WEBVTT - How to Save the Most Lives

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<v Speaker 1>Pushkin. Think of everything everybody in the United States buys

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<v Speaker 1>in a year, all the food, all the clothes, insurance, education, housing, travel, iPhones,

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<v Speaker 1>everything of all the stuff we buy. Out of all

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<v Speaker 1>the money we spend, around twenty percent, around one in

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<v Speaker 1>five dollars, is spent on healthcare. This is extraordinary. It

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<v Speaker 1>is completely out of line with what other people in

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<v Speaker 1>other countries spend. It's about twice as much as what

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<v Speaker 1>other rich countries spend relative to their GDPs on healthcare.

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<v Speaker 1>And crucially, we in the US do not get better

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<v Speaker 1>health outcomes, So we're spending lots of money without much

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<v Speaker 1>to show for it. For a long time, people have

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<v Speaker 1>looked at this problem and thought, what if instead of

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<v Speaker 1>paying doctors and hospitals to treat us when we get sick,

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<v Speaker 1>we could pay them to keep us healthy. Is there

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<v Speaker 1>some way we could spend less on healthcare and get

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<v Speaker 1>better results. I'm Jacob Goldstein, and this is what's your problem.

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<v Speaker 1>My guest today is Farzad Mosta Shari. He's the co

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<v Speaker 1>founder and CEO of a healthcare company called Alde. Farzod's

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<v Speaker 1>problem is this, how can you pay doctors to keep

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<v Speaker 1>us healthy rather than treating us after we get sick.

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<v Speaker 1>People have been struggling to solve this problem for decades,

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<v Speaker 1>but for a bunch of reasons you'll hear about on

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<v Speaker 1>the show, Farzad and his fellow travelers may be the

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<v Speaker 1>ones to finally solve them. Before he became an entrepreneur,

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<v Speaker 1>Farzad went to medical school, but he told me that

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<v Speaker 1>back in the mid nineties, when he was doing his

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<v Speaker 1>clinical training, his residency and internal medicine, he started to

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<v Speaker 1>question what he was and what he was not learning.

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<v Speaker 1>I kept looking at stuff that was happening and being like,

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<v Speaker 1>this doesn't make sense to me, Like why, like you're

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<v Speaker 1>telling you're teaching me how to you know, treat someone

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<v Speaker 1>who's coming in here with difficulty breeding in the emergency room.

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<v Speaker 1>I get that, Like it's really important that that person

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<v Speaker 1>get their treatment, their nebulizer so they can breathe again,

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<v Speaker 1>that's really I get that. But why is this person

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<v Speaker 1>coming in today? Why is it all of a sudden

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<v Speaker 1>we're seeing people coming in with breeding problems. Why why

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<v Speaker 1>is it that the people who tend to come in

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<v Speaker 1>with breeding problems tend to be from these neighborhoods, not

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<v Speaker 1>other neighborhoods. And it was those questions that were between

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<v Speaker 1>medicine and public health and ibidemology that really grabbed me

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<v Speaker 1>so far as Odd finished his residency and went into

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<v Speaker 1>public health, and he had this this trait that at

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<v Speaker 1>the time was kind of a superpower. He was really

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<v Speaker 1>into computers. So I came of age at a time

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<v Speaker 1>when in a way, you know, asking the question can

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<v Speaker 1>you use a computer for that was was like, uh,

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<v Speaker 1>you know, unlocked a lot of a lot of value.

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<v Speaker 1>And so I joined the New York City Health Department

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<v Speaker 1>and I said, oh, public health surveillance, can we use

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<v Speaker 1>a computer for that? Right? So one of my first startup, Jacob,

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<v Speaker 1>was a within the New York City Department of Health

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<v Speaker 1>where I started. Was one of the group of people

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<v Speaker 1>who started this national idea of why don't we why

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<v Speaker 1>don't we track when when someone gets logged into the

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<v Speaker 1>emergency room for just for their registering the patient in

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<v Speaker 1>the emergency room, or when an ambulance gets dispatched, or

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<v Speaker 1>when when when some item in the pharmacy goes beep

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<v Speaker 1>across the scanner. Like before all those things were on paper.

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<v Speaker 1>Now they're all electronic, which means I can get a

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<v Speaker 1>copy and if I can get a copy, then I

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<v Speaker 1>can tell you in real time that something's happening to

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<v Speaker 1>the health of the city. Right that was That was

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<v Speaker 1>like consumed me for five to ten years, and it

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<v Speaker 1>was really really cool. It was really really fun, and

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<v Speaker 1>that system TILT today is the system that we use,

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<v Speaker 1>for example, during COVID to see that what was happening

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<v Speaker 1>in the city. But then something fundamentally shifted in my life,

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<v Speaker 1>which was Mike Bloomberg asked the Commissioner of Health at

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<v Speaker 1>the time, who turned to me. I was like the

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<v Speaker 1>data guy, and the question he asked was, don't worry

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<v Speaker 1>about the politics, don't worry about the budget, don't worry

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<v Speaker 1>about any of that. Your job is to answer the question.

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<v Speaker 1>What's the question? And the question was what's the problem?

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<v Speaker 1>What's your problem? As you might say, the question was, hey,

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<v Speaker 1>how do we save the most lives? And I was

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<v Speaker 1>like can I swear? Oh yeah? Fuck? That was like

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<v Speaker 1>what am I doing? Like? What am I doing? Why

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<v Speaker 1>am I not starting with that question? That is such

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<v Speaker 1>the right question, how do we save the most lives?

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<v Speaker 1>And it turned out that no one in like, we're

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<v Speaker 1>spending three four trillion dollars, We're doing all this activity right,

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<v Speaker 1>and no one was starting from that viewpoint. There were

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<v Speaker 1>hundreds of thousands of research articles written about various things

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<v Speaker 1>that we're doing, and no one was saying, what is

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<v Speaker 1>this the scientific, evidence based answer to the question how

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<v Speaker 1>do we save the most lives? And it turned out,

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<v Speaker 1>in New York City in the year of Our Lord

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<v Speaker 1>two thousand and two, the answer to that was smoking.

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<v Speaker 1>The answer to that was we're going to attack smoking.

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<v Speaker 1>So that meant that we were going to dedicate more

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<v Speaker 1>of our living waking hours to how do we reduce

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<v Speaker 1>smoking in New York City than we do to anything else.

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<v Speaker 1>And within the time span of four years, we drove

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<v Speaker 1>I was a small part of a big effort to

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<v Speaker 1>drive teen smoking from fourteen to seven percent in New

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<v Speaker 1>York City, to drive adult smoking from twenty two point

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<v Speaker 1>eight percent to sixteen point eight percent. That saved more

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<v Speaker 1>lives than anything else we could have possibly done. And

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<v Speaker 1>then the next question was, Okay, that's public health. That's great,

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<v Speaker 1>that's changing the environment within which people make decisions. But

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<v Speaker 1>we know what we're spending all this money on healthcare?

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<v Speaker 1>What can healthcare do to save the most lives? Just

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<v Speaker 1>stay with that question. That's such a clarifying question that is,

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<v Speaker 1>so that is my flaming sword, right, It's like, how

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<v Speaker 1>can we save how can medicine? How can healthcare save

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<v Speaker 1>the most lives? And again no one had the answer.

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<v Speaker 1>So we published this paper, Randy analysis. Do you want

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<v Speaker 1>to take a guess, Jacob, as to what thing in healthcare?

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<v Speaker 1>If we you know, we have thousands of evidence based

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<v Speaker 1>guidelines and protocols and quality measures, and like, what is

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<v Speaker 1>the one thing? And then phrase the question, well that

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<v Speaker 1>if you took the performance of the American healthcare system

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<v Speaker 1>from where it is today to where the best American

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<v Speaker 1>healthcare institutions can perform, would reduce the number of premature

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<v Speaker 1>deaths by the greatest amount. Do not have any idea

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<v Speaker 1>what that would be? Based on my preparation for this interview,

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<v Speaker 1>I'm gonna guess lowering people's blood pressure. Yeah all right,

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<v Speaker 1>see that's not fair because you've been listening. I did

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<v Speaker 1>my homework. Homework, you did your homework. But the funny

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<v Speaker 1>thing is that if you ask that question of you know,

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<v Speaker 1>hospital CEOs, they'll say, like getting stroke patients to the

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<v Speaker 1>hospital ten minutes faster something. Yeah. Yeah, they'll say, like,

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<v Speaker 1>let's not kill people in the hospital through infections, they'll say, right, well,

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<v Speaker 1>because it's not really traditionally a hospital CEO's job to

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<v Speaker 1>keep people out of the hospital, which is what you

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<v Speaker 1>want to do if you want people not to die,

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<v Speaker 1>right exactly. But even if you if you talk to

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<v Speaker 1>health planned people that they don't know right there, if

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<v Speaker 1>you if you look at the number of quality measures

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<v Speaker 1>that doctors are incentivized and measured against, right, like, there's

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<v Speaker 1>not a there's not a ten x difference between the

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<v Speaker 1>amount of importance placed on blood pressure control. Then there

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<v Speaker 1>is the amount of importance placed on mamograms and colonoscopies

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<v Speaker 1>and flu vaccines and all that other stuff. Right, it's

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<v Speaker 1>all good stuff. And you're saying the evidence as the

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<v Speaker 1>efficacy of lowering blood pressure means there should be a

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<v Speaker 1>ten x difference between blood pressure and right, it's just

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<v Speaker 1>gigantic and it's sable, right like, And it's just science

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<v Speaker 1>of blow Like we know there's cheap, cheap, safe drugs,

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<v Speaker 1>there's life like, we know how to lower people's blood pressure.

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<v Speaker 1>And and then that was the that became my problem. Right,

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<v Speaker 1>It's like, well, why aren't we why aren't we why

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<v Speaker 1>aren't we controlling blood pressure, and I came to the

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<v Speaker 1>wrong answer. And the reason I came to the wrong

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<v Speaker 1>answer was because I tended to be the kind of

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<v Speaker 1>person who said, can use a computer for that. You

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<v Speaker 1>saw the computer as it was, It was your hammer.

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<v Speaker 1>So every problem looked like a nail that could be

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<v Speaker 1>hit with the computer. And look, there was some truth

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<v Speaker 1>to it, right, Like the truth to it was, you

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<v Speaker 1>can't improve blood pressure control if you can't even know

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<v Speaker 1>what blood pressure control rates are, right, Because you actually

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<v Speaker 1>couldn't tell what blood pressure control rates were in any

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<v Speaker 1>given practice, any given population, because the data was trapped

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<v Speaker 1>in deadwood that there were you would write it down,

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<v Speaker 1>You would write down the person's bloodressure control. How would

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<v Speaker 1>I even know, if I'm a doctor what percent of

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<v Speaker 1>my patients had their blood pressure control? I literally couldn't,

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<v Speaker 1>with a room full of paper charts, answer that question.

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<v Speaker 1>I couldn't have quality improvement. I couldn't have decisions support

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<v Speaker 1>that would prompt me when I'm seeing a patient. I

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<v Speaker 1>couldn't have a registry that made a list of the

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<v Speaker 1>patients to say, can you use a computer for that? Right?

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<v Speaker 1>As your friend that problem with the computer, yeah, so

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<v Speaker 1>then so then we and we had a big concern

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<v Speaker 1>around around what was now called health equity. So we

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<v Speaker 1>went around to the doctors in New York City's poorest neighborhoods,

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<v Speaker 1>in Harlem, in the South Bronx, in Central Brooklyn, bed Stye,

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<v Speaker 1>and we said, hey, guess what you've been Medicine has

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<v Speaker 1>been using paper and pen for thousands of years. We're

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<v Speaker 1>going to give you a free electronic health record with

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<v Speaker 1>all of these functionalities built into it. And we spent

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<v Speaker 1>the next year and a half building into the h

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<v Speaker 1>ARE the functionality electronic health record, the functionality that we

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<v Speaker 1>thought should exist in those electronic health records, and then

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<v Speaker 1>we did that. We rolled it out to half of

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<v Speaker 1>all those primary care docs in New York cities poorest neighborhoods.

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<v Speaker 1>So mission accomplished. But you know, you want to guess

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<v Speaker 1>what happened to blood pressure control rates? They did not improve.

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<v Speaker 1>They did not improve, And the number of strokes and

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<v Speaker 1>the number of heart attacks and the number of kidney

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<v Speaker 1>failures from that continued just as much. And so that

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<v Speaker 1>was my continued frustrating He said, what was your frustration.

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<v Speaker 1>That's my frustration is, um, I didn't feel like I

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<v Speaker 1>felt like we'd done all this work, and at the

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<v Speaker 1>end of the day, the reason we'd done all that

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<v Speaker 1>work hadn't hadn't been accomplished. And I reluctantly, you know,

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<v Speaker 1>for twenty years, Jacob, I had like I was a

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<v Speaker 1>public health guy, I was a government person, like like

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<v Speaker 1>I was not a private sector person. And in fact,

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<v Speaker 1>I kind of thought that the profit motive was the

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<v Speaker 1>problem in many cases. And that's right, and it's wrong.

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<v Speaker 1>And what I reluctantly came to see is that the

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<v Speaker 1>financial incentives are the water we swimming and and and

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<v Speaker 1>that sometimes we and even see it likes it shapes

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<v Speaker 1>the force of everything around us, the gravity that pulls

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<v Speaker 1>on us or not right, like everything around us is

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<v Speaker 1>there's this invisible thing which is the financial incentives of

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<v Speaker 1>the system. And I was trying to ignore them. I

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<v Speaker 1>was trying to pretend like they didn't exist. And in fact,

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<v Speaker 1>the financial incentives of the healthcare system are to treat strokes.

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<v Speaker 1>They are not to No one makes billions of dollars

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<v Speaker 1>preventing strokes. No one makes you make money waiting until

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<v Speaker 1>literally waiting until someone's kidney fails, and then there's billions

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<v Speaker 1>of dollars available to you. For treatment of like taking

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<v Speaker 1>someone's blood out of their body, running it through a

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<v Speaker 1>machine and returning it to them. There's a lot of

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<v Speaker 1>money in that, not needing that. There's not a lot

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<v Speaker 1>of money doing that. You're talking about strokes and kidney

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<v Speaker 1>fail because we know that lowering blood pressure, which is

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<v Speaker 1>relatively cheap to do, lowers the risk of strokes and

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<v Speaker 1>kidney failure. But you're saying we don't lower blood pressure,

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<v Speaker 1>and that happens because that's literally the money in healthcare flows,

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<v Speaker 1>because doctors and hospitals get money for treating strokes and

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<v Speaker 1>kidney failure and not for lowering blood pressure. Yeah. I

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<v Speaker 1>mean there's one the one part of the healthcare system

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<v Speaker 1>that is supposed to be in charge of the controlling

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<v Speaker 1>the blood pressure, which is primary care. Right, it's the

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<v Speaker 1>primary care part. And guess what part of our healthcare

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<v Speaker 1>system makes the least money, makes the you know, has

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<v Speaker 1>the least prestige in in where I trained, right, it's

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<v Speaker 1>it's Oh, those those people who you know they talk

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<v Speaker 1>to patients, they don't they don't you know, they don't

0:14:01.996 --> 0:14:04.796
<v Speaker 1>cut the patients, they don't do procedures on them, they

0:14:04.796 --> 0:14:08.076
<v Speaker 1>don't do expensive scans on them, right, They sit in

0:14:08.116 --> 0:14:10.316
<v Speaker 1>a little room and they talk to the patient about

0:14:10.356 --> 0:14:13.476
<v Speaker 1>why they should take their medicine. Right. Oh, those those

0:14:13.636 --> 0:14:17.476
<v Speaker 1>those poor, those poor little primary care doctors. We spend

0:14:17.516 --> 0:14:21.156
<v Speaker 1>five percent of healthcare costs on primary care, five percent.

0:14:21.316 --> 0:14:23.396
<v Speaker 1>We spend as much on ICUs as we do on

0:14:23.396 --> 0:14:28.316
<v Speaker 1>primary and even primary care doctors traditionally do not get

0:14:28.356 --> 0:14:31.556
<v Speaker 1>more money when their patients stay healthy. Right. And I

0:14:31.636 --> 0:14:36.116
<v Speaker 1>know primary care doctors are not motivated by money explicitly,

0:14:36.276 --> 0:14:40.796
<v Speaker 1>but but still the incentives don't push primary care doctors

0:14:40.836 --> 0:14:44.196
<v Speaker 1>to reduce their patient's blood pressure though they I'm sure

0:14:44.276 --> 0:14:47.396
<v Speaker 1>they want to, they don't get paid more if they do. Traditionally,

0:14:49.236 --> 0:14:55.396
<v Speaker 1>until twenty twelve, big moment, big narrative moment happening, big moment,

0:14:59.876 --> 0:15:04.316
<v Speaker 1>say healthcare economics, Cliffhanger. We'll get to that big moment

0:15:04.596 --> 0:15:07.076
<v Speaker 1>and to how it led Farza to start his company

0:15:07.556 --> 0:15:15.476
<v Speaker 1>in just a minute. Okay, now back to the show

0:15:15.636 --> 0:15:19.596
<v Speaker 1>to find out what exactly happened in twenty twelve. In

0:15:19.636 --> 0:15:25.916
<v Speaker 1>twenty twelves, the rules for accountable care are published by

0:15:26.116 --> 0:15:28.916
<v Speaker 1>the Center for Medicare and Medicaid Services. That basically says,

0:15:29.396 --> 0:15:31.956
<v Speaker 1>if you get a group of primary care dots together,

0:15:32.756 --> 0:15:37.196
<v Speaker 1>and you gain accountability for the total cost of care

0:15:37.356 --> 0:15:40.516
<v Speaker 1>for the patients they care for. If those costs come down,

0:15:41.396 --> 0:15:45.596
<v Speaker 1>we will give you half of that those savings. And

0:15:45.676 --> 0:15:48.836
<v Speaker 1>it was like it was like the heavens had