolest thing. I mean for an economist. Okay,

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Speaker 3: So there is a private market for anti fraud in

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Speaker 3: the US. If you know about a company or person

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Speaker 3: that is defrauding any public program, not specific to healthcare,

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Speaker 3: you can hire your own attorney and there are firms

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Speaker 3: that specialize in this. You can sue that person in

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00:21:16,320 --> 00:21:19,879
Speaker 3: federal civil court, and the whistleblower gets a share of

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Speaker 3: the money they bring back to the government. And this

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Speaker 3: is a super effective program because it means that every nurse,

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Speaker 3: every billing agent, every doctor in a hospital, if that organization.

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Speaker 2: This is what we're going to get into. Tracy, I'm

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Speaker 2: not gonna do fraud because even though I want to

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00:21:34,560 --> 00:21:36,520
Speaker 2: know how it works, but I do want to make that.

438
00:21:36,680 --> 00:21:38,440
Speaker 1: I'm going to become a fraud bounty hunter.

439
00:21:40,680 --> 00:21:43,240
Speaker 3: It's bounty hunting. But bounty hunting is great because of

440
00:21:43,240 --> 00:21:45,880
Speaker 3: two reasons. The first is there's this private information component.

441
00:21:46,200 --> 00:21:49,399
Speaker 3: Rather than waiting for some analyst in Washington to figure

442
00:21:49,440 --> 00:21:53,760
Speaker 3: out today's fraud, just make a huge reward available for

443
00:21:53,800 --> 00:21:57,440
Speaker 3: the individuals who know about it, because there's lots of information.

444
00:21:57,520 --> 00:22:00,199
Speaker 3: Right from an economist perspective, the problem here is the

445
00:22:00,200 --> 00:22:03,000
Speaker 3: problem we have in a lot of healthcare. It's information asymmetry.

446
00:22:03,320 --> 00:22:05,600
Speaker 3: The doctors and the hospitals and the nursing homes know

447
00:22:05,680 --> 00:22:08,320
Speaker 3: so much more about the patient than the insurance company.

448
00:22:08,359 --> 00:22:11,679
Speaker 3: And here the insurance company is the government. And so

449
00:22:12,240 --> 00:22:14,600
Speaker 3: rather than trying to make just top down solutions, and

450
00:22:14,640 --> 00:22:16,520
Speaker 3: there are some top down solutions, don't get me wrong,

451
00:22:16,840 --> 00:22:21,960
Speaker 3: it's really important that we also allow the individuals who

452
00:22:22,000 --> 00:22:25,600
Speaker 3: have the information, the valuable information, to be rewarded for that.

453
00:22:25,720 --> 00:22:29,280
Speaker 3: So the whistleblower program private information. There's also the private

454
00:22:29,560 --> 00:22:32,480
Speaker 3: cause of action, the idea that every person can become

455
00:22:32,800 --> 00:22:35,760
Speaker 3: Literally the legal term sometimes use is private attorney general. Right,

456
00:22:36,000 --> 00:22:38,159
Speaker 3: you can go and hire a lawyer and sue, and

457
00:22:38,200 --> 00:22:41,280
Speaker 3: that lawsuit is on behalf of the United States of America.

458
00:22:41,359 --> 00:22:45,000
Speaker 3: And this has been an extremely effective program historically.

459
00:22:45,080 --> 00:22:45,760
Speaker 2: Say more about that.

460
00:22:45,880 --> 00:22:47,960
Speaker 3: Yeah, So I ran a Freedom of Information Act request

461
00:22:48,000 --> 00:22:50,480
Speaker 3: on the Department of Justice for data on every whistleblower

462
00:22:50,560 --> 00:22:53,400
Speaker 3: lawsuit from nineteen eighty seven through about twenty seventeen. When

463
00:22:53,400 --> 00:22:56,280
Speaker 3: I first filed this. There are thousands of these cases.

464
00:22:56,440 --> 00:22:59,359
Speaker 3: They've brought in billions of dollars for the government. Fifty

465
00:22:59,400 --> 00:23:01,879
Speaker 3: five percent of them are in healthcare, but they're also

466
00:23:02,040 --> 00:23:04,639
Speaker 3: used all over the government. We see cases related to

467
00:23:04,720 --> 00:23:07,600
Speaker 3: the Transportation Department, the Department of Education, the Department of Defense,

468
00:23:07,920 --> 00:23:10,919
Speaker 3: and the ideas the same. Instead of trying to have

469
00:23:11,000 --> 00:23:13,600
Speaker 3: the government figure out how to run anti fraud, anti

470
00:23:13,600 --> 00:23:15,880
Speaker 3: fraud can pay for itself. There are lots of people

471
00:23:15,920 --> 00:23:18,480
Speaker 3: who would love to earn a million dollars being a whistleblower,

472
00:23:18,640 --> 00:23:20,240
Speaker 3: and these whistleblowers get paid pretty well.

473
00:23:20,800 --> 00:23:23,879
Speaker 1: Since you mentioned transportation and education, just then, can you

474
00:23:23,920 --> 00:23:27,240
Speaker 1: talk about some examples of fraud outside of the medical sector.

475
00:23:27,480 --> 00:23:31,000
Speaker 3: Absolutely. So. I have a recent paper about the unemployment

476
00:23:31,080 --> 00:23:34,000
Speaker 3: insurance market during COVID, so I'd be happy to talk

477
00:23:34,040 --> 00:23:34,399
Speaker 3: about that.

478
00:23:34,480 --> 00:23:35,040
Speaker 1: Oh, that's great.

479
00:23:35,080 --> 00:23:37,760
Speaker 3: So during COVID we had the biggest expansion of unemployment

480
00:23:37,840 --> 00:23:39,760
Speaker 3: insurance in history. I think you guys know about this.

481
00:23:40,040 --> 00:23:42,280
Speaker 3: You've talked a little bit about PPP, and there's some

482
00:23:42,320 --> 00:23:46,280
Speaker 3: great research there on fraud. PPP was for companies. Unemployment

483
00:23:46,280 --> 00:23:48,560
Speaker 3: insurance was for individuals who lost their job, and we

484
00:23:48,680 --> 00:23:51,760
Speaker 3: expanded it to also include gig economy workers through a

485
00:23:51,800 --> 00:23:54,439
Speaker 3: program called PUA. You guys probably know about this.

486
00:23:54,960 --> 00:23:57,520
Speaker 2: So this is starting to feel like history imade so

487
00:23:57,560 --> 00:24:00,240
Speaker 2: crazy because this is like yesterday, but also it's it

488
00:24:00,240 --> 00:24:02,639
Speaker 2: feels so long. Yeah, the time distortion is real.

489
00:24:03,680 --> 00:24:07,399
Speaker 3: So unemployment insurance rose heavily during the pandemic, and with

490
00:24:07,480 --> 00:24:09,520
Speaker 3: it came a lot of fraud. Now why is there

491
00:24:09,520 --> 00:24:12,040
Speaker 3: fraud in the unemployment insurance sector. Well, the government's cutting checks,

492
00:24:12,240 --> 00:24:14,399
Speaker 3: and the government's cutting checks really fast. So you might

493
00:24:14,440 --> 00:24:16,200
Speaker 3: remember at the beginning of the pandemic, there was this

494
00:24:16,400 --> 00:24:19,960
Speaker 3: immediate recession, and there was fear of the big macroeconomic

495
00:24:20,000 --> 00:24:22,359
Speaker 3: consequences of everybody being out of a job on many people,

496
00:24:22,400 --> 00:24:24,199
Speaker 3: I should say being out of a job, and so

497
00:24:24,280 --> 00:24:28,440
Speaker 3: the government really loosened and expanded its use of unemployment insurance.

498
00:24:28,600 --> 00:24:30,320
Speaker 3: But whenever the government says, hey, we're going to write

499
00:24:30,320 --> 00:24:33,160
Speaker 3: eight hundred billion dollars in checks, people get creative about

500
00:24:33,160 --> 00:24:35,520
Speaker 3: ways to builk the government. And in particular, this type

501
00:24:35,520 --> 00:24:39,640
Speaker 3: of fraud was really identity theft. It is super easy,

502
00:24:40,040 --> 00:24:41,960
Speaker 3: super easy to go on the dark web and buy

503
00:24:42,000 --> 00:24:44,080
Speaker 3: a social Security number, and so that's what a lot

504
00:24:44,119 --> 00:24:48,240
Speaker 3: of criminals and organized criminal groups did during the pandemic,

505
00:24:48,680 --> 00:24:55,400
Speaker 3: there was a widespread fraud where individuals would go purchase identities,

506
00:24:55,920 --> 00:24:59,960
Speaker 3: apply en mass to state unemployment insurance programs, collect them,

507
00:25:00,760 --> 00:25:03,040
Speaker 3: take it out of the system, and then we think

508
00:25:03,080 --> 00:25:05,960
Speaker 3: in many cases it was either offshore or rerouted to

509
00:25:06,000 --> 00:25:10,240
Speaker 3: criminal organizations. So this was so incredibly wides But actually

510
00:25:10,440 --> 00:25:12,680
Speaker 3: my wife got a prepaid debit card in the mail

511
00:25:12,960 --> 00:25:15,240
Speaker 3: from the unemployment agency and she didn't lose her job.

512
00:25:15,680 --> 00:25:18,080
Speaker 3: I've talked to just dozens of people across the country

513
00:25:18,080 --> 00:25:19,600
Speaker 3: and in all sorts of sectors, who said, yeah, this

514
00:25:19,640 --> 00:25:20,359
Speaker 3: actually happened to me.

515
00:25:20,760 --> 00:25:23,400
Speaker 1: Wait, this leads to something that I want to ask

516
00:25:23,440 --> 00:25:27,320
Speaker 1: as well, which is how is fraud propagated? Because this

517
00:25:27,400 --> 00:25:29,960
Speaker 1: kind of gets to Joe's question earlier, how do people

518
00:25:30,000 --> 00:25:32,439
Speaker 1: actually do this? How do I learn to do fraud?

519
00:25:32,640 --> 00:25:34,640
Speaker 1: Is it like I just look it up on the internet,

520
00:25:34,760 --> 00:25:37,960
Speaker 1: or is it like someone I'm associated with tells me.

521
00:25:38,680 --> 00:25:42,320
Speaker 3: So different frauds have different mechanisms by which people learn them,

522
00:25:42,320 --> 00:25:45,000
Speaker 3: but in general, there is a social learning component to this. Absolutely.

523
00:25:45,280 --> 00:25:47,840
Speaker 3: So with some of the institutional frauds that we've been

524
00:25:47,880 --> 00:25:50,200
Speaker 3: talking about, a hospital that decides that they're going to

525
00:25:50,240 --> 00:25:52,919
Speaker 3: suddenly charge a lot of money through some loophole. Often

526
00:25:52,960 --> 00:25:56,000
Speaker 3: there are business decisions being made by executives. Sometimes there

527
00:25:56,000 --> 00:25:59,480
Speaker 3: are consultants involved, and big national chains often are the

528
00:25:59,480 --> 00:26:02,280
Speaker 3: ones that's read these because they have kind of centralized management.

529
00:26:02,520 --> 00:26:05,480
Speaker 3: The hospital administrators from different hospitals look at this. I'm thinking,

530
00:26:05,520 --> 00:26:08,840
Speaker 3: for example, of the tenant hospitals which paid nine hundred

531
00:26:08,880 --> 00:26:11,359
Speaker 3: million dollars back to the government for a small little

532
00:26:11,400 --> 00:26:13,960
Speaker 3: loophole that was supposed to be for an outlier payments program,

533
00:26:14,119 --> 00:26:15,840
Speaker 3: and they just drove a truck through that loophole they

534
00:26:16,080 --> 00:26:19,480
Speaker 3: ended up stealing. So if you go in patient in

535
00:26:19,480 --> 00:26:21,840
Speaker 3: a hospital, the hospital gets paid fixed themount under Medicare

536
00:26:21,920 --> 00:26:24,480
Speaker 3: through what's called a prospective payment system. They don't pay

537
00:26:24,520 --> 00:26:27,160
Speaker 3: per cost. They just say, you know you have a pneumonia,

538
00:26:27,240 --> 00:26:29,680
Speaker 3: we're going to pay this much. The government was worried

539
00:26:29,680 --> 00:26:31,920
Speaker 3: when they set this program up that some very expensive

540
00:26:31,920 --> 00:26:35,040
Speaker 3: patients wouldn't get treatment because if the hospital knows that

541
00:26:35,160 --> 00:26:36,760
Speaker 3: and they know that, they're going to lose money on you,

542
00:26:37,160 --> 00:26:39,560
Speaker 3: so they made this asterisk. A lot of these things

543
00:26:39,560 --> 00:26:42,920
Speaker 3: are asterisks to have an outlier payment system where if

544
00:26:42,920 --> 00:26:46,120
Speaker 3: a patient is super expensive, then they pay extra and

545
00:26:46,280 --> 00:26:49,280
Speaker 3: the tenant hospitals figured out how to make every patient

546
00:26:49,280 --> 00:26:52,560
Speaker 3: look super expensive by manipulating some of their balance sheets,

547
00:26:52,640 --> 00:26:55,639
Speaker 3: and they ended up spending nine hundred million dollars to

548
00:26:56,440 --> 00:26:58,200
Speaker 3: excuse me, they end up to settle claims, so that

549
00:26:58,320 --> 00:27:00,600
Speaker 3: Tenant never admitted fault, I should say, but the government

550
00:27:00,640 --> 00:27:03,679
Speaker 3: received nine hundred million dollars back from Tenant because you know,

551
00:27:03,720 --> 00:27:06,480
Speaker 3: these allegations were I think true that that Tenant had

552
00:27:06,520 --> 00:27:08,560
Speaker 3: done this, and I estimate actually that the government lost

553
00:27:08,600 --> 00:27:10,280
Speaker 3: billions of dollars to that. Okay, So but I want

554
00:27:10,280 --> 00:27:11,480
Speaker 3: to go back to this question, So how did that

555
00:27:11,520 --> 00:27:13,639
Speaker 3: one happen? Well, there was a consultancy in New Jersey

556
00:27:13,680 --> 00:27:15,480
Speaker 3: that was going around telling people, hey, do you know

557
00:27:15,520 --> 00:27:17,840
Speaker 3: about this outlier payment system? And that's how we think

558
00:27:17,880 --> 00:27:19,760
Speaker 3: that fraud spread, and it's spread all over the country.

559
00:27:20,160 --> 00:27:24,240
Speaker 3: So there's some of this corporate learning from other companies,

560
00:27:24,280 --> 00:27:26,639
Speaker 3: and there's also a lot of social learning. So in

561
00:27:26,680 --> 00:27:30,320
Speaker 3: the case of unemployment insurance fraud or PPP fraud, you

562
00:27:30,320 --> 00:27:34,440
Speaker 3: can go and you can find Telegram groups and Facebook

563
00:27:34,440 --> 00:27:36,960
Speaker 3: groups of people that are like, here's how you apply

564
00:27:37,000 --> 00:27:39,560
Speaker 3: for a PPP loan. There's a great new paper by

565
00:27:39,640 --> 00:27:41,960
Speaker 3: John Griffin at the University of Texas on these Facebook

566
00:27:42,000 --> 00:27:45,399
Speaker 3: groups and like they're called like fraud kings, like they

567
00:27:45,400 --> 00:27:47,440
Speaker 3: know what there were maybe PPP loan kings. There's something

568
00:27:47,440 --> 00:27:49,800
Speaker 3: that it's like really obvious what they're doing and everyone knows.

569
00:27:50,000 --> 00:27:52,080
Speaker 3: And so in the case of health care fraud, in

570
00:27:52,119 --> 00:27:54,640
Speaker 3: the case of non healthcare fraud, often it's you're surrounded

571
00:27:54,640 --> 00:27:56,520
Speaker 3: by people who know how to do this, or you

572
00:27:56,560 --> 00:27:59,199
Speaker 3: meet them through digital platforms, and then people learn and

573
00:27:59,240 --> 00:28:02,360
Speaker 3: so a lot of the fraud we see propagates through communities.

574
00:28:03,000 --> 00:28:05,399
Speaker 3: In the case of the ambulance market, we saw that

575
00:28:05,440 --> 00:28:08,679
Speaker 3: there were certain Eastern European groups that were responsible for

576
00:28:08,720 --> 00:28:11,080
Speaker 3: this in different parts of the country and often from

577
00:28:11,119 --> 00:28:14,560
Speaker 3: the same from original areas, and so you know, generally

578
00:28:14,600 --> 00:28:16,639
Speaker 3: the understanding, at least from the government is that there

579
00:28:16,720 --> 00:28:18,400
Speaker 3: must have been some social learning going out there.

580
00:28:18,400 --> 00:28:20,479
Speaker 2: It's very hard to prove, of course, right, someone figures

581
00:28:20,520 --> 00:28:22,920
Speaker 2: it out and then tell family that's right, that makes sense.

582
00:28:23,160 --> 00:28:26,280
Speaker 2: Let's talk about detection via data and more, and you

583
00:28:26,320 --> 00:28:28,000
Speaker 2: talked about how you have access to all of this

584
00:28:28,240 --> 00:28:33,200
Speaker 2: data and so forth. Obviously you can describe fraud qualitatively

585
00:28:33,480 --> 00:28:37,080
Speaker 2: by saying this is how an ambulance company cheats the government,

586
00:28:37,119 --> 00:28:40,479
Speaker 2: et cetera. What do the fingerprints of fraud look like

587
00:28:40,520 --> 00:28:42,640
Speaker 2: when you look at it on the macro scale, What

588
00:28:43,200 --> 00:28:46,120
Speaker 2: pops up in the data that would at least be

589
00:28:46,200 --> 00:28:48,280
Speaker 2: a yellow flag and say this is something we need

590
00:28:48,280 --> 00:28:48,880
Speaker 2: to look at more.

591
00:28:49,080 --> 00:28:52,920
Speaker 3: So there are huge run ups in spending in every

592
00:28:52,960 --> 00:28:54,680
Speaker 3: type of fraud I've ever seen, and every type of

593
00:28:54,840 --> 00:28:56,480
Speaker 3: I mean, the whole point is if you're not making money,

594
00:28:56,480 --> 00:28:57,960
Speaker 3: it's not a good fraud, right, And so if you

595
00:28:58,040 --> 00:29:02,000
Speaker 3: just make a plot of bending against you know, time,

596
00:29:02,240 --> 00:29:04,920
Speaker 3: you can often just see these really big exponential growth. Now,

597
00:29:04,960 --> 00:29:06,920
Speaker 3: some of those are legitimate, because if there's a new

598
00:29:07,040 --> 00:29:10,520
Speaker 3: great medical technology and people start using it, that also

599
00:29:10,640 --> 00:29:13,560
Speaker 3: looks like a technological adoption curve, right of a kind

600
00:29:13,560 --> 00:29:17,120
Speaker 3: of big upper rank. But when it's fraud, it you know,

601
00:29:17,240 --> 00:29:20,800
Speaker 3: first of all, it looks like massive year over year increases.

602
00:29:21,360 --> 00:29:24,400
Speaker 3: The second is that it'll often be way too much,

603
00:29:25,200 --> 00:29:27,680
Speaker 3: to the point where it's obvious that nobody's getting this.

604
00:29:27,760 --> 00:29:30,720
Speaker 3: For example, sometimes you'll see a doctor who's just billing

605
00:29:30,720 --> 00:29:33,479
Speaker 3: from too many home care visits, but like the homecare

606
00:29:33,560 --> 00:29:35,560
Speaker 3: visits are sixty minutes, and the doctor's billing for five

607
00:29:35,560 --> 00:29:37,920
Speaker 3: thousand of them a year. It's like, well, that's five

608
00:29:37,960 --> 00:29:39,920
Speaker 3: thousand hours. There aren't five thousand work hours in the year.

609
00:29:39,960 --> 00:29:41,720
Speaker 3: The government should just be able to detect that. You

610
00:29:41,760 --> 00:29:44,800
Speaker 3: should just not pay those So where the failure is

611
00:29:44,800 --> 00:29:46,480
Speaker 3: is not in how hard it is to detect in

612
00:29:46,560 --> 00:29:48,880
Speaker 3: data such and not that hard to detect in data.

613
00:29:48,920 --> 00:29:51,880
Speaker 3: It's on the incentives for the enforcers to look at

614
00:29:51,880 --> 00:29:54,240
Speaker 3: that data and use it appropriately. And that's where we

615
00:29:54,280 --> 00:29:56,160
Speaker 3: get back to this limited enforcement capacity.

616
00:29:56,440 --> 00:29:59,760
Speaker 1: How do you measure benefits on the other side, because

617
00:29:59,760 --> 00:30:02,760
Speaker 1: that's it seems again like a potential avenue where there

618
00:30:02,800 --> 00:30:04,000
Speaker 1: could be some disagreement.

619
00:30:04,960 --> 00:30:07,719
Speaker 3: So I think it's super important that we preserve access

620
00:30:07,720 --> 00:30:11,160
Speaker 3: to the public programs. And my research is not focused

621
00:30:11,160 --> 00:30:12,800
Speaker 3: at all on, you know, how do we take things

622
00:30:12,840 --> 00:30:14,880
Speaker 3: away from people? And in particular, I always try to

623
00:30:14,920 --> 00:30:19,520
Speaker 3: measure very hard whether there are health effects associated with this.

624
00:30:19,640 --> 00:30:21,240
Speaker 3: So in the case of the ambulances, we're able to

625
00:30:21,280 --> 00:30:24,400
Speaker 3: show pretty definitively that there are no negative health effets

626
00:30:24,440 --> 00:30:25,880
Speaker 3: associated with cutting this, and this is something I do

627
00:30:25,960 --> 00:30:28,200
Speaker 3: in all my papers. You really have to ask the question,

628
00:30:28,520 --> 00:30:31,960
Speaker 3: were people losing care that they needed? And so super

629
00:30:31,960 --> 00:30:34,600
Speaker 3: great question in the context of some of these very

630
00:30:34,640 --> 00:30:38,240
Speaker 3: obvious frauds. Sometimes people aren't even getting the service. So

631
00:30:38,280 --> 00:30:40,600
Speaker 3: if the government's paying for something and nobody ever got it,

632
00:30:40,640 --> 00:30:43,560
Speaker 3: taking it away is costless. So that's the best efficient

633
00:30:43,680 --> 00:30:45,760
Speaker 3: thing that we could do, is just stop paying for

634
00:30:45,800 --> 00:30:48,160
Speaker 3: things that aren't even happening. Right, Let's not even talk

635
00:30:48,160 --> 00:30:50,680
Speaker 3: about waste. Let's just talk about these outright frauds. And

636
00:30:50,760 --> 00:30:52,800
Speaker 3: so how do you measure it? I mean, if you

637
00:30:52,840 --> 00:30:55,719
Speaker 3: look at very large scale claims data, as I do,

638
00:30:55,760 --> 00:30:58,480
Speaker 3: and the government can, you can see, Okay, we cut

639
00:30:58,480 --> 00:31:01,600
Speaker 3: out this provider, let's look at their patients. Did they

640
00:31:01,600 --> 00:31:04,480
Speaker 3: go to the hospital more? That's an empirical question, right,

641
00:31:04,480 --> 00:31:06,520
Speaker 3: And so there's no reason that it just has to

642
00:31:06,560 --> 00:31:08,000
Speaker 3: be a gas, right. This is something that we should

643
00:31:08,040 --> 00:31:10,640
Speaker 3: be measuring as part of our data analysis associated with

644
00:31:10,680 --> 00:31:11,160
Speaker 3: handy front.

645
00:31:27,360 --> 00:31:31,400
Speaker 2: So it's very obvious why, whether we're talking about the

646
00:31:31,440 --> 00:31:35,000
Speaker 2: government or private insurance, that it's just rules on top

647
00:31:35,040 --> 00:31:38,400
Speaker 2: of rules on top of rules and asterisks and so forth,

648
00:31:38,520 --> 00:31:44,560
Speaker 2: because this is very complicated stuff. Rules also create problems

649
00:31:45,200 --> 00:31:49,440
Speaker 2: and compliance by the rules. Can you know strict adherence

650
00:31:49,440 --> 00:31:52,640
Speaker 2: to the rules can also have negative effects. I have

651
00:31:52,720 --> 00:31:55,840
Speaker 2: to imagine, for example, that there are say, many people

652
00:31:56,040 --> 00:31:59,920
Speaker 2: in the US currently on some sort of GLP one drug,

653
00:32:00,440 --> 00:32:03,560
Speaker 2: but maybe technically they don't have the thing, but you

654
00:32:03,600 --> 00:32:05,080
Speaker 2: know a lot of it. There seem to be a

655
00:32:05,120 --> 00:32:07,960
Speaker 2: lot of benefits from weight loss and so forth. Have

656
00:32:08,080 --> 00:32:12,600
Speaker 2: you found examples in your research in which there is

657
00:32:12,640 --> 00:32:18,200
Speaker 2: some sort of positive externality from deviation from the rules?

658
00:32:18,240 --> 00:32:20,320
Speaker 3: Absolutely? Absolutely, Thanks for cueuing this up. I have a

659
00:32:20,360 --> 00:32:23,960
Speaker 3: great story. So let's talk about the hospice industry. Hospice

660
00:32:23,960 --> 00:32:26,920
Speaker 3: care is an end of life benefit for patients who

661
00:32:26,960 --> 00:32:29,080
Speaker 3: have a prognosis of six months or less. So if

662
00:32:29,080 --> 00:32:31,240
Speaker 3: you're dying and a physician certifies that you're dying within

663
00:32:31,280 --> 00:32:34,320
Speaker 3: six months, you qualify for hospice. What is hospice? You

664
00:32:34,360 --> 00:32:36,600
Speaker 3: give up the curative care, and you stop taking all

665
00:32:36,600 --> 00:32:38,480
Speaker 3: of these meds with the horrible side effects, and you

666
00:32:38,480 --> 00:32:40,200
Speaker 3: stop going to the hospital as often, and you can

667
00:32:40,200 --> 00:32:43,239
Speaker 3: die peacefully at home with pain medication. And I think

668
00:32:43,280 --> 00:32:45,760
Speaker 3: that this is a great program. I think it's really important.

669
00:32:45,840 --> 00:32:47,920
Speaker 3: We spend twenty billion dollars a year on the federal

670
00:32:47,960 --> 00:32:52,680
Speaker 3: hospice program, and more than fifty percent of Medicare patients

671
00:32:52,720 --> 00:32:54,880
Speaker 3: who die every year will have had a hospice claim, Okay,

672
00:32:54,920 --> 00:32:56,920
Speaker 3: this is important. This is the way that we're treating

673
00:32:56,920 --> 00:32:59,840
Speaker 3: people at the end of life. It's really hard to

674
00:32:59,880 --> 00:33:03,280
Speaker 3: know who's dying within six months. That is not a

675
00:33:03,320 --> 00:33:10,040
Speaker 3: trivial estimate, and historically there was subjectivity in this. So

676
00:33:10,600 --> 00:33:13,920
Speaker 3: over the twenty year period nineteen ninety nine through twenty nineteen,

677
00:33:14,200 --> 00:33:18,360
Speaker 3: there was a quadrupling of for profit hospices, and many

678
00:33:18,400 --> 00:33:22,560
Speaker 3: of them increasingly took Alzheimer's and dementia patients. Why because

679
00:33:22,560 --> 00:33:25,760
Speaker 3: they stay for a long time on these hospice programs,

680
00:33:26,080 --> 00:33:29,240
Speaker 3: and the hospice programs are paid about two hundred dollars

681
00:33:29,240 --> 00:33:33,000
Speaker 3: a day. And the federal government cried fraud, and we

682
00:33:33,040 --> 00:33:37,160
Speaker 3: saw one hundred and sixty three federal whistleblower lawsuits against

683
00:33:37,160 --> 00:33:40,480
Speaker 3: for profit hospice companies more than three hundred million dollars

684
00:33:40,480 --> 00:33:42,920
Speaker 3: in settlements, saying this is fraud. You shouldn't have taken

685
00:33:42,920 --> 00:33:45,000
Speaker 3: the patients. You should have known that they weren't dying

686
00:33:45,000 --> 00:33:47,840
Speaker 3: fast enough that they did not qualify for hospice. So

687
00:33:47,920 --> 00:33:50,280
Speaker 3: I wrote a paper with John Gruber at MIT, as

688
00:33:50,280 --> 00:33:52,120
Speaker 3: well as one of our PhD students and David Howard

689
00:33:52,160 --> 00:33:55,520
Speaker 3: at Emery, some really top notch economists and we looked

690
00:33:55,560 --> 00:33:57,680
Speaker 3: at this program. We said, Okay, this is something people

691
00:33:57,680 --> 00:34:00,320
Speaker 3: are really concerned about. And what we found really knocked

692
00:34:00,360 --> 00:34:03,520
Speaker 3: our socks off. The fraud actually was not as bad

693
00:34:03,520 --> 00:34:06,600
Speaker 3: as people said, in particular because did some patients go

694
00:34:06,640 --> 00:34:09,200
Speaker 3: to hospice who may otherwise not have because they were

695
00:34:09,200 --> 00:34:11,560
Speaker 3: not dying fast enough. Sure, but it's still a heck

696
00:34:11,600 --> 00:34:14,160
Speaker 3: of a lot cheaper to go to hospice for six

697
00:34:14,200 --> 00:34:15,840
Speaker 3: months than it is to go to the hospital and

698
00:34:15,880 --> 00:34:17,759
Speaker 3: the nursing home and the homecare agency and the Durbal

699
00:34:17,760 --> 00:34:20,560
Speaker 3: medical equipment and the pharmaceuticals. And we estimate that these

700
00:34:20,600 --> 00:34:24,760
Speaker 3: patients saved tens of thousands of dollars. And the patients

701
00:34:24,880 --> 00:34:27,200
Speaker 3: liked it. They and their families are picking hospice. This

702
00:34:27,239 --> 00:34:29,520
Speaker 3: is something that they want. They don't want the hospital,

703
00:34:29,560 --> 00:34:31,720
Speaker 3: so they're getting a service they want and the government

704
00:34:31,760 --> 00:34:33,640
Speaker 3: is saving money. And yet we've decided that this is

705
00:34:33,680 --> 00:34:35,960
Speaker 3: a fraud because there's this rule. Now, when you think

706
00:34:36,000 --> 00:34:38,400
Speaker 3: about it from that way, six months is totally arbitrary.

707
00:34:38,400 --> 00:34:40,480
Speaker 3: Why is it six months and not eight months? And

708
00:34:40,520 --> 00:34:43,000
Speaker 3: so this paper, it's called Dying or Lying, It just

709
00:34:43,280 --> 00:34:46,400
Speaker 3: was accepted last week at the AER. It shows pretty

710
00:34:46,400 --> 00:34:50,400
Speaker 3: conclusively that the policies aimed at limiting fraud threw the

711
00:34:50,400 --> 00:34:51,240
Speaker 3: baby out with the bathloar.

712
00:34:52,239 --> 00:34:55,960
Speaker 1: You know, we started this conversation mentioning DOJE the new

713
00:34:56,120 --> 00:35:00,560
Speaker 1: Department of Government Efficiency. I guess one obvious question to

714
00:35:00,600 --> 00:35:02,719
Speaker 1: ask you would be, you know, if you were in

715
00:35:02,920 --> 00:35:07,000
Speaker 1: Elon or Vivek's shoes, what would be your you know,

716
00:35:07,080 --> 00:35:09,560
Speaker 1: the first thing you would start with when it comes

717
00:35:09,600 --> 00:35:11,880
Speaker 1: to rooting out fraud or I don't know if you

718
00:35:11,880 --> 00:35:16,359
Speaker 1: want to get into wider types of inefficiencies, but what's

719
00:35:16,400 --> 00:35:17,200
Speaker 1: the first thing you would do?

720
00:35:17,320 --> 00:35:20,400
Speaker 2: Yeah, they make you the real point person on this.

721
00:35:20,440 --> 00:35:21,160
Speaker 2: How do you start?

722
00:35:21,560 --> 00:35:25,400
Speaker 3: So first, I'm optimistic about the Department of Government Efficiency

723
00:35:25,400 --> 00:35:28,120
Speaker 3: because I think that there are some clear evidence based,

724
00:35:28,760 --> 00:35:31,480
Speaker 3: obvious wins that we have left on the table in

725
00:35:31,560 --> 00:35:35,000
Speaker 3: terms of saving government money. The first is, we know

726
00:35:35,320 --> 00:35:39,040
Speaker 3: that anti fraud efforts, particularly in the Medicare and Medicaid programs,

727
00:35:39,080 --> 00:35:41,920
Speaker 3: have huge return on investments, and we underfund them. So

728
00:35:41,960 --> 00:35:45,120
Speaker 3: we could staff up those offices at the Department of Justice,

729
00:35:45,160 --> 00:35:46,920
Speaker 3: at the Department of Justice Districts, at the Office of

730
00:35:46,920 --> 00:35:49,760
Speaker 3: the Inspector General and literally just chase down the leads

731
00:35:49,760 --> 00:35:51,120
Speaker 3: that we already know about we don't even have to

732
00:35:51,160 --> 00:35:53,240
Speaker 3: go after other leads. If you look at your average

733
00:35:53,520 --> 00:35:56,200
Speaker 3: civil assistant US attorney, they've got thirty good cases on

734
00:35:56,239 --> 00:35:59,239
Speaker 3: their desk and they get to pick two of them sore.

735
00:35:59,480 --> 00:36:01,960
Speaker 3: And many of these cases allege millions of dollars of

736
00:36:01,960 --> 00:36:03,840
Speaker 3: fraud and they say, I'm sorry, we have a number.

737
00:36:03,960 --> 00:36:06,080
Speaker 3: We can't go under anything right now less than ten million,

738
00:36:06,120 --> 00:36:08,600
Speaker 3: and they drop the case. So even if we just

739
00:36:08,800 --> 00:36:11,160
Speaker 3: doubled the number of people or at the Department of

740
00:36:11,200 --> 00:36:13,919
Speaker 3: Justice who focus on fraud against the government, that would

741
00:36:13,920 --> 00:36:16,600
Speaker 3: pay for itself many times over. Okay, So that's an

742
00:36:16,600 --> 00:36:19,520
Speaker 3: obvious one. The second is to start getting serious about data.

743
00:36:20,040 --> 00:36:23,960
Speaker 3: If we do not hire and recruit excellent analysts to

744
00:36:24,000 --> 00:36:26,279
Speaker 3: look at government data from the government, we are going

745
00:36:26,280 --> 00:36:29,879
Speaker 3: to miss obvious frauds. Everything I've talked about so far

746
00:36:30,200 --> 00:36:33,200
Speaker 3: is not rocket science. We're talking about huge amounts of

747
00:36:33,200 --> 00:36:34,960
Speaker 3: billing for patients who obviously don't need it in some

748
00:36:35,000 --> 00:36:37,000
Speaker 3: of these cases, and the government pays it and they

749
00:36:37,040 --> 00:36:39,480
Speaker 3: miss it. They have access to that data in real time,

750
00:36:39,480 --> 00:36:42,040
Speaker 3: they're writ in the checks. Why are we not screening that?

751
00:36:42,080 --> 00:36:44,360
Speaker 3: And the answer is not a lot of data analysts

752
00:36:44,400 --> 00:36:46,359
Speaker 3: work at the Department of Justice, and there are some.

753
00:36:46,680 --> 00:36:48,000
Speaker 3: There are some that work at the Office and the

754
00:36:48,040 --> 00:36:51,000
Speaker 3: Inspector General of Health and Human Services, but not that many.

755
00:36:51,160 --> 00:36:53,480
Speaker 3: Why because they pay like seventy thousand dollars a year.

756
00:36:53,600 --> 00:36:55,120
Speaker 3: And if you're a good data analyst, you don't go

757
00:36:55,160 --> 00:36:57,120
Speaker 3: to work for the government for seventy thousand dollars a year.

758
00:36:57,239 --> 00:36:59,239
Speaker 3: If the government wants top talent, it's got to be

759
00:36:59,280 --> 00:37:02,680
Speaker 3: willing to recruit, and those are competitive positions. And so

760
00:37:02,920 --> 00:37:04,879
Speaker 3: if we get serious about data, we get serious about

761
00:37:04,880 --> 00:37:08,880
Speaker 3: machine learning, we get serious about investing in the attorneys

762
00:37:08,920 --> 00:37:11,239
Speaker 3: who are doing the good work, and just the programs

763
00:37:11,239 --> 00:37:15,120
Speaker 3: work really well. The second is rigorous evaluation, right, we

764
00:37:15,200 --> 00:37:18,080
Speaker 3: need to know when we try policies, do they work

765
00:37:18,200 --> 00:37:19,880
Speaker 3: or do they not work. When we put in a

766
00:37:19,880 --> 00:37:23,440
Speaker 3: prior authorization program, when we put in a new screening

767
00:37:23,480 --> 00:37:25,839
Speaker 3: for people to take a selfie on their phone before

768
00:37:25,880 --> 00:37:28,120
Speaker 3: we pay their unemployment insurance claim, that's actually how we

769
00:37:28,120 --> 00:37:30,960
Speaker 3: fix the unemployment insurance problem. It was an identity theft problem.

770
00:37:31,000 --> 00:37:32,480
Speaker 3: You just got to take a selfie on your phone.

771
00:37:32,640 --> 00:37:36,080
Speaker 3: These ideas work, but we need to evaluate them as

772
00:37:36,160 --> 00:37:37,360
Speaker 3: serious policy.

773
00:37:36,960 --> 00:37:40,120
Speaker 2: As I just have one more question, and I don't

774
00:37:40,120 --> 00:37:44,239
Speaker 2: even know whether this is capable of being ascertained in

775
00:37:44,280 --> 00:37:47,200
Speaker 2: a substantive way. But when you think about the stuff

776
00:37:47,239 --> 00:37:49,799
Speaker 2: that you research and we're talking about, like, you know,

777
00:37:49,920 --> 00:37:53,920
Speaker 2: big question is can we meaningfully move the dial on

778
00:37:54,719 --> 00:37:59,040
Speaker 2: fraudulent spending or maybe wasteful spending? And do we have

779
00:37:59,120 --> 00:38:02,359
Speaker 2: a number it exists? Do we know how much could

780
00:38:02,400 --> 00:38:04,920
Speaker 2: potentially be saved? So?

781
00:38:05,040 --> 00:38:07,960
Speaker 3: I think healthcare fraud alone in the United States is

782
00:38:08,000 --> 00:38:11,480
Speaker 3: something like one hundred billion dollars a year. Now that's

783
00:38:11,520 --> 00:38:13,960
Speaker 3: across private and public. But we spend one point five

784
00:38:14,040 --> 00:38:16,880
Speaker 3: trillion dollars on Medicare and Medicaid. So the idea that

785
00:38:16,960 --> 00:38:19,400
Speaker 3: it's a big chunks, now, it's not that big of

786
00:38:19,400 --> 00:38:21,080
Speaker 3: a chunk. We spent you know, two point three trillion

787
00:38:21,120 --> 00:38:23,280
Speaker 3: dollars overall, and so to say fifty to one hundred

788
00:38:23,280 --> 00:38:25,719
Speaker 3: billion dollars a year of fraud, yeah, I think that

789
00:38:25,880 --> 00:38:28,799
Speaker 3: number is reasonable. Yeah, So can we move the needle

790
00:38:28,840 --> 00:38:31,440
Speaker 3: on it? Absolutely? There are things that we know work

791
00:38:31,520 --> 00:38:34,640
Speaker 3: really well, huge treatment effects at very low cost. Now,

792
00:38:34,760 --> 00:38:39,520
Speaker 3: government spending overall, we spend what six seven trillion dollars

793
00:38:39,560 --> 00:38:41,879
Speaker 3: a year in the government. When we look at these

794
00:38:41,920 --> 00:38:45,720
Speaker 3: other public programs there are some that have obvious frauds

795
00:38:45,719 --> 00:38:48,160
Speaker 3: going on, Like I mentioned the unemployment insurance system that

796
00:38:48,239 --> 00:38:51,080
Speaker 3: had tens or possibly one hundred billion dollars of fraud

797
00:38:51,160 --> 00:38:53,840
Speaker 3: there as well the PPP program. We spent one hundred

798
00:38:53,840 --> 00:38:56,560
Speaker 3: billion dollars or at least on fraud in that program.

799
00:38:56,760 --> 00:38:59,279
Speaker 3: And so not every program is rife with fraud. There

800
00:38:59,280 --> 00:39:01,879
Speaker 3: are programs that are very hard to defraud. It's hard

801
00:39:01,880 --> 00:39:04,279
Speaker 3: to defraud social Security why because they have your full

802
00:39:04,320 --> 00:39:06,040
Speaker 3: earnings record and they pick the number and they send

803
00:39:06,080 --> 00:39:09,840
Speaker 3: you a check. Very limited fraud in the social security system.

804
00:39:09,880 --> 00:39:11,840
Speaker 3: I think that there's probably a lot of fraud in

805
00:39:11,840 --> 00:39:14,600
Speaker 3: some of the infrastructure and defense, and so our framework

806
00:39:14,640 --> 00:39:17,000
Speaker 3: has to be like where does the government know the least,

807
00:39:17,400 --> 00:39:20,320
Speaker 3: the government doesn't really know what's going into every element

808
00:39:20,360 --> 00:39:22,719
Speaker 3: of a defense spend or every element of you know,

809
00:39:23,239 --> 00:39:25,600
Speaker 3: how is the road being built or what is the

810
00:39:25,600 --> 00:39:28,040
Speaker 3: hospital doing? And when you have those big information, these symmetries,

811
00:39:28,080 --> 00:39:29,920
Speaker 3: that's where the big frauds. So if I think that

812
00:39:29,920 --> 00:39:33,600
Speaker 3: there's one hundred billion dollars just for Medicare and medicaid spending,

813
00:39:33,719 --> 00:39:36,600
Speaker 3: or maybe you know, throw in the advantage spending and

814
00:39:36,640 --> 00:39:39,000
Speaker 3: the you know, other the federal employees health benefits and

815
00:39:39,000 --> 00:39:41,040
Speaker 3: the VA. To get to the hundred billion number of

816
00:39:41,080 --> 00:39:43,680
Speaker 3: that healthcare fraud, I think that it very easily that

817
00:39:43,800 --> 00:39:47,400
Speaker 3: hundred billion dollars also occurs. Yet again in some of

818
00:39:47,440 --> 00:39:48,880
Speaker 3: these other like defense, I'm sure.

819
00:39:48,640 --> 00:39:52,440
Speaker 2: That there's plan Jensen leader luis amazing. Thank you so

820
00:39:52,520 --> 00:39:53,440
Speaker 2: much for coming on them.

821
00:39:53,440 --> 00:39:54,319
Speaker 3: Park, Thanks for having me.

822
00:40:07,120 --> 00:40:11,360
Speaker 2: Tracy. I am pro getting rid of fraud. No, I've

823
00:40:11,440 --> 00:40:13,160
Speaker 2: for real, I don't take a lot of it.

824
00:40:13,160 --> 00:40:15,200
Speaker 1: It seems like a low bar. Although you did ask

825
00:40:15,239 --> 00:40:17,880
Speaker 1: repeatedly how you could commit it, but I know that

826
00:40:18,000 --> 00:40:19,840
Speaker 1: was for informational purposes only.

827
00:40:20,080 --> 00:40:22,680
Speaker 2: No, I'm going to actually I'm not going to get

828
00:40:22,719 --> 00:40:24,920
Speaker 2: into the business of medical fraud. I'm going to get

829
00:40:24,920 --> 00:40:28,439
Speaker 2: into the business of being one of those independent whistleblowers

830
00:40:28,440 --> 00:40:30,920
Speaker 2: to bounty hunters. Yeah, that sounds great, but I feel

831
00:40:30,920 --> 00:40:32,360
Speaker 2: like I need to know how it works so I

832
00:40:32,400 --> 00:40:35,000
Speaker 2: can identify it. But I really do think, you know,

833
00:40:35,120 --> 00:40:38,399
Speaker 2: setting aside, setting aside everything fraud is bad.

834
00:40:38,840 --> 00:40:40,759
Speaker 1: I do think, and I kind of said this in

835
00:40:40,800 --> 00:40:43,279
Speaker 1: the beginning, that we can all agree that fraud and

836
00:40:43,440 --> 00:40:45,680
Speaker 1: waste is bad, like no matter where you fall on

837
00:40:45,719 --> 00:40:49,080
Speaker 1: the political spectrum, because if you free up money that's

838
00:40:49,160 --> 00:40:52,000
Speaker 1: not doing anything, then you could, in theory, put it

839
00:40:52,040 --> 00:40:55,200
Speaker 1: to a different use and get more bang for your bucks,

840
00:40:55,200 --> 00:40:59,120
Speaker 1: so to speak. I think obviously, and we touched on this,

841
00:40:59,239 --> 00:41:03,359
Speaker 1: like a lot of these decisions over what's fraudulent or

842
00:41:03,600 --> 00:41:07,400
Speaker 1: especially what's wasteful maybe not necessarily fraud come down to

843
00:41:07,760 --> 00:41:10,279
Speaker 1: specific judgments and they can be subjective, and I think

844
00:41:10,280 --> 00:41:12,480
Speaker 1: that's where a lot of the disagreement is going to

845
00:41:12,480 --> 00:41:16,320
Speaker 1: be going forward. But I do think the point about

846
00:41:16,719 --> 00:41:20,440
Speaker 1: using the data better. The government must have some amazing data.

847
00:41:20,840 --> 00:41:23,240
Speaker 1: We kind of talked about it, especially in these specific

848
00:41:23,280 --> 00:41:24,960
Speaker 1: sectors like healthcare.

849
00:41:25,400 --> 00:41:27,719
Speaker 2: Two things on the data that were really interesting. So

850
00:41:27,840 --> 00:41:30,920
Speaker 2: one is just this idea that if you're a talented

851
00:41:31,040 --> 00:41:32,600
Speaker 2: data scientist.

852
00:41:32,360 --> 00:41:33,120
Speaker 1: Go to the doge.

853
00:41:33,239 --> 00:41:34,960
Speaker 2: And this has come up in some of our past

854
00:41:35,000 --> 00:41:37,919
Speaker 2: episodes that we've done with a few other guests. There

855
00:41:37,960 --> 00:41:41,719
Speaker 2: does seem to be this structural issue, right of how

856
00:41:41,800 --> 00:41:45,000
Speaker 2: government pays and whether the how compelling a job in

857
00:41:45,040 --> 00:41:48,000
Speaker 2: government is, et cetera. And so there does seem to

858
00:41:48,040 --> 00:41:50,759
Speaker 2: be an issue with how do you staff up a

859
00:41:50,800 --> 00:41:54,480
Speaker 2: big team of data scientists that are incentive and have

860
00:41:54,560 --> 00:41:57,640
Speaker 2: the agency and capacity to use that data. Do something

861
00:41:57,800 --> 00:42:00,880
Speaker 2: when they discover it. And then you know, very interesting

862
00:42:01,520 --> 00:42:05,480
Speaker 2: comment that last one about it's very hard to defraud

863
00:42:05,520 --> 00:42:08,680
Speaker 2: social security, and so this idea of like where is

864
00:42:08,719 --> 00:42:12,600
Speaker 2: the fraud most likely to exist? Areas in which there

865
00:42:12,640 --> 00:42:17,160
Speaker 2: is some limited asymmetrical as economists like to use information,

866
00:42:17,360 --> 00:42:20,440
Speaker 2: which is, you know, the government doesn't know what happens

867
00:42:20,800 --> 00:42:24,440
Speaker 2: when you are not you and I go into a doctor, right,

868
00:42:24,440 --> 00:42:28,000
Speaker 2: there's some level of information asymmetry there. It doesn't really

869
00:42:28,080 --> 00:42:30,439
Speaker 2: know what kind of walker you or I are going

870
00:42:30,480 --> 00:42:33,880
Speaker 2: to need in thirty or forty years to get around,

871
00:42:33,880 --> 00:42:37,000
Speaker 2: et cetera. And then furthermore, we didn't touch on it,

872
00:42:37,040 --> 00:42:40,719
Speaker 2: but I do think or absolutely as a podcast, are

873
00:42:40,719 --> 00:42:42,759
Speaker 2: going to need to do more, should do way more

874
00:42:42,800 --> 00:42:45,799
Speaker 2: on defense spending. There's a million angles that we have

875
00:42:45,840 --> 00:42:48,239
Speaker 2: to do on that. But you could see like we

876
00:42:48,280 --> 00:42:50,440
Speaker 2: don't really know what went into the assembly of this

877
00:42:50,520 --> 00:42:52,920
Speaker 2: and the cost of this program and is this part

878
00:42:53,000 --> 00:42:55,879
Speaker 2: really worth this much money or maybe as a competition thing.

879
00:42:56,080 --> 00:42:58,920
Speaker 2: There's a lot of fruit there for future episodes.

880
00:42:58,960 --> 00:43:01,040
Speaker 1: But I do think one of the big tensions here

881
00:43:01,200 --> 00:43:05,719
Speaker 1: is the sort of government generalists versus like the experts

882
00:43:05,760 --> 00:43:08,640
Speaker 1: that they're listening to what I mean is, for instance,

883
00:43:08,680 --> 00:43:12,200
Speaker 1: if you're a defense contractor and you're building I don't know,

884
00:43:12,360 --> 00:43:16,120
Speaker 1: like a submarine launch pad or whatever, like the government

885
00:43:16,120 --> 00:43:18,719
Speaker 1: official isn't necessarily going to know all the nuts and

886
00:43:18,760 --> 00:43:21,680
Speaker 1: bolts that need to go into that. And so yeah,

887
00:43:21,719 --> 00:43:24,760
Speaker 1: I always wonder how you sort of overcome that informational gap.

888
00:43:24,840 --> 00:43:26,600
Speaker 2: There's a lot there. Let's do more on this topic.

889
00:43:26,680 --> 00:43:28,120
Speaker 1: All right, shall we leave it there for now?

890
00:43:28,239 --> 00:43:29,000
Speaker 2: Let's leave it there.

891
00:43:29,239 --> 00:43:31,880
Speaker 1: This has been another episode of the All Thoughts podcast.

892
00:43:32,000 --> 00:43:34,800
Speaker 1: I'm Tracy Alloway. You can follow me at Tracy Alloway.

893
00:43:34,960 --> 00:43:37,960
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.

894
00:43:38,160 --> 00:43:42,200
Speaker 2: Follow our guest Jetson leader Luis. He's at jetson Econ. Also,

895
00:43:42,239 --> 00:43:44,680
Speaker 2: he has a number of papers on his website that

896
00:43:44,719 --> 00:43:47,120
Speaker 2: you can just click on and go read. They're all

897
00:43:47,120 --> 00:43:51,120
Speaker 2: really fascinating. Follow our producers Carmen Rodriguez at Carman Armann

898
00:43:51,200 --> 00:43:54,279
Speaker 2: dash Ol Bennett at Dashbot and kill Brooks at Kilbrooks.

899
00:43:54,560 --> 00:43:57,239
Speaker 2: Thank you to our producer Moses Onam. For more Odd

900
00:43:57,320 --> 00:44:00,239
Speaker 2: Laws content, go to Bloomberg dot com slash oddlog. We

901
00:44:00,239 --> 00:44:03,200
Speaker 2: have transcripts, a blog, and a newsletter, and you can

902
00:44:03,280 --> 00:44:06,160
Speaker 2: chet about all of these topics in our discord with

903
00:44:06,280 --> 00:44:09,760
Speaker 2: fellow listeners Discord, dot gg, slash up, lots.

904
00:44:09,800 --> 00:44:12,120
Speaker 1: And if you enjoy all lots, if you like it

905
00:44:12,160 --> 00:44:15,320
Speaker 1: when we dive deep into the data around government fraud,

906
00:44:15,440 --> 00:44:18,160
Speaker 1: then please leave us a positive review on your favorite

907
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Speaker 1: podcast platform. And remember, if you are a Bloomberg subscriber,

908
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Speaker 1: Apple Podcasts and follow the instructions there. Thanks for listening.

912
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Speaker 1: Eight