WEBVTT - How Fraudsters Are Bilking the Government Out of Billions of Dollars

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<v Speaker 1>Bloomberg Audio Studios, Podcasts, Radio News.

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<v Speaker 2>Hello and welcome to another episode of The Odd Laws podcast.

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<v Speaker 2>I'm Joe Wisenthal and I'm Tracy Alloway. Tracy, what do

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<v Speaker 2>you think about DOGE.

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<v Speaker 1>The coin or the new Department of Government Efficiency the latter.

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<v Speaker 1>Here's what I will say. Part of me hates that

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<v Speaker 1>government efficiency is being politicized in this way, because if

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<v Speaker 1>you think that government services are a desirable thing to have,

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<v Speaker 1>then you should definitely be against waste and inefficiency and

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<v Speaker 1>fraud in that market, because at a minimum, if you're

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<v Speaker 1>wasting money, you could be using that money to do

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<v Speaker 1>even more. And then, obviously, if you think that government

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<v Speaker 1>is just bad in general, then I imagine that you

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<v Speaker 1>also think that the government wasting money is also bad.

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<v Speaker 2>There's no constituency there probably shouldn't be an ideological constituency

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<v Speaker 2>or political constituency for waste, right.

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<v Speaker 1>Yeah, exactly, Like I feel like we should all agree

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<v Speaker 1>on this, But also I hate the way it's kind

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<v Speaker 1>of unrolling. Let's put it that way.

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<v Speaker 2>I would say I broadly agree waste is bad. Whether

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<v Speaker 2>the existing makeup of this sort of quasi blue ribbon

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<v Speaker 2>commission to get rid of waste whatever that means is

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<v Speaker 2>actually going to do it. Look, I'll be open minded,

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<v Speaker 2>but I like the premise of cracking down on waste.

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<v Speaker 2>I will just say that, you know what the other

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<v Speaker 2>thing is. So first of all, part of the issue

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<v Speaker 2>here is that waste is probably difficult to define. Fraud

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<v Speaker 2>in some cases is probably difficult to define. There will

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<v Speaker 2>probably be political fights over certain types of spending that

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<v Speaker 2>it's like, you call this waste, I call this a

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<v Speaker 2>good allocation of whatever. I imagine many of the political fights

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<v Speaker 2>around this will sort of revolve around some of these questions.

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<v Speaker 2>There's a lot there. But I'm glad we generally agree

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<v Speaker 2>that fraud is bad.

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<v Speaker 1>Fraud is bad, waste is bad. But I think you're

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<v Speaker 1>absolutely right that the definitions are going to be crucial, right,

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<v Speaker 1>And this is where I worry about the politicization, because

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<v Speaker 1>you can just come in and say like, oh, well,

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<v Speaker 1>I don't like this, so I'm going to call this

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<v Speaker 1>a waste and go on from there. But I think

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<v Speaker 1>we should talk about it because you know, this has

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<v Speaker 1>come up in a number of episodes, specifically on the

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<v Speaker 1>PPP post pandemic, and so it's clearly something that is

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<v Speaker 1>on people's minds totally.

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<v Speaker 2>Right now, we are recording this Thursday, November fourteenth. We

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<v Speaker 2>are in a moment where resources in the economy are constrained. Right,

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<v Speaker 2>the unemployment rate is low, inflation continue used to be

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<v Speaker 2>by some measures above the Fed's goal. It is not

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<v Speaker 2>crazy from like a macro standpoint to think we need

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<v Speaker 2>to make things more efficient and better to have a

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<v Speaker 2>better use of our real resources right now.

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<v Speaker 1>And one, this is your other middle aged man thing.

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<v Speaker 1>You know, you've decided to become like a real resource

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<v Speaker 1>constraint guy.

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<v Speaker 2>Right yeah, I'm like, oh, we got we got to

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<v Speaker 2>crack down. You have to make tough decisions, to make

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<v Speaker 2>tough decisions. We're taking the candy away from the kids.

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<v Speaker 2>I'm going to be one of those.

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<v Speaker 1>Here's a tough decision. If you're going to talk to

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<v Speaker 1>someone about government waste, would you talk to Elon or Vivek.

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<v Speaker 1>There's two.

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<v Speaker 2>Well, Elon would get more downloads if we had a podcast,

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<v Speaker 2>so I would talk to Elon. But we actually have

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<v Speaker 2>a better guest. I think we have the perfect guest

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<v Speaker 2>to talk about government waste fraud where we might be

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<v Speaker 2>able to move the dial in a substantive way on

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<v Speaker 2>this kind of stuff. We're going to be speaking with

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<v Speaker 2>Jetson Leader Luis. He is an assistant professor at the

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<v Speaker 2>Questionroom School of Business at Boston University. He's also a

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<v Speaker 2>faculty research fellow at the nb ARE and this is

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<v Speaker 2>what he studies, particularly areas around fraud and how companies

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<v Speaker 2>defraud the government wasting millions, probably billions of dollars. So, Jetson,

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<v Speaker 2>thank you so much for coming.

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<v Speaker 3>On odd Lots, Thanks so much for having me.

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<v Speaker 2>Why do you describe your research? I kind of described it,

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<v Speaker 2>but why did you describe what you do in your background?

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<v Speaker 3>Yeah? That's great. So I'm an assistant professor. I study economics,

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<v Speaker 3>and in particular I'm interested in questions about fraud in

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<v Speaker 3>government spending. I receive my PhD from MIT Economics in

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<v Speaker 3>twenty twenty, and I've written a number of papers trying

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<v Speaker 3>to explore the mechanisms the government can and does use

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<v Speaker 3>to cut out fraud in it in public expenditure.

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<v Speaker 1>Was there a particular moment or reason that drew you

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<v Speaker 1>to this particular field.

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<v Speaker 3>I think fraud in government spending has historically been underanalyzed,

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<v Speaker 3>and in particular in the healthcare system, where I've done

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<v Speaker 3>a lot of work. There are really big and impactful

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<v Speaker 3>policies that are being used to try to eliminate fraud,

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<v Speaker 3>but historically we didn't really understand what worked and what

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<v Speaker 3>didn't work and why it worked, and so I think,

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<v Speaker 3>you know, there was a big opportunity in the research there.

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<v Speaker 3>And I've always been interested in questions of bad behavior.

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<v Speaker 3>I have a paper that I started early in my

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<v Speaker 3>career on fraud in the World Bank that we just

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<v Speaker 3>got accepted at the journal World Development. And so overall,

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<v Speaker 3>I'm really interested in this question about, you know, where's

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<v Speaker 3>the money going and how can we you know, fix

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<v Speaker 3>that to make sure that the federal funds are being

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<v Speaker 3>used for people who need them.

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<v Speaker 2>I definitely want to talk a little bit about fixing

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<v Speaker 2>the problem and identifying the problem and so forth, But

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<v Speaker 2>actually I really want to start on how to defraud

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<v Speaker 2>the government because no, for real, because I sometimes we'll

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<v Speaker 2>see headlines it's like so and so arrested for insurance

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<v Speaker 2>fraud of some sort, and you know that's bad, But

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<v Speaker 2>I also like have there's like weird thing that happens

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<v Speaker 2>in my head where I'm like, I wouldn't even know

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<v Speaker 2>how to defraud.

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<v Speaker 1>Tell us what's the most accessible way to defraud the government.

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<v Speaker 2>So, like, give us an example, or when we talk about, okay,

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<v Speaker 2>fraud in the medical system, what are people doing? What

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<v Speaker 2>is a classical form of defrauding to government in the

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<v Speaker 2>world of healthcare.

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<v Speaker 3>So, in the world of healthcare, there are some really

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<v Speaker 3>obvious frauds that have persisted for years and that I

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<v Speaker 3>think we're finally maybe starting to wrap our hands around.

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<v Speaker 3>And one that comes to mind immediately is the ambulance market.

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<v Speaker 2>Okay, say more so.

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<v Speaker 3>This was actually described to me when I first heard

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<v Speaker 3>it by a colleague friend who works for the federal

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<v Speaker 3>government as the perfect healthcare fraud. And so ambulance services

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<v Speaker 3>are paid for by Medicare. Medicare is the old age

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<v Speaker 3>health insurance program for Americans. We spend more than eight

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<v Speaker 3>hundred billion dollars a year on this program. We spend

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<v Speaker 3>another seven hundred billion dollars a year on Medicaid. So

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<v Speaker 3>we're talking about one point five trillion dollars of outlays

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<v Speaker 3>to these programs. It's very hard for the government to

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<v Speaker 3>ensure that every dollar that's going out is legitimate, right,

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<v Speaker 3>It's a volume problem. Ambulance services are highly reimbursed and

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<v Speaker 3>low overhead. If you want to start an ambulance company,

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<v Speaker 3>you need to buy an ambulance. That's like thirty grand

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<v Speaker 3>used online. You can actually go and google it yourself.

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<v Speaker 3>You can go buy an ambulance, and then you need

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<v Speaker 3>a couple of employees. It's actually a super low overhead business,

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<v Speaker 3>which means it's easy for people to start starting around.

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<v Speaker 3>We think two thousand and three, the market for ambulance services,

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<v Speaker 3>and in particular repetitive non emergency ambulance services, started getting

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<v Speaker 3>saturated by intentional fraudulent actors.

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<v Speaker 1>Wait, what's a non emergency ambulance?

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<v Speaker 3>Non emergency ambulance is a patient who needs to go

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<v Speaker 3>to a service because they are sick enough that they

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<v Speaker 3>can't ride in a taxi or take the train, and

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<v Speaker 3>they the only safe way for them to get to

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<v Speaker 3>a service is in an ambulance. And in particular, this

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<v Speaker 3>really blew up in the dialysis industry. Dialysis patients, there

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<v Speaker 3>are about half a million of them. We actually spend

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<v Speaker 3>I think you know this, one percent of the federal

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<v Speaker 3>budget on the dialysis program.

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<v Speaker 2>Incredible, set not.

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<v Speaker 3>One percent of medicare. One percent of the federal budget

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<v Speaker 3>is the dialysis program. We do not in general pay

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<v Speaker 3>for ambulance rides or taxi rides for these people to

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<v Speaker 3>go to and from the visits. They are responsible for

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<v Speaker 3>getting themselves to the clinic every day, three times a week,

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<v Speaker 3>generally for a few hours, and that's in perpetuity. It's

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<v Speaker 3>very challenging to get a kidney and therefore to get

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<v Speaker 3>off of diale. So we had this system, and this

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<v Speaker 3>is sort of the canonical Medicare fraud. We build in

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<v Speaker 3>a little thing for the few people who need it,

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<v Speaker 3>and that turns into a loophole through which bad actors

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<v Speaker 3>drive a truck. So we built in this provision, which

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<v Speaker 3>is if the only safe way that you can get

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<v Speaker 3>to the dialysis clinic is in an ambulance, Medicare will

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<v Speaker 3>pay for an ambulance, and they pay for it at

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<v Speaker 3>a competitive rate for the ambulance companies, at say two

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<v Speaker 3>hundred and fifty dollars for a one way ride. Now

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<v Speaker 3>that's not that much money for a real ambulance, but

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<v Speaker 3>it's a heck of a lot of money for a taxi.

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<v Speaker 3>And what happened is thousands of firms around the country

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<v Speaker 3>opened with the express intention not of giving people serious

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<v Speaker 3>medical care, but of becoming an expensive ambulance taxi and

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<v Speaker 3>build the government. We have one hundred percent data from

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<v Speaker 3>the dialysis system. We can see all of these payments

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<v Speaker 3>more than seven billion dollars for non emergency ambulance transportation

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<v Speaker 3>over the following ten years, seven point seven billion dollars,

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<v Speaker 3>and a lot of it was fraud, and the government

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<v Speaker 3>cracked down. The government really tried to crack down, and

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<v Speaker 3>in particular, the used a number of tools. The first

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<v Speaker 3>one is they started throwing people in prison. This is

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<v Speaker 3>what you say, you see in the headlines. Yeah, but

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<v Speaker 3>it's so easy to start an ambulance company that we'd

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<v Speaker 3>see these stories where, you know, someone gets busted and

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<v Speaker 3>their family member goes and opens a company next door

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<v Speaker 3>the next day. And this persisted for years, with thousands

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<v Speaker 3>of companies and billions of dollars of spending just down

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<v Speaker 3>the drain.

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<v Speaker 1>I imagine some of the difficulty is also deciding who genuinely

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<v Speaker 1>needs an ambulance ride and who doesn't. Right, So this

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<v Speaker 1>is something that I never quite understand about US healthcare

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<v Speaker 1>in general. The first ten years of my adulthood were

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<v Speaker 1>in the UK, and there's a national health service there,

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<v Speaker 1>and if the doctor told you you needed something. You know,

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<v Speaker 1>you got that something. Maybe it would take a while,

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<v Speaker 1>but eventually you would get it, whereas in the US

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<v Speaker 1>you seem to have all these decision makers in the process,

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<v Speaker 1>and yet we're talking about medical care, which you would

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<v Speaker 1>think would need to be dictated by like highly trained

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<v Speaker 1>medical doctors.

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<v Speaker 3>This is super interesting that that's your intuition, because that's

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<v Speaker 3>exactly how they fixed it. So the way that Medicare

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<v Speaker 3>closed this loophole was by requiring what's called prior authorization.

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<v Speaker 3>Now if instead of going to an ambulance company and saying,

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<v Speaker 3>you know, please give me this ride, or the even worse,

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<v Speaker 3>the ambulance company coming to the patient and saying do

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<v Speaker 3>you want a taxi ride, which is actually what was happening,

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<v Speaker 3>instead they required that a physicians signed off and say, look,

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<v Speaker 3>this patient is actually sick, they're bedridden. There's no other

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<v Speaker 3>way that they can get to dialysis this week. And

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<v Speaker 3>if you didn't have the doctor's note, Medicare didn't pay.

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<v Speaker 3>And we estimate that around the timing of them implementing

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<v Speaker 3>this prior authorization requirement, which they rolled out in different

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<v Speaker 3>states at different times. This is like what economists love

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<v Speaker 3>in our research and actual test a difference, and difference

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<v Speaker 3>exactly when they rolled this out in different places, in

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<v Speaker 3>different times. We see a sixty seven percent drop in

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<v Speaker 3>spending the next month, persistent, and not only that, we

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<v Speaker 3>can then trace the patients and say were these patients harmed?

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<v Speaker 3>Did they actually miss their dialysis visits and end up

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<v Speaker 3>in the hospital, And we find no evidence at all

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<v Speaker 3>of negative patient health effects, And so we actually saved

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<v Speaker 3>billions of dollars by putting in something that was so basic,

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<v Speaker 3>which is this the doctor sign?

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<v Speaker 1>Why didn't they do it before?

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<v Speaker 3>The structure of Medicare is largely disaggregated, where individuals are

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<v Speaker 3>able to go to different services as long as they

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<v Speaker 3>qualify for them, and some of those are doctor's visits, hospitals,

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<v Speaker 3>medical equipment, pharmaceuticals. We pay for a lot of things,

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<v Speaker 3>and there is a real worry that requiring too much

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<v Speaker 3>paperwork can burden the system, and we don't want to

0:11:25.960 --> 0:11:29.160
<v Speaker 3>turn the Medicare system into an even more heavily administrative

0:11:29.160 --> 0:11:33.120
<v Speaker 3>burden system. So there are different qualification rules, but largely

0:11:33.200 --> 0:11:38.520
<v Speaker 3>what happens is the qualification rules are not always enforced upfront.

0:11:38.720 --> 0:11:41.400
<v Speaker 3>We do a lot of requiring people to follow the rules,

0:11:41.400 --> 0:11:43.400
<v Speaker 3>maybe we do some audits, maybe we chase after them

0:11:43.440 --> 0:11:47.319
<v Speaker 3>with criminal lawsuits afterwards. But largely there are circumstances where

0:11:47.520 --> 0:11:51.400
<v Speaker 3>nefarious actors, it's a relatively high trusystem. Nefarious actors will

0:11:51.600 --> 0:11:53.679
<v Speaker 3>find these loopholes and drive a truck through them. And

0:11:53.720 --> 0:11:55.199
<v Speaker 3>I can talk about a million examples of this.

0:11:55.480 --> 0:11:58.640
<v Speaker 2>Well, let's talk about so Okay, it sounds like the

0:11:58.720 --> 0:12:02.400
<v Speaker 2>ambulance from taken care of more or less.

0:12:02.600 --> 0:12:05.360
<v Speaker 3>So the ambulance fraud is less than it used to be.

0:12:05.440 --> 0:12:07.800
<v Speaker 3>And that's the dialysis ambulance fraud. Oh yeah, this paper

0:12:07.840 --> 0:12:09.760
<v Speaker 3>was just accepted at the General Political Economy, So we're

0:12:09.800 --> 0:12:11.000
<v Speaker 3>super happy. Shout out to my.

0:12:10.960 --> 0:12:13.160
<v Speaker 2>Congratulations who did a great job there.

0:12:13.800 --> 0:12:16.240
<v Speaker 3>The ambulance market itself has other frauds, right, we're talking

0:12:16.240 --> 0:12:18.840
<v Speaker 3>about one type, which is this repetitive dialysis fraud. There

0:12:18.840 --> 0:12:22.679
<v Speaker 3>are still lots of unnecessary ambulance rides, ghost ambulance rides

0:12:22.679 --> 0:12:24.679
<v Speaker 3>where patients don't even get in the ambulance and bills

0:12:24.679 --> 0:12:27.480
<v Speaker 3>are sent. One question is what can the government do?

0:12:27.960 --> 0:12:29.640
<v Speaker 3>And part of it is I think that there needs

0:12:29.679 --> 0:12:32.440
<v Speaker 3>to be a better focus on using data to detect

0:12:32.640 --> 0:12:33.440
<v Speaker 3>and stop the fraud.

0:12:33.559 --> 0:12:35.520
<v Speaker 1>Yeah, talk to us about the data because I imagine,

0:12:35.559 --> 0:12:39.560
<v Speaker 1>like you're talking about government spending and programs, plus in

0:12:39.559 --> 0:12:42.640
<v Speaker 1>some instances the medical industry, there must be interesting data

0:12:42.880 --> 0:12:43.640
<v Speaker 1>available to you.

0:12:44.320 --> 0:12:47.240
<v Speaker 3>So I have fantastic access to data. I use one

0:12:47.320 --> 0:12:50.080
<v Speaker 3>hundred percent sample Medicare claims data from nineteen ninety nine

0:12:50.120 --> 0:12:53.839
<v Speaker 3>through twenty nineteen for all inpatient and outpatient services, durbal

0:12:53.880 --> 0:12:56.200
<v Speaker 3>medical equipments. I can see twenty percent of physician office

0:12:56.240 --> 0:13:00.319
<v Speaker 3>visits and party pharmaceuticals. So it's an ridiculous volume. And

0:13:01.080 --> 0:13:03.520
<v Speaker 3>you know, I'm very equipped with data. I teach data

0:13:03.520 --> 0:13:05.640
<v Speaker 3>analysis and I have you know, PhD students and other

0:13:05.640 --> 0:13:07.600
<v Speaker 3>professors who work with me. And even for us, it's

0:13:07.600 --> 0:13:09.480
<v Speaker 3>a big problem. How do we actually wrap our hands

0:13:09.520 --> 0:13:13.120
<v Speaker 3>around this? Yeah, the government has not historically invested very

0:13:13.160 --> 0:13:16.679
<v Speaker 3>well in its data analysis for anti fraud. Part of

0:13:16.720 --> 0:13:20.160
<v Speaker 3>the reason is that the organizations that are responsible for this,

0:13:20.360 --> 0:13:22.199
<v Speaker 3>which are the Department of Justice and the Office of

0:13:22.280 --> 0:13:26.760
<v Speaker 3>the Inspector General. Those career lawyers are fantastic. I cannot

0:13:26.760 --> 0:13:29.800
<v Speaker 3>say enough positive things about my colleagues at the Department

0:13:29.840 --> 0:13:31.720
<v Speaker 3>of Justice and the Office of the Inspector General, but

0:13:31.760 --> 0:13:34.160
<v Speaker 3>there are too few of them. We do not pay

0:13:34.200 --> 0:13:36.720
<v Speaker 3>them very well, and they are not data analysts. They

0:13:36.760 --> 0:13:37.280
<v Speaker 3>are lawyers.

0:13:37.760 --> 0:13:42.520
<v Speaker 2>Okay, so you mentioned there's still some ambulance the dialysis specifically,

0:13:42.800 --> 0:13:46.480
<v Speaker 2>sounds like that was mostly taken care of. There's other

0:13:46.640 --> 0:13:50.800
<v Speaker 2>ambulance fraud out there. You mentioned that some people that

0:13:50.840 --> 0:13:54.439
<v Speaker 2>there's billing ghost ambulances or people never even ride the ambulance.

0:13:54.720 --> 0:13:57.360
<v Speaker 2>What's hot right now, what's the new ambulance fraud?

0:13:57.520 --> 0:14:00.360
<v Speaker 3>So what's amazing here is that it seems like it's

0:14:00.400 --> 0:14:03.760
<v Speaker 3>a constantly evolving marketplace. Right We have to think fraud

0:14:03.840 --> 0:14:07.200
<v Speaker 3>is a technology where people figure out a loophole and

0:14:07.240 --> 0:14:09.360
<v Speaker 3>then they tell their friends and these things spread and

0:14:09.400 --> 0:14:11.800
<v Speaker 3>eventually the government catches up, and so we're playing cat

0:14:11.840 --> 0:14:14.680
<v Speaker 3>and mouse every year. Right now. I think it's wound care.

0:14:14.960 --> 0:14:16.440
<v Speaker 2>Okay, say more about wound care.

0:14:16.480 --> 0:14:20.120
<v Speaker 3>There's been a rise in these expensive treatments for patients

0:14:20.120 --> 0:14:22.400
<v Speaker 3>with non healing wounds. So, if you're diabetic, you're likely

0:14:22.400 --> 0:14:23.960
<v Speaker 3>to have neuropathy, and one of the things that comes

0:14:23.960 --> 0:14:27.360
<v Speaker 3>with diabetic neuropathy is that you often have wounds, particularly

0:14:27.360 --> 0:14:29.800
<v Speaker 3>on their feet, where the patient doesn't heal. There are

0:14:29.840 --> 0:14:33.800
<v Speaker 3>some modern technology skin substitute things you can graft onto

0:14:33.800 --> 0:14:36.440
<v Speaker 3>these wounds that seem like they pay pretty well and

0:14:36.640 --> 0:14:38.920
<v Speaker 3>potentially even work, and then a few doctors have just

0:14:38.920 --> 0:14:40.880
<v Speaker 3>started spending millions of dollars on that. But that's a

0:14:40.880 --> 0:14:44.560
<v Speaker 3>flash in the pan. Historically, we see fraud really rife

0:14:44.560 --> 0:14:48.360
<v Speaker 3>in the durable medical equipment industry. There's been fraud in

0:14:48.520 --> 0:14:52.800
<v Speaker 3>basically everything that healthcare touches. Another thing right now that's

0:14:53.080 --> 0:14:56.560
<v Speaker 3>really popular. We're seeing a lot of fraud in vascular

0:14:56.600 --> 0:15:00.280
<v Speaker 3>care that is helping patients who have collapsed vain be

0:15:00.360 --> 0:15:02.920
<v Speaker 3>able to receive intravenous treatments. But again, it's just like

0:15:03.200 --> 0:15:03.440
<v Speaker 3>just to.

0:15:04.360 --> 0:15:08.840
<v Speaker 2>Drill into specifics, let's just durable medical equipment fraud. Yes,

0:15:09.080 --> 0:15:12.320
<v Speaker 2>I want to get into it. What am I doing so?

0:15:12.840 --> 0:15:14.760
<v Speaker 3>Or you want to get into durable medical equipment fraud,

0:15:14.760 --> 0:15:16.440
<v Speaker 3>Well you're in luck because it's you know, durable medical

0:15:16.440 --> 0:15:18.760
<v Speaker 3>equipment has been the wild West of the healthcare system

0:15:18.760 --> 0:15:21.880
<v Speaker 3>for twenty years with billions of dollars of fraud. I

0:15:21.880 --> 0:15:23.520
<v Speaker 3>have to admit I'm writing a paper on it right now.

0:15:23.560 --> 0:15:25.600
<v Speaker 3>That's I'm excited. I know a lot about it. So

0:15:25.920 --> 0:15:28.200
<v Speaker 3>you remember the scooter store they used to like advertise

0:15:28.240 --> 0:15:30.640
<v Speaker 3>on late night TV. Are you an old person?

0:15:32.360 --> 0:15:32.600
<v Speaker 1>Number.

0:15:32.640 --> 0:15:35.240
<v Speaker 3>Would you like a free wheelchair? Yeah? So there's been

0:15:35.280 --> 0:15:39.120
<v Speaker 3>some excellent kind of investigative journalism on this so durable

0:15:39.160 --> 0:15:42.480
<v Speaker 3>medical equipment. I want people to think walkers, wheelchairs, oxygen pumps,

0:15:42.480 --> 0:15:45.400
<v Speaker 3>hospital beds in their home. Things that people need that

0:15:45.480 --> 0:15:48.480
<v Speaker 3>are supposed to be permanent. You know, seatpat machines, yep.

0:15:49.440 --> 0:15:54.440
<v Speaker 3>And these are largely given by suppliers that are sometimes

0:15:54.480 --> 0:15:57.480
<v Speaker 3>big national firms and sometimes small mom and pop shops.

0:15:57.760 --> 0:15:59.800
<v Speaker 3>I want you to think about Flora. This is sort

0:15:59.800 --> 0:16:03.760
<v Speaker 3>of a Florida story. If you're interested in selling someone

0:16:04.440 --> 0:16:07.880
<v Speaker 3>a you know, a fraudulent walker or pomp or something

0:16:07.960 --> 0:16:09.320
<v Speaker 3>like that, it's actually.

0:16:09.080 --> 0:16:10.760
<v Speaker 2>Wait, what does it mean? A fraudulent walker one that

0:16:10.840 --> 0:16:11.960
<v Speaker 2>doesn't work well.

0:16:11.840 --> 0:16:12.440
<v Speaker 1>That you don't need.

0:16:12.560 --> 0:16:15.080
<v Speaker 3>I guess this is super interesting, right, So what do

0:16:15.120 --> 0:16:18.040
<v Speaker 3>we mean when we say fraud? Healthcare fraud has different types, right,

0:16:18.080 --> 0:16:19.520
<v Speaker 3>and I can break it. There are three types of

0:16:19.520 --> 0:16:21.960
<v Speaker 3>health care fraud. There's upcoding. That's where I sell you

0:16:22.040 --> 0:16:24.360
<v Speaker 3>a little push wheelchair, but I go build a government

0:16:24.400 --> 0:16:27.200
<v Speaker 3>for a super lux automatic wheelchair. We call that upcoding.

0:16:27.600 --> 0:16:31.280
<v Speaker 3>There's medical necessity fraud. That's where we say that a

0:16:31.320 --> 0:16:34.960
<v Speaker 3>patient needs something and they don't. And then there's substandard care.

0:16:35.200 --> 0:16:37.440
<v Speaker 3>That's where we have a patient who actually does need

0:16:37.480 --> 0:16:39.960
<v Speaker 3>something and we give them junk. And all of them

0:16:40.000 --> 0:16:43.240
<v Speaker 3>happen in all forms of medicine, but in particular and

0:16:43.320 --> 0:16:45.680
<v Speaker 3>durable medical equipment. I think it's a lot of medical

0:16:45.720 --> 0:16:48.400
<v Speaker 3>necessity fraud. I think we have patients who are getting

0:16:48.400 --> 0:16:50.800
<v Speaker 3>a knock on the door, high do you want this

0:16:50.920 --> 0:16:53.800
<v Speaker 3>fancy new device free to you? Now, what's really interesting

0:16:53.800 --> 0:16:56.720
<v Speaker 3>is we're actually supposed to collect a twenty percent copay

0:16:57.160 --> 0:16:59.760
<v Speaker 3>for the durable medical equipment products. But the fraud and

0:16:59.800 --> 0:17:02.800
<v Speaker 3>that's designed by medicare to make sure that patients aren't

0:17:02.800 --> 0:17:05.399
<v Speaker 3>getting stuff that they don't need, so they.

0:17:05.240 --> 0:17:06.200
<v Speaker 2>Have to have some skin in the game.

0:17:06.200 --> 0:17:08.440
<v Speaker 3>They're supposed to do. But if you're a fraudulent firm,

0:17:08.600 --> 0:17:10.840
<v Speaker 3>you just don't collect it. You're very happy to have

0:17:10.880 --> 0:17:13.720
<v Speaker 3>the government's eighty percent, and the patient wouldn't take it

0:17:13.760 --> 0:17:14.520
<v Speaker 3>if they had to pay.

0:17:14.640 --> 0:17:17.679
<v Speaker 1>Yeah, what's been the I'm trying to think how to

0:17:17.680 --> 0:17:20.960
<v Speaker 1>frame this, but I guess what's been the cultural or

0:17:21.359 --> 0:17:26.119
<v Speaker 1>incentive approach in government to stamping out fraud. If I

0:17:26.160 --> 0:17:30.160
<v Speaker 1>am a government official and I design a poor social

0:17:30.600 --> 0:17:33.000
<v Speaker 1>service program of some sort that has a bunch of

0:17:33.000 --> 0:17:36.399
<v Speaker 1>loopholes that ends up costing lots of money. Do I

0:17:36.400 --> 0:17:38.920
<v Speaker 1>get in trouble or do I get rewarded if I

0:17:39.000 --> 0:17:42.000
<v Speaker 1>managed to tweak the program so that it doesn't have

0:17:42.040 --> 0:17:42.919
<v Speaker 1>a lot of fraud in it.

0:17:43.960 --> 0:17:50.040
<v Speaker 3>The government generally underinvests and miss prices. It's anti fraud investments,

0:17:50.160 --> 0:17:52.960
<v Speaker 3>by which I mean when we consider how we are

0:17:53.080 --> 0:17:55.560
<v Speaker 3>measuring what the government is doing to stop fraud. You're

0:17:55.560 --> 0:17:59.760
<v Speaker 3>talking about these career civil servants and how we reward them. Historically,

0:18:00.160 --> 0:18:03.359
<v Speaker 3>the focus has been on how much are you getting back?

0:18:03.960 --> 0:18:05.919
<v Speaker 3>And I've made this point in a bunch of research,

0:18:05.920 --> 0:18:07.960
<v Speaker 3>and I recently released a white paper through the Center

0:18:08.000 --> 0:18:11.520
<v Speaker 3>for Medicare and Medicare Medicaid Services saying how much money

0:18:11.560 --> 0:18:14.159
<v Speaker 3>you get back is irrelevant. That is the wrong number.

0:18:14.400 --> 0:18:16.800
<v Speaker 3>That is really the number that everyone in government is

0:18:16.840 --> 0:18:20.160
<v Speaker 3>focused on. When you say anti fraud recovery. We caught

0:18:20.480 --> 0:18:23.040
<v Speaker 3>this many people, we put this many people in jail,

0:18:23.280 --> 0:18:26.560
<v Speaker 3>and we got a billion dollars back this year. And

0:18:26.600 --> 0:18:28.560
<v Speaker 3>the point that I've made is the money you're getting

0:18:28.640 --> 0:18:32.320
<v Speaker 3>back is just a small share of the effect of

0:18:32.359 --> 0:18:35.159
<v Speaker 3>your anti fraud efforts. What you should really care about

0:18:35.359 --> 0:18:38.640
<v Speaker 3>is your deterrence effect. And I've shown in research deterrence

0:18:38.640 --> 0:18:41.920
<v Speaker 3>effects are in many cases like ten times larger than

0:18:41.960 --> 0:18:44.040
<v Speaker 3>these recovery dollars. So you go to the Partner of

0:18:44.080 --> 0:18:45.960
<v Speaker 3>Justice and you talk to their healthcare fraud people. They

0:18:45.960 --> 0:18:47.919
<v Speaker 3>write a report to Congress every year. It's called the

0:18:47.920 --> 0:18:51.320
<v Speaker 3>Healthcare Fraud and Abuse Report, and they put a number

0:18:51.400 --> 0:18:54.120
<v Speaker 3>there for return on investment, and Congress asks them, tell

0:18:54.200 --> 0:18:56.479
<v Speaker 3>us how much money did you spend and how what

0:18:56.600 --> 0:18:58.760
<v Speaker 3>was your return on investment? And they say the number

0:18:58.800 --> 0:19:02.160
<v Speaker 3>is four, And okay, first of all, four ex return

0:19:02.200 --> 0:19:04.879
<v Speaker 3>on investment already very good. That immediately means that we

0:19:04.880 --> 0:19:07.119
<v Speaker 3>should spend more resources there. But I think that's actually

0:19:07.119 --> 0:19:09.200
<v Speaker 3>the wrong number. I think the number is forty because

0:19:09.200 --> 0:19:12.359
<v Speaker 3>the four is only counting money that they're getting written

0:19:12.400 --> 0:19:14.840
<v Speaker 3>to them in terms of checks back. But if you

0:19:14.880 --> 0:19:16.760
<v Speaker 3>count to terrence, and you have to count to terrence,

0:19:17.160 --> 0:19:19.920
<v Speaker 3>the value of these anti fraud efforts is huge. So

0:19:20.000 --> 0:19:22.480
<v Speaker 3>do we reward people in terms of the value. They

0:19:22.520 --> 0:19:26.320
<v Speaker 3>bring this to Tracy's question in some sense, yes, right, Look,

0:19:26.440 --> 0:19:28.919
<v Speaker 3>there's a press release they trot out the attorney general,

0:19:29.280 --> 0:19:31.679
<v Speaker 3>maybe the civil servant gets exact and.

0:19:31.760 --> 0:19:33.600
<v Speaker 1>A minimum I could go to my boss and be like,

0:19:33.640 --> 0:19:35.600
<v Speaker 1>I saved US twenty million dollars.

0:19:35.440 --> 0:19:38.199
<v Speaker 3>But largely no, we don't pay these people. Well, we

0:19:38.200 --> 0:19:40.760
<v Speaker 3>don't retain them. Well, if you look at your average

0:19:40.800 --> 0:19:43.639
<v Speaker 3>assistant US attorney, they go to the government for a

0:19:43.640 --> 0:19:46.320
<v Speaker 3>few years, do it fantastic work, and then realize that

0:19:46.359 --> 0:19:47.880
<v Speaker 3>private industry pays three times as much.

0:19:47.880 --> 0:20:07.159
<v Speaker 2>In the league, when we were talking about the original

0:20:07.280 --> 0:20:10.360
<v Speaker 2>dialysis ambulance fraud, it sounded like there was a very

0:20:10.359 --> 0:20:14.080
<v Speaker 2>elegant solution, just get a doctor sign off. When you

0:20:14.119 --> 0:20:16.760
<v Speaker 2>look at some of these other emerging frauds, and you

0:20:16.800 --> 0:20:21.280
<v Speaker 2>mentioned wound care for example, or you mentioned someone gets

0:20:21.320 --> 0:20:24.240
<v Speaker 2>a really nice scooter that they didn't really need, or

0:20:24.280 --> 0:20:27.959
<v Speaker 2>something like that, because and you mentioned that the requirement

0:20:28.119 --> 0:20:31.440
<v Speaker 2>of putting up twenty percent copay is not that effective

0:20:31.480 --> 0:20:33.520
<v Speaker 2>because the seller is just like, you know what, We'll

0:20:33.560 --> 0:20:35.480
<v Speaker 2>take the hit and only take the eighty percent. Is

0:20:35.560 --> 0:20:39.359
<v Speaker 2>no big deal. How much of the challenge is on

0:20:39.440 --> 0:20:41.840
<v Speaker 2>the data and identification side, which we should talk a

0:20:41.840 --> 0:20:46.119
<v Speaker 2>little bit more, versus the versus the mechanism which you

0:20:46.200 --> 0:20:47.280
<v Speaker 2>use to crack down on it.

0:20:47.359 --> 0:20:50.119
<v Speaker 3>So I'm actually really in favor of this program. We

0:20:50.280 --> 0:20:53.240
<v Speaker 3>use the big whistleblower program called the False Claims Act. Okay,

0:20:53.240 --> 0:20:55.080
<v Speaker 3>and I've written extensively. Do you guys know how this works?

0:20:55.280 --> 0:20:55.600
<v Speaker 2>No?

0:20:55.600 --> 0:20:59.200
<v Speaker 3>No, it's the weirdest, coolest thing. I mean for an economist. Okay,

0:20:59.480 --> 0:21:03.720
<v Speaker 3>So there is a private market for anti fraud in

0:21:03.800 --> 0:21:07.240
<v Speaker 3>the US. If you know about a company or person

0:21:07.320 --> 0:21:10.160
<v Speaker 3>that is defrauding any public program, not specific to healthcare,

0:21:10.600 --> 0:21:13.240
<v Speaker 3>you can hire your own attorney and there are firms

0:21:13.280 --> 0:21:16.199
<v Speaker 3>that specialize in this. You can sue that person in

0:21:16.320 --> 0:21:19.879
<v Speaker 3>federal civil court, and the whistleblower gets a share of

0:21:19.880 --> 0:21:21.840
<v Speaker 3>the money they bring back to the government. And this

0:21:22.000 --> 0:21:25.159
<v Speaker 3>is a super effective program because it means that every nurse,

0:21:25.480 --> 0:21:29.760
<v Speaker 3>every billing agent, every doctor in a hospital, if that organization.

0:21:29.960 --> 0:21:31.880
<v Speaker 2>This is what we're going to get into. Tracy, I'm

0:21:31.920 --> 0:21:34.520
<v Speaker 2>not gonna do fraud because even though I want to

0:21:34.560 --> 0:21:36.520
<v Speaker 2>know how it works, but I do want to make that.

0:21:36.680 --> 0:21:38.440
<v Speaker 1>I'm going to become a fraud bounty hunter.

0:21:40.680 --> 0:21:43.240
<v Speaker 3>It's bounty hunting. But bounty hunting is great because of

0:21:43.240 --> 0:21:45.880
<v Speaker 3>two reasons. The first is there's this private information component.

0:21:46.200 --> 0:21:49.399
<v Speaker 3>Rather than waiting for some analyst in Washington to figure

0:21:49.440 --> 0:21:53.760
<v Speaker 3>out today's fraud, just make a huge reward available for

0:21:53.800 --> 0:21:57.440
<v Speaker 3>the individuals who know about it, because there's lots of information.

0:21:57.520 --> 0:22:00.199
<v Speaker 3>Right from an economist perspective, the problem here is the

0:22:00.200 --> 0:22:03.000
<v Speaker 3>problem we have in a lot of healthcare. It's information asymmetry.

0:22:03.320 --> 0:22:05.600
<v Speaker 3>The doctors and the hospitals and the nursing homes know

0:22:05.680 --> 0:22:08.320
<v Speaker 3>so much more about the patient than the insurance company.

0:22:08.359 --> 0:22:11.679
<v Speaker 3>And here the insurance company is the government. And so

0:22:12.240 --> 0:22:14.600
<v Speaker 3>rather than trying to make just top down solutions, and

0:22:14.640 --> 0:22:16.520
<v Speaker 3>there are some top down solutions, don't get me wrong,

0:22:16.840 --> 0:22:21.960
<v Speaker 3>it's really important that we also allow the individuals who

0:22:22.000 --> 0:22:25.600
<v Speaker 3>have the information, the valuable information, to be rewarded for that.

0:22:25.720 --> 0:22:29.280
<v Speaker 3>So the whistleblower program private information. There's also the private

0:22:29.560 --> 0:22:32.480
<v Speaker 3>cause of action, the idea that every person can become

0:22:32.800 --> 0:22:35.760
<v Speaker 3>Literally the legal term sometimes use is private attorney general. Right,

0:22:36.000 --> 0:22:38.159
<v Speaker 3>you can go and hire a lawyer and sue, and

0:22:38.200 --> 0:22:41.280
<v Speaker 3>that lawsuit is on behalf of the United States of America.

0:22:41.359 --> 0:22:45.000
<v Speaker 3>And this has been an extremely effective program historically.

0:22:45.080 --> 0:22:45.760
<v Speaker 2>Say more about that.

0:22:45.880 --> 0:22:47.960
<v Speaker 3>Yeah, So I ran a Freedom of Information Act request

0:22:48.000 --> 0:22:50.480
<v Speaker 3>on the Department of Justice for data on every whistleblower

0:22:50.560 --> 0:22:53.400
<v Speaker 3>lawsuit from nineteen eighty seven through about twenty seventeen. When

0:22:53.400 --> 0:22:56.280
<v Speaker 3>I first filed this. There are thousands of these cases.

0:22:56.440 --> 0:22:59.359
<v Speaker 3>They've brought in billions of dollars for the government. Fifty

0:22:59.400 --> 0:23:01.879
<v Speaker 3>five percent of them are in healthcare, but they're also

0:23:02.040 --> 0:23:04.639
<v Speaker 3>used all over the government. We see cases related to

0:23:04.720 --> 0:23:07.600
<v Speaker 3>the Transportation Department, the Department of Education, the Department of Defense,

0:23:07.920 --> 0:23:10.919
<v Speaker 3>and the ideas the same. Instead of trying to have

0:23:11.000 --> 0:23:13.600
<v Speaker 3>the government figure out how to run anti fraud, anti

0:23:13.600 --> 0:23:15.880
<v Speaker 3>fraud can pay for itself. There are lots of people

0:23:15.920 --> 0:23:18.480
<v Speaker 3>who would love to earn a million dollars being a whistleblower,

0:23:18.640 --> 0:23:20.240
<v Speaker 3>and these whistleblowers get paid pretty well.

0:23:20.800 --> 0:23:23.879
<v Speaker 1>Since you mentioned transportation and education, just then, can you

0:23:23.920 --> 0:23:27.240
<v Speaker 1>talk about some examples of fraud outside of the medical sector.

0:23:27.480 --> 0:23:31.000
<v Speaker 3>Absolutely. So. I have a recent paper about the unemployment

0:23:31.080 --> 0:23:34.000
<v Speaker 3>insurance market during COVID, so I'd be happy to talk

0:23:34.040 --> 0:23:34.399
<v Speaker 3>about that.

0:23:34.480 --> 0:23:35.040
<v Speaker 1>Oh, that's great.

0:23:35.080 --> 0:23:37.760
<v Speaker 3>So during COVID we had the biggest expansion of unemployment

0:23:37.840 --> 0:23:39.760
<v Speaker 3>insurance in history. I think you guys know about this.

0:23:40.040 --> 0:23:42.280
<v Speaker 3>You've talked a little bit about PPP, and there's some

0:23:42.320 --> 0:23:46.280
<v Speaker 3>great research there on fraud. PPP was for companies. Unemployment

0:23:46.280 --> 0:23:48.560
<v Speaker 3>insurance was for individuals who lost their job, and we

0:23:48.680 --> 0:23:51.760
<v Speaker 3>expanded it to also include gig economy workers through a

0:23:51.800 --> 0:23:54.439
<v Speaker 3>program called PUA. You guys probably know about this.

0:23:54.960 --> 0:23:57.520
<v Speaker 2>So this is starting to feel like history imade so

0:23:57.560 --> 0:24:00.240
<v Speaker 2>crazy because this is like yesterday, but also it's it

0:24:00.240 --> 0:24:02.639
<v Speaker 2>feels so long. Yeah, the time distortion is real.

0:24:03.680 --> 0:24:07.399
<v Speaker 3>So unemployment insurance rose heavily during the pandemic, and with

0:24:07.480 --> 0:24:09.520
<v Speaker 3>it came a lot of fraud. Now why is there

0:24:09.520 --> 0:24:12.040
<v Speaker 3>fraud in the unemployment insurance sector. Well, the government's cutting checks,

0:24:12.240 --> 0:24:14.399
<v Speaker 3>and the government's cutting checks really fast. So you might

0:24:14.440 --> 0:24:16.200
<v Speaker 3>remember at the beginning of the pandemic, there was this

0:24:16.400 --> 0:24:19.960
<v Speaker 3>immediate recession, and there was fear of the big macroeconomic

0:24:20.000 --> 0:24:22.359
<v Speaker 3>consequences of everybody being out of a job on many people,

0:24:22.400 --> 0:24:24.199
<v Speaker 3>I should say being out of a job, and so

0:24:24.280 --> 0:24:28.440
<v Speaker 3>the government really loosened and expanded its use of unemployment insurance.

0:24:28.600 --> 0:24:30.320
<v Speaker 3>But whenever the government says, hey, we're going to write

0:24:30.320 --> 0:24:33.160
<v Speaker 3>eight hundred billion dollars in checks, people get creative about

0:24:33.160 --> 0:24:35.520
<v Speaker 3>ways to builk the government. And in particular, this type

0:24:35.520 --> 0:24:39.640
<v Speaker 3>of fraud was really identity theft. It is super easy,

0:24:40.040 --> 0:24:41.960
<v Speaker 3>super easy to go on the dark web and buy

0:24:42.000 --> 0:24:44.080
<v Speaker 3>a social Security number, and so that's what a lot

0:24:44.119 --> 0:24:48.240
<v Speaker 3>of criminals and organized criminal groups did during the pandemic,

0:24:48.680 --> 0:24:55.400
<v Speaker 3>there was a widespread fraud where individuals would go purchase identities,

0:24:55.920 --> 0:24:59.960
<v Speaker 3>apply en mass to state unemployment insurance programs, collect them,

0:25:00.760 --> 0:25:03.040
<v Speaker 3>take it out of the system, and then we think

0:25:03.080 --> 0:25:05.960
<v Speaker 3>in many cases it was either offshore or rerouted to

0:25:06.000 --> 0:25:10.240
<v Speaker 3>criminal organizations. So this was so incredibly wides But actually

0:25:10.440 --> 0:25:12.680
<v Speaker 3>my wife got a prepaid debit card in the mail

0:25:12.960 --> 0:25:15.240
<v Speaker 3>from the unemployment agency and she didn't lose her job.

0:25:15.680 --> 0:25:18.080
<v Speaker 3>I've talked to just dozens of people across the country

0:25:18.080 --> 0:25:19.600
<v Speaker 3>and in all sorts of sectors, who said, yeah, this

0:25:19.640 --> 0:25:20.359
<v Speaker 3>actually happened to me.

0:25:20.760 --> 0:25:23.400
<v Speaker 1>Wait, this leads to something that I want to ask

0:25:23.440 --> 0:25:27.320
<v Speaker 1>as well, which is how is fraud propagated? Because this

0:25:27.400 --> 0:25:29.960
<v Speaker 1>kind of gets to Joe's question earlier, how do people

0:25:30.000 --> 0:25:32.439
<v Speaker 1>actually do this? How do I learn to do fraud?

0:25:32.640 --> 0:25:34.640
<v Speaker 1>Is it like I just look it up on the internet,

0:25:34.760 --> 0:25:37.960
<v Speaker 1>or is it like someone I'm associated with tells me.

0:25:38.680 --> 0:25:42.320
<v Speaker 3>So different frauds have different mechanisms by which people learn them,

0:25:42.320 --> 0:25:45.000
<v Speaker 3>but in general, there is a social learning component to this. Absolutely.

0:25:45.280 --> 0:25:47.840
<v Speaker 3>So with some of the institutional frauds that we've been

0:25:47.880 --> 0:25:50.200
<v Speaker 3>talking about, a hospital that decides that they're going to

0:25:50.240 --> 0:25:52.919
<v Speaker 3>suddenly charge a lot of money through some loophole. Often

0:25:52.960 --> 0:25:56.000
<v Speaker 3>there are business decisions being made by executives. Sometimes there

0:25:56.000 --> 0:25:59.480
<v Speaker 3>are consultants involved, and big national chains often are the

0:25:59.480 --> 0:26:02.280
<v Speaker 3>ones that's read these because they have kind of centralized management.

0:26:02.520 --> 0:26:05.480
<v Speaker 3>The hospital administrators from different hospitals look at this. I'm thinking,

0:26:05.520 --> 0:26:08.840
<v Speaker 3>for example, of the tenant hospitals which paid nine hundred

0:26:08.880 --> 0:26:11.359
<v Speaker 3>million dollars back to the government for a small little

0:26:11.400 --> 0:26:13.960
<v Speaker 3>loophole that was supposed to be for an outlier payments program,

0:26:14.119 --> 0:26:15.840
<v Speaker 3>and they just drove a truck through that loophole they

0:26:16.080 --> 0:26:19.480
<v Speaker 3>ended up stealing. So if you go in patient in

0:26:19.480 --> 0:26:21.840
<v Speaker 3>a hospital, the hospital gets paid fixed themount under Medicare

0:26:21.920 --> 0:26:24.480
<v Speaker 3>through what's called a prospective payment system. They don't pay

0:26:24.520 --> 0:26:27.160
<v Speaker 3>per cost. They just say, you know you have a pneumonia,

0:26:27.240 --> 0:26:29.680
<v Speaker 3>we're going to pay this much. The government was worried

0:26:29.680 --> 0:26:31.920
<v Speaker 3>when they set this program up that some very expensive

0:26:31.920 --> 0:26:35.040
<v Speaker 3>patients wouldn't get treatment because if the hospital knows that

0:26:35.160 --> 0:26:36.760
<v Speaker 3>and they know that, they're going to lose money on you,

0:26:37.160 --> 0:26:39.560
<v Speaker 3>so they made this asterisk. A lot of these things

0:26:39.560 --> 0:26:42.920
<v Speaker 3>are asterisks to have an outlier payment system where if

0:26:42.920 --> 0:26:46.120
<v Speaker 3>a patient is super expensive, then they pay extra and

0:26:46.280 --> 0:26:49.280
<v Speaker 3>the tenant hospitals figured out how to make every patient

0:26:49.280 --> 0:26:52.560
<v Speaker 3>look super expensive by manipulating some of their balance sheets,

0:26:52.640 --> 0:26:55.639
<v Speaker 3>and they ended up spending nine hundred million dollars to

0:26:56.440 --> 0:26:58.200
<v Speaker 3>excuse me, they end up to settle claims, so that

0:26:58.320 --> 0:27:00.600
<v Speaker 3>Tenant never admitted fault, I should say, but the government

0:27:00.640 --> 0:27:03.679
<v Speaker 3>received nine hundred million dollars back from Tenant because you know,

0:27:03.720 --> 0:27:06.480
<v Speaker 3>these allegations were I think true that that Tenant had

0:27:06.520 --> 0:27:08.560
<v Speaker 3>done this, and I estimate actually that the government lost

0:27:08.600 --> 0:27:10.280
<v Speaker 3>billions of dollars to that. Okay, So but I want

0:27:10.280 --> 0:27:11.480
<v Speaker 3>to go back to this question, So how did that

0:27:11.520 --> 0:27:13.639
<v Speaker 3>one happen? Well, there was a consultancy in New Jersey

0:27:13.680 --> 0:27:15.480
<v Speaker 3>that was going around telling people, hey, do you know

0:27:15.520 --> 0:27:17.840
<v Speaker 3>about this outlier payment system? And that's how we think

0:27:17.880 --> 0:27:19.760
<v Speaker 3>that fraud spread, and it's spread all over the country.

0:27:20.160 --> 0:27:24.240
<v Speaker 3>So there's some of this corporate learning from other companies,

0:27:24.280 --> 0:27:26.639
<v Speaker 3>and there's also a lot of social learning. So in

0:27:26.680 --> 0:27:30.320
<v Speaker 3>the case of unemployment insurance fraud or PPP fraud, you

0:27:30.320 --> 0:27:34.440
<v Speaker 3>can go and you can find Telegram groups and Facebook

0:27:34.440 --> 0:27:36.960
<v Speaker 3>groups of people that are like, here's how you apply

0:27:37.000 --> 0:27:39.560
<v Speaker 3>for a PPP loan. There's a great new paper by

0:27:39.640 --> 0:27:41.960
<v Speaker 3>John Griffin at the University of Texas on these Facebook

0:27:42.000 --> 0:27:45.399
<v Speaker 3>groups and like they're called like fraud kings, like they

0:27:45.400 --> 0:27:47.440
<v Speaker 3>know what there were maybe PPP loan kings. There's something

0:27:47.440 --> 0:27:49.800
<v Speaker 3>that it's like really obvious what they're doing and everyone knows.

0:27:50.000 --> 0:27:52.080
<v Speaker 3>And so in the case of health care fraud, in

0:27:52.119 --> 0:27:54.640
<v Speaker 3>the case of non healthcare fraud, often it's you're surrounded

0:27:54.640 --> 0:27:56.520
<v Speaker 3>by people who know how to do this, or you

0:27:56.560 --> 0:27:59.199
<v Speaker 3>meet them through digital platforms, and then people learn and

0:27:59.240 --> 0:28:02.360
<v Speaker 3>so a lot of the fraud we see propagates through communities.

0:28:03.000 --> 0:28:05.399
<v Speaker 3>In the case of the ambulance market, we saw that

0:28:05.440 --> 0:28:08.679
<v Speaker 3>there were certain Eastern European groups that were responsible for

0:28:08.720 --> 0:28:11.080
<v Speaker 3>this in different parts of the country and often from

0:28:11.119 --> 0:28:14.560
<v Speaker 3>the same from original areas, and so you know, generally

0:28:14.600 --> 0:28:16.639
<v Speaker 3>the understanding, at least from the government is that there

0:28:16.720 --> 0:28:18.400
<v Speaker 3>must have been some social learning going out there.

0:28:18.400 --> 0:28:20.479
<v Speaker 2>It's very hard to prove, of course, right, someone figures

0:28:20.520 --> 0:28:22.920
<v Speaker 2>it out and then tell family that's right, that makes sense.

0:28:23.160 --> 0:28:26.280
<v Speaker 2>Let's talk about detection via data and more, and you

0:28:26.320 --> 0:28:28.000
<v Speaker 2>talked about how you have access to all of this

0:28:28.240 --> 0:28:33.200
<v Speaker 2>data and so forth. Obviously you can describe fraud qualitatively

0:28:33.480 --> 0:28:37.080
<v Speaker 2>by saying this is how an ambulance company cheats the government,

0:28:37.119 --> 0:28:40.479
<v Speaker 2>et cetera. What do the fingerprints of fraud look like

0:28:40.520 --> 0:28:42.640
<v Speaker 2>when you look at it on the macro scale, What

0:28:43.200 --> 0:28:46.120
<v Speaker 2>pops up in the data that would at least be

0:28:46.200 --> 0:28:48.280
<v Speaker 2>a yellow flag and say this is something we need

0:28:48.280 --> 0:28:48.880
<v Speaker 2>to look at more.

0:28:49.080 --> 0:28:52.920
<v Speaker 3>So there are huge run ups in spending in every

0:28:52.960 --> 0:28:54.680
<v Speaker 3>type of fraud I've ever seen, and every type of

0:28:54.840 --> 0:28:56.480
<v Speaker 3>I mean, the whole point is if you're not making money,

0:28:56.480 --> 0:28:57.960
<v Speaker 3>it's not a good fraud, right, And so if you

0:28:58.040 --> 0:29:02.000
<v Speaker 3>just make a plot of bending against you know, time,

0:29:02.240 --> 0:29:04.920
<v Speaker 3>you can often just see these really big exponential growth. Now,

0:29:04.960 --> 0:29:06.920
<v Speaker 3>some of those are legitimate, because if there's a new

0:29:07.040 --> 0:29:10.520
<v Speaker 3>great medical technology and people start using it, that also

0:29:10.640 --> 0:29:13.560
<v Speaker 3>looks like a technological adoption curve, right of a kind

0:29:13.560 --> 0:29:17.120
<v Speaker 3>of big upper rank. But when it's fraud, it you know,

0:29:17.240 --> 0:29:20.800
<v Speaker 3>first of all, it looks like massive year over year increases.

0:29:21.360 --> 0:29:24.400
<v Speaker 3>The second is that it'll often be way too much,

0:29:25.200 --> 0:29:27.680
<v Speaker 3>to the point where it's obvious that nobody's getting this.

0:29:27.760 --> 0:29:30.720
<v Speaker 3>For example, sometimes you'll see a doctor who's just billing

0:29:30.720 --> 0:29:33.479
<v Speaker 3>from too many home care visits, but like the homecare

0:29:33.560 --> 0:29:35.560
<v Speaker 3>visits are sixty minutes, and the doctor's billing for five

0:29:35.560 --> 0:29:37.920
<v Speaker 3>thousand of them a year. It's like, well, that's five

0:29:37.960 --> 0:29:39.920
<v Speaker 3>thousand hours. There aren't five thousand work hours in the year.

0:29:39.960 --> 0:29:41.720
<v Speaker 3>The government should just be able to detect that. You

0:29:41.760 --> 0:29:44.800
<v Speaker 3>should just not pay those So where the failure is

0:29:44.800 --> 0:29:46.480
<v Speaker 3>is not in how hard it is to detect in

0:29:46.560 --> 0:29:48.880
<v Speaker 3>data such and not that hard to detect in data.

0:29:48.920 --> 0:29:51.880
<v Speaker 3>It's on the incentives for the enforcers to look at

0:29:51.880 --> 0:29:54.240
<v Speaker 3>that data and use it appropriately. And that's where we

0:29:54.280 --> 0:29:56.160
<v Speaker 3>get back to this limited enforcement capacity.

0:29:56.440 --> 0:29:59.760
<v Speaker 1>How do you measure benefits on the other side, because

0:29:59.760 --> 0:30:02.760
<v Speaker 1>that's it seems again like a potential avenue where there

0:30:02.800 --> 0:30:04.000
<v Speaker 1>could be some disagreement.

0:30:04.960 --> 0:30:07.719
<v Speaker 3>So I think it's super important that we preserve access

0:30:07.720 --> 0:30:11.160
<v Speaker 3>to the public programs. And my research is not focused

0:30:11.160 --> 0:30:12.800
<v Speaker 3>at all on, you know, how do we take things

0:30:12.840 --> 0:30:14.880
<v Speaker 3>away from people? And in particular, I always try to

0:30:14.920 --> 0:30:19.520
<v Speaker 3>measure very hard whether there are health effects associated with this.

0:30:19.640 --> 0:30:21.240
<v Speaker 3>So in the case of the ambulances, we're able to

0:30:21.280 --> 0:30:24.400
<v Speaker 3>show pretty definitively that there are no negative health effets

0:30:24.440 --> 0:30:25.880
<v Speaker 3>associated with cutting this, and this is something I do

0:30:25.960 --> 0:30:28.200
<v Speaker 3>in all my papers. You really have to ask the question,

0:30:28.520 --> 0:30:31.960
<v Speaker 3>were people losing care that they needed? And so super

0:30:31.960 --> 0:30:34.600
<v Speaker 3>great question in the context of some of these very

0:30:34.640 --> 0:30:38.240
<v Speaker 3>obvious frauds. Sometimes people aren't even getting the service. So

0:30:38.280 --> 0:30:40.600
<v Speaker 3>if the government's paying for something and nobody ever got it,

0:30:40.640 --> 0:30:43.560
<v Speaker 3>taking it away is costless. So that's the best efficient

0:30:43.680 --> 0:30:45.760
<v Speaker 3>thing that we could do, is just stop paying for

0:30:45.800 --> 0:30:48.160
<v Speaker 3>things that aren't even happening. Right, Let's not even talk

0:30:48.160 --> 0:30:50.680
<v Speaker 3>about waste. Let's just talk about these outright frauds. And

0:30:50.760 --> 0:30:52.800
<v Speaker 3>so how do you measure it? I mean, if you

0:30:52.840 --> 0:30:55.719
<v Speaker 3>look at very large scale claims data, as I do,

0:30:55.760 --> 0:30:58.480
<v Speaker 3>and the government can, you can see, Okay, we cut

0:30:58.480 --> 0:31:01.600
<v Speaker 3>out this provider, let's look at their patients. Did they

0:31:01.600 --> 0:31:04.480
<v Speaker 3>go to the hospital more? That's an empirical question, right,

0:31:04.480 --> 0:31:06.520
<v Speaker 3>And so there's no reason that it just has to

0:31:06.560 --> 0:31:08.000
<v Speaker 3>be a gas, right. This is something that we should

0:31:08.040 --> 0:31:10.640
<v Speaker 3>be measuring as part of our data analysis associated with

0:31:10.680 --> 0:31:11.160
<v Speaker 3>handy front.

0:31:27.360 --> 0:31:31.400
<v Speaker 2>So it's very obvious why, whether we're talking about the

0:31:31.440 --> 0:31:35.000
<v Speaker 2>government or private insurance, that it's just rules on top

0:31:35.040 --> 0:31:38.400
<v Speaker 2>of rules on top of rules and asterisks and so forth,

0:31:38.520 --> 0:31:44.560
<v Speaker 2>because this is very complicated stuff. Rules also create problems

0:31:45.200 --> 0:31:49.440
<v Speaker 2>and compliance by the rules. Can you know strict adherence

0:31:49.440 --> 0:31:52.640
<v Speaker 2>to the rules can also have negative effects. I have

0:31:52.720 --> 0:31:55.840
<v Speaker 2>to imagine, for example, that there are say, many people

0:31:56.040 --> 0:31:59.920
<v Speaker 2>in the US currently on some sort of GLP one drug,

0:32:00.440 --> 0:32:03.560
<v Speaker 2>but maybe technically they don't have the thing, but you

0:32:03.600 --> 0:32:05.080
<v Speaker 2>know a lot of it. There seem to be a

0:32:05.120 --> 0:32:07.960
<v Speaker 2>lot of benefits from weight loss and so forth. Have

0:32:08.080 --> 0:32:12.600
<v Speaker 2>you found examples in your research in which there is

0:32:12.640 --> 0:32:18.200
<v Speaker 2>some sort of positive externality from deviation from the rules?

0:32:18.240 --> 0:32:20.320
<v Speaker 3>Absolutely? Absolutely, Thanks for cueuing this up. I have a

0:32:20.360 --> 0:32:23.960
<v Speaker 3>great story. So let's talk about the hospice industry. Hospice

0:32:23.960 --> 0:32:26.920
<v Speaker 3>care is an end of life benefit for patients who

0:32:26.960 --> 0:32:29.080
<v Speaker 3>have a prognosis of six months or less. So if

0:32:29.080 --> 0:32:31.240
<v Speaker 3>you're dying and a physician certifies that you're dying within

0:32:31.280 --> 0:32:34.320
<v Speaker 3>six months, you qualify for hospice. What is hospice? You

0:32:34.360 --> 0:32:36.600
<v Speaker 3>give up the curative care, and you stop taking all

0:32:36.600 --> 0:32:38.480
<v Speaker 3>of these meds with the horrible side effects, and you

0:32:38.480 --> 0:32:40.200
<v Speaker 3>stop going to the hospital as often, and you can

0:32:40.200 --> 0:32:43.239
<v Speaker 3>die peacefully at home with pain medication. And I think

0:32:43.280 --> 0:32:45.760
<v Speaker 3>that this is a great program. I think it's really important.

0:32:45.840 --> 0:32:47.920
<v Speaker 3>We spend twenty billion dollars a year on the federal

0:32:47.960 --> 0:32:52.680
<v Speaker 3>hospice program, and more than fifty percent of Medicare patients

0:32:52.720 --> 0:32:54.880
<v Speaker 3>who die every year will have had a hospice claim, Okay,

0:32:54.920 --> 0:32:56.920
<v Speaker 3>this is important. This is the way that we're treating

0:32:56.920 --> 0:32:59.840
<v Speaker 3>people at the end of life. It's really hard to

0:32:59.880 --> 0:33:03.280
<v Speaker 3>know who's dying within six months. That is not a

0:33:03.320 --> 0:33:10.040
<v Speaker 3>trivial estimate, and historically there was subjectivity in this. So

0:33:10.600 --> 0:33:13.920
<v Speaker 3>over the twenty year period nineteen ninety nine through twenty nineteen,

0:33:14.200 --> 0:33:18.360
<v Speaker 3>there was a quadrupling of for profit hospices, and many

0:33:18.400 --> 0:33:22.560
<v Speaker 3>of them increasingly took Alzheimer's and dementia patients. Why because

0:33:22.560 --> 0:33:25.760
<v Speaker 3>they stay for a long time on these hospice programs,

0:33:26.080 --> 0:33:29.240
<v Speaker 3>and the hospice programs are paid about two hundred dollars

0:33:29.240 --> 0:33:33.000
<v Speaker 3>a day. And the federal government cried fraud, and we

0:33:33.040 --> 0:33:37.160
<v Speaker 3>saw one hundred and sixty three federal whistleblower lawsuits against

0:33:37.160 --> 0:33:40.480
<v Speaker 3>for profit hospice companies more than three hundred million dollars

0:33:40.480 --> 0:33:42.920
<v Speaker 3>in settlements, saying this is fraud. You shouldn't have taken

0:33:42.920 --> 0:33:45.000
<v Speaker 3>the patients. You should have known that they weren't dying

0:33:45.000 --> 0:33:47.840
<v Speaker 3>fast enough that they did not qualify for hospice. So

0:33:47.920 --> 0:33:50.280
<v Speaker 3>I wrote a paper with John Gruber at MIT, as

0:33:50.280 --> 0:33:52.120
<v Speaker 3>well as one of our PhD students and David Howard

0:33:52.160 --> 0:33:55.520
<v Speaker 3>at Emery, some really top notch economists and we looked

0:33:55.560 --> 0:33:57.680
<v Speaker 3>at this program. We said, Okay, this is something people

0:33:57.680 --> 0:34:00.320
<v Speaker 3>are really concerned about. And what we found really knocked

0:34:00.360 --> 0:34:03.520
<v Speaker 3>our socks off. The fraud actually was not as bad

0:34:03.520 --> 0:34:06.600
<v Speaker 3>as people said, in particular because did some patients go

0:34:06.640 --> 0:34:09.200
<v Speaker 3>to hospice who may otherwise not have because they were

0:34:09.200 --> 0:34:11.560
<v Speaker 3>not dying fast enough. Sure, but it's still a heck

0:34:11.600 --> 0:34:14.160
<v Speaker 3>of a lot cheaper to go to hospice for six

0:34:14.200 --> 0:34:15.840
<v Speaker 3>months than it is to go to the hospital and

0:34:15.880 --> 0:34:17.759
<v Speaker 3>the nursing home and the homecare agency and the Durbal

0:34:17.760 --> 0:34:20.560
<v Speaker 3>medical equipment and the pharmaceuticals. And we estimate that these

0:34:20.600 --> 0:34:24.760
<v Speaker 3>patients saved tens of thousands of dollars. And the patients

0:34:24.880 --> 0:34:27.200
<v Speaker 3>liked it. They and their families are picking hospice. This

0:34:27.239 --> 0:34:29.520
<v Speaker 3>is something that they want. They don't want the hospital,

0:34:29.560 --> 0:34:31.720
<v Speaker 3>so they're getting a service they want and the government

0:34:31.760 --> 0:34:33.640
<v Speaker 3>is saving money. And yet we've decided that this is

0:34:33.680 --> 0:34:35.960
<v Speaker 3>a fraud because there's this rule. Now, when you think

0:34:36.000 --> 0:34:38.400
<v Speaker 3>about it from that way, six months is totally arbitrary.

0:34:38.400 --> 0:34:40.480
<v Speaker 3>Why is it six months and not eight months? And

0:34:40.520 --> 0:34:43.000
<v Speaker 3>so this paper, it's called Dying or Lying, It just

0:34:43.280 --> 0:34:46.400
<v Speaker 3>was accepted last week at the AER. It shows pretty

0:34:46.400 --> 0:34:50.400
<v Speaker 3>conclusively that the policies aimed at limiting fraud threw the

0:34:50.400 --> 0:34:51.240
<v Speaker 3>baby out with the bathloar.

0:34:52.239 --> 0:34:55.960
<v Speaker 1>You know, we started this conversation mentioning DOJE the new

0:34:56.120 --> 0:35:00.560
<v Speaker 1>Department of Government Efficiency. I guess one obvious question to

0:35:00.600 --> 0:35:02.719
<v Speaker 1>ask you would be, you know, if you were in

0:35:02.920 --> 0:35:07.000
<v Speaker 1>Elon or Vivek's shoes, what would be your you know,

0:35:07.080 --> 0:35:09.560
<v Speaker 1>the first thing you would start with when it comes

0:35:09.600 --> 0:35:11.880
<v Speaker 1>to rooting out fraud or I don't know if you

0:35:11.880 --> 0:35:16.359
<v Speaker 1>want to get into wider types of inefficiencies, but what's

0:35:16.400 --> 0:35:17.200
<v Speaker 1>the first thing you would do?

0:35:17.320 --> 0:35:20.400
<v Speaker 2>Yeah, they make you the real point person on this.

0:35:20.440 --> 0:35:21.160
<v Speaker 2>How do you start?

0:35:21.560 --> 0:35:25.400
<v Speaker 3>So first, I'm optimistic about the Department of Government Efficiency

0:35:25.400 --> 0:35:28.120
<v Speaker 3>because I think that there are some clear evidence based,

0:35:28.760 --> 0:35:31.480
<v Speaker 3>obvious wins that we have left on the table in

0:35:31.560 --> 0:35:35.000
<v Speaker 3>terms of saving government money. The first is, we know

0:35:35.320 --> 0:35:39.040
<v Speaker 3>that anti fraud efforts, particularly in the Medicare and Medicaid programs,

0:35:39.080 --> 0:35:41.920
<v Speaker 3>have huge return on investments, and we underfund them. So

0:35:41.960 --> 0:35:45.120
<v Speaker 3>we could staff up those offices at the Department of Justice,

0:35:45.160 --> 0:35:46.920
<v Speaker 3>at the Department of Justice Districts, at the Office of

0:35:46.920 --> 0:35:49.760
<v Speaker 3>the Inspector General and literally just chase down the leads

0:35:49.760 --> 0:35:51.120
<v Speaker 3>that we already know about we don't even have to

0:35:51.160 --> 0:35:53.240
<v Speaker 3>go after other leads. If you look at your average

0:35:53.520 --> 0:35:56.200
<v Speaker 3>civil assistant US attorney, they've got thirty good cases on

0:35:56.239 --> 0:35:59.239
<v Speaker 3>their desk and they get to pick two of them sore.

0:35:59.480 --> 0:36:01.960
<v Speaker 3>And many of these cases allege millions of dollars of

0:36:01.960 --> 0:36:03.840
<v Speaker 3>fraud and they say, I'm sorry, we have a number.

0:36:03.960 --> 0:36:06.080
<v Speaker 3>We can't go under anything right now less than ten million,

0:36:06.120 --> 0:36:08.600
<v Speaker 3>and they drop the case. So even if we just

0:36:08.800 --> 0:36:11.160
<v Speaker 3>doubled the number of people or at the Department of

0:36:11.200 --> 0:36:13.919
<v Speaker 3>Justice who focus on fraud against the government, that would

0:36:13.920 --> 0:36:16.600
<v Speaker 3>pay for itself many times over. Okay, So that's an

0:36:16.600 --> 0:36:19.520
<v Speaker 3>obvious one. The second is to start getting serious about data.

0:36:20.040 --> 0:36:23.960
<v Speaker 3>If we do not hire and recruit excellent analysts to

0:36:24.000 --> 0:36:26.279
<v Speaker 3>look at government data from the government, we are going

0:36:26.280 --> 0:36:29.879
<v Speaker 3>to miss obvious frauds. Everything I've talked about so far

0:36:30.200 --> 0:36:33.200
<v Speaker 3>is not rocket science. We're talking about huge amounts of

0:36:33.200 --> 0:36:34.960
<v Speaker 3>billing for patients who obviously don't need it in some

0:36:35.000 --> 0:36:37.000
<v Speaker 3>of these cases, and the government pays it and they

0:36:37.040 --> 0:36:39.480
<v Speaker 3>miss it. They have access to that data in real time,

0:36:39.480 --> 0:36:42.040
<v Speaker 3>they're writ in the checks. Why are we not screening that?

0:36:42.080 --> 0:36:44.360
<v Speaker 3>And the answer is not a lot of data analysts

0:36:44.400 --> 0:36:46.359
<v Speaker 3>work at the Department of Justice, and there are some.

0:36:46.680 --> 0:36:48.000
<v Speaker 3>There are some that work at the Office and the

0:36:48.040 --> 0:36:51.000
<v Speaker 3>Inspector General of Health and Human Services, but not that many.

0:36:51.160 --> 0:36:53.480
<v Speaker 3>Why because they pay like seventy thousand dollars a year.

0:36:53.600 --> 0:36:55.120
<v Speaker 3>And if you're a good data analyst, you don't go

0:36:55.160 --> 0:36:57.120
<v Speaker 3>to work for the government for seventy thousand dollars a year.

0:36:57.239 --> 0:36:59.239
<v Speaker 3>If the government wants top talent, it's got to be

0:36:59.280 --> 0:37:02.680
<v Speaker 3>willing to recruit, and those are competitive positions. And so

0:37:02.920 --> 0:37:04.879
<v Speaker 3>if we get serious about data, we get serious about

0:37:04.880 --> 0:37:08.880
<v Speaker 3>machine learning, we get serious about investing in the attorneys

0:37:08.920 --> 0:37:11.239
<v Speaker 3>who are doing the good work, and just the programs

0:37:11.239 --> 0:37:15.120
<v Speaker 3>work really well. The second is rigorous evaluation, right, we

0:37:15.200 --> 0:37:18.080
<v Speaker 3>need to know when we try policies, do they work

0:37:18.200 --> 0:37:19.880
<v Speaker 3>or do they not work. When we put in a

0:37:19.880 --> 0:37:23.440
<v Speaker 3>prior authorization program, when we put in a new screening

0:37:23.480 --> 0:37:25.839
<v Speaker 3>for people to take a selfie on their phone before

0:37:25.880 --> 0:37:28.120
<v Speaker 3>we pay their unemployment insurance claim, that's actually how we

0:37:28.120 --> 0:37:30.960
<v Speaker 3>fix the unemployment insurance problem. It was an identity theft problem.

0:37:31.000 --> 0:37:32.480
<v Speaker 3>You just got to take a selfie on your phone.

0:37:32.640 --> 0:37:36.080
<v Speaker 3>These ideas work, but we need to evaluate them as

0:37:36.160 --> 0:37:37.360
<v Speaker 3>serious policy.

0:37:36.960 --> 0:37:40.120
<v Speaker 2>As I just have one more question, and I don't

0:37:40.120 --> 0:37:44.239
<v Speaker 2>even know whether this is capable of being ascertained in

0:37:44.280 --> 0:37:47.200
<v Speaker 2>a substantive way. But when you think about the stuff

0:37:47.239 --> 0:37:49.799
<v Speaker 2>that you research and we're talking about, like, you know,

0:37:49.920 --> 0:37:53.920
<v Speaker 2>big question is can we meaningfully move the dial on

0:37:54.719 --> 0:37:59.040
<v Speaker 2>fraudulent spending or maybe wasteful spending? And do we have

0:37:59.120 --> 0:38:02.359
<v Speaker 2>a number it exists? Do we know how much could

0:38:02.400 --> 0:38:04.920
<v Speaker 2>potentially be saved? So?

0:38:05.040 --> 0:38:07.960
<v Speaker 3>I think healthcare fraud alone in the United States is

0:38:08.000 --> 0:38:11.480
<v Speaker 3>something like one hundred billion dollars a year. Now that's

0:38:11.520 --> 0:38:13.960
<v Speaker 3>across private and public. But we spend one point five

0:38:14.040 --> 0:38:16.880
<v Speaker 3>trillion dollars on Medicare and Medicaid. So the idea that

0:38:16.960 --> 0:38:19.400
<v Speaker 3>it's a big chunks, now, it's not that big of

0:38:19.400 --> 0:38:21.080
<v Speaker 3>a chunk. We spent you know, two point three trillion

0:38:21.120 --> 0:38:23.280
<v Speaker 3>dollars overall, and so to say fifty to one hundred

0:38:23.280 --> 0:38:25.719
<v Speaker 3>billion dollars a year of fraud, yeah, I think that

0:38:25.880 --> 0:38:28.799
<v Speaker 3>number is reasonable. Yeah, So can we move the needle

0:38:28.840 --> 0:38:31.440
<v Speaker 3>on it? Absolutely? There are things that we know work

0:38:31.520 --> 0:38:34.640
<v Speaker 3>really well, huge treatment effects at very low cost. Now,

0:38:34.760 --> 0:38:39.520
<v Speaker 3>government spending overall, we spend what six seven trillion dollars

0:38:39.560 --> 0:38:41.879
<v Speaker 3>a year in the government. When we look at these

0:38:41.920 --> 0:38:45.720
<v Speaker 3>other public programs there are some that have obvious frauds

0:38:45.719 --> 0:38:48.160
<v Speaker 3>going on, Like I mentioned the unemployment insurance system that

0:38:48.239 --> 0:38:51.080
<v Speaker 3>had tens or possibly one hundred billion dollars of fraud

0:38:51.160 --> 0:38:53.840
<v Speaker 3>there as well the PPP program. We spent one hundred

0:38:53.840 --> 0:38:56.560
<v Speaker 3>billion dollars or at least on fraud in that program.

0:38:56.760 --> 0:38:59.279
<v Speaker 3>And so not every program is rife with fraud. There

0:38:59.280 --> 0:39:01.879
<v Speaker 3>are programs that are very hard to defraud. It's hard

0:39:01.880 --> 0:39:04.279
<v Speaker 3>to defraud social Security why because they have your full

0:39:04.320 --> 0:39:06.040
<v Speaker 3>earnings record and they pick the number and they send

0:39:06.080 --> 0:39:09.840
<v Speaker 3>you a check. Very limited fraud in the social security system.

0:39:09.880 --> 0:39:11.840
<v Speaker 3>I think that there's probably a lot of fraud in

0:39:11.840 --> 0:39:14.600
<v Speaker 3>some of the infrastructure and defense, and so our framework

0:39:14.640 --> 0:39:17.000
<v Speaker 3>has to be like where does the government know the least,

0:39:17.400 --> 0:39:20.320
<v Speaker 3>the government doesn't really know what's going into every element

0:39:20.360 --> 0:39:22.719
<v Speaker 3>of a defense spend or every element of you know,

0:39:23.239 --> 0:39:25.600
<v Speaker 3>how is the road being built or what is the

0:39:25.600 --> 0:39:28.040
<v Speaker 3>hospital doing? And when you have those big information, these symmetries,

0:39:28.080 --> 0:39:29.920
<v Speaker 3>that's where the big frauds. So if I think that

0:39:29.920 --> 0:39:33.600
<v Speaker 3>there's one hundred billion dollars just for Medicare and medicaid spending,

0:39:33.719 --> 0:39:36.600
<v Speaker 3>or maybe you know, throw in the advantage spending and

0:39:36.640 --> 0:39:39.000
<v Speaker 3>the you know, other the federal employees health benefits and

0:39:39.000 --> 0:39:41.040
<v Speaker 3>the VA. To get to the hundred billion number of

0:39:41.080 --> 0:39:43.680
<v Speaker 3>that healthcare fraud, I think that it very easily that

0:39:43.800 --> 0:39:47.400
<v Speaker 3>hundred billion dollars also occurs. Yet again in some of

0:39:47.440 --> 0:39:48.880
<v Speaker 3>these other like defense, I'm sure.

0:39:48.640 --> 0:39:52.440
<v Speaker 2>That there's plan Jensen leader luis amazing. Thank you so

0:39:52.520 --> 0:39:53.440
<v Speaker 2>much for coming on them.

0:39:53.440 --> 0:39:54.319
<v Speaker 3>Park, Thanks for having me.

0:40:07.120 --> 0:40:11.360
<v Speaker 2>Tracy. I am pro getting rid of fraud. No, I've

0:40:11.440 --> 0:40:13.160
<v Speaker 2>for real, I don't take a lot of it.

0:40:13.160 --> 0:40:15.200
<v Speaker 1>It seems like a low bar. Although you did ask

0:40:15.239 --> 0:40:17.880
<v Speaker 1>repeatedly how you could commit it, but I know that

0:40:18.000 --> 0:40:19.840
<v Speaker 1>was for informational purposes only.

0:40:20.080 --> 0:40:22.680
<v Speaker 2>No, I'm going to actually I'm not going to get

0:40:22.719 --> 0:40:24.920
<v Speaker 2>into the business of medical fraud. I'm going to get

0:40:24.920 --> 0:40:28.439
<v Speaker 2>into the business of being one of those independent whistleblowers

0:40:28.440 --> 0:40:30.920
<v Speaker 2>to bounty hunters. Yeah, that sounds great, but I feel

0:40:30.920 --> 0:40:32.360
<v Speaker 2>like I need to know how it works so I

0:40:32.400 --> 0:40:35.000
<v Speaker 2>can identify it. But I really do think, you know,

0:40:35.120 --> 0:40:38.399
<v Speaker 2>setting aside, setting aside everything fraud is bad.

0:40:38.840 --> 0:40:40.759
<v Speaker 1>I do think, and I kind of said this in

0:40:40.800 --> 0:40:43.279
<v Speaker 1>the beginning, that we can all agree that fraud and

0:40:43.440 --> 0:40:45.680
<v Speaker 1>waste is bad, like no matter where you fall on

0:40:45.719 --> 0:40:49.080
<v Speaker 1>the political spectrum, because if you free up money that's

0:40:49.160 --> 0:40:52.000
<v Speaker 1>not doing anything, then you could, in theory, put it

0:40:52.040 --> 0:40:55.200
<v Speaker 1>to a different use and get more bang for your bucks,

0:40:55.200 --> 0:40:59.120
<v Speaker 1>so to speak. I think obviously, and we touched on this,

0:40:59.239 --> 0:41:03.359
<v Speaker 1>like a lot of these decisions over what's fraudulent or

0:41:03.600 --> 0:41:07.400
<v Speaker 1>especially what's wasteful maybe not necessarily fraud come down to

0:41:07.760 --> 0:41:10.279
<v Speaker 1>specific judgments and they can be subjective, and I think

0:41:10.280 --> 0:41:12.480
<v Speaker 1>that's where a lot of the disagreement is going to

0:41:12.480 --> 0:41:16.320
<v Speaker 1>be going forward. But I do think the point about

0:41:16.719 --> 0:41:20.440
<v Speaker 1>using the data better. The government must have some amazing data.

0:41:20.840 --> 0:41:23.240
<v Speaker 1>We kind of talked about it, especially in these specific

0:41:23.280 --> 0:41:24.960
<v Speaker 1>sectors like healthcare.

0:41:25.400 --> 0:41:27.719
<v Speaker 2>Two things on the data that were really interesting. So

0:41:27.840 --> 0:41:30.920
<v Speaker 2>one is just this idea that if you're a talented

0:41:31.040 --> 0:41:32.600
<v Speaker 2>data scientist.

0:41:32.360 --> 0:41:33.120
<v Speaker 1>Go to the doge.

0:41:33.239 --> 0:41:34.960
<v Speaker 2>And this has come up in some of our past

0:41:35.000 --> 0:41:37.919
<v Speaker 2>episodes that we've done with a few other guests. There

0:41:37.960 --> 0:41:41.719
<v Speaker 2>does seem to be this structural issue, right of how

0:41:41.800 --> 0:41:45.000
<v Speaker 2>government pays and whether the how compelling a job in

0:41:45.040 --> 0:41:48.000
<v Speaker 2>government is, et cetera. And so there does seem to

0:41:48.040 --> 0:41:50.759
<v Speaker 2>be an issue with how do you staff up a

0:41:50.800 --> 0:41:54.480
<v Speaker 2>big team of data scientists that are incentive and have

0:41:54.560 --> 0:41:57.640
<v Speaker 2>the agency and capacity to use that data. Do something

0:41:57.800 --> 0:42:00.880
<v Speaker 2>when they discover it. And then you know, very interesting

0:42:01.520 --> 0:42:05.480
<v Speaker 2>comment that last one about it's very hard to defraud

0:42:05.520 --> 0:42:08.680
<v Speaker 2>social security, and so this idea of like where is

0:42:08.719 --> 0:42:12.600
<v Speaker 2>the fraud most likely to exist? Areas in which there

0:42:12.640 --> 0:42:17.160
<v Speaker 2>is some limited asymmetrical as economists like to use information,

0:42:17.360 --> 0:42:20.440
<v Speaker 2>which is, you know, the government doesn't know what happens

0:42:20.800 --> 0:42:24.440
<v Speaker 2>when you are not you and I go into a doctor, right,

0:42:24.440 --> 0:42:28.000
<v Speaker 2>there's some level of information asymmetry there. It doesn't really

0:42:28.080 --> 0:42:30.439
<v Speaker 2>know what kind of walker you or I are going

0:42:30.480 --> 0:42:33.880
<v Speaker 2>to need in thirty or forty years to get around,

0:42:33.880 --> 0:42:37.000
<v Speaker 2>et cetera. And then furthermore, we didn't touch on it,

0:42:37.040 --> 0:42:40.719
<v Speaker 2>but I do think or absolutely as a podcast, are

0:42:40.719 --> 0:42:42.759
<v Speaker 2>going to need to do more, should do way more

0:42:42.800 --> 0:42:45.799
<v Speaker 2>on defense spending. There's a million angles that we have

0:42:45.840 --> 0:42:48.239
<v Speaker 2>to do on that. But you could see like we

0:42:48.280 --> 0:42:50.440
<v Speaker 2>don't really know what went into the assembly of this

0:42:50.520 --> 0:42:52.920
<v Speaker 2>and the cost of this program and is this part

0:42:53.000 --> 0:42:55.879
<v Speaker 2>really worth this much money or maybe as a competition thing.

0:42:56.080 --> 0:42:58.920
<v Speaker 2>There's a lot of fruit there for future episodes.

0:42:58.960 --> 0:43:01.040
<v Speaker 1>But I do think one of the big tensions here

0:43:01.200 --> 0:43:05.719
<v Speaker 1>is the sort of government generalists versus like the experts

0:43:05.760 --> 0:43:08.640
<v Speaker 1>that they're listening to what I mean is, for instance,

0:43:08.680 --> 0:43:12.200
<v Speaker 1>if you're a defense contractor and you're building I don't know,

0:43:12.360 --> 0:43:16.120
<v Speaker 1>like a submarine launch pad or whatever, like the government

0:43:16.120 --> 0:43:18.719
<v Speaker 1>official isn't necessarily going to know all the nuts and

0:43:18.760 --> 0:43:21.680
<v Speaker 1>bolts that need to go into that. And so yeah,

0:43:21.719 --> 0:43:24.760
<v Speaker 1>I always wonder how you sort of overcome that informational gap.

0:43:24.840 --> 0:43:26.600
<v Speaker 2>There's a lot there. Let's do more on this topic.

0:43:26.680 --> 0:43:28.120
<v Speaker 1>All right, shall we leave it there for now?

0:43:28.239 --> 0:43:29.000
<v Speaker 2>Let's leave it there.

0:43:29.239 --> 0:43:31.880
<v Speaker 1>This has been another episode of the All Thoughts podcast.

0:43:32.000 --> 0:43:34.800
<v Speaker 1>I'm Tracy Alloway. You can follow me at Tracy Alloway.

0:43:34.960 --> 0:43:37.960
<v Speaker 2>And I'm Joe Wisenthal. You can follow me at the Stalwart.

0:43:38.160 --> 0:43:42.200
<v Speaker 2>Follow our guest Jetson leader Luis. He's at jetson Econ. Also,

0:43:42.239 --> 0:43:44.680
<v Speaker 2>he has a number of papers on his website that

0:43:44.719 --> 0:43:47.120
<v Speaker 2>you can just click on and go read. They're all

0:43:47.120 --> 0:43:51.120
<v Speaker 2>really fascinating. Follow our producers Carmen Rodriguez at Carman Armann

0:43:51.200 --> 0:43:54.279
<v Speaker 2>dash Ol Bennett at Dashbot and kill Brooks at Kilbrooks.

0:43:54.560 --> 0:43:57.239
<v Speaker 2>Thank you to our producer Moses Onam. For more Odd

0:43:57.320 --> 0:44:00.239
<v Speaker 2>Laws content, go to Bloomberg dot com slash oddlog. We

0:44:00.239 --> 0:44:03.200
<v Speaker 2>have transcripts, a blog, and a newsletter, and you can

0:44:03.280 --> 0:44:06.160
<v Speaker 2>chet about all of these topics in our discord with

0:44:06.280 --> 0:44:09.760
<v Speaker 2>fellow listeners Discord, dot gg, slash up, lots.

0:44:09.800 --> 0:44:12.120
<v Speaker 1>And if you enjoy all lots, if you like it

0:44:12.160 --> 0:44:15.320
<v Speaker 1>when we dive deep into the data around government fraud,

0:44:15.440 --> 0:44:18.160
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0:45:00.480 --> 0:45:00.719
<v Speaker 1>Eight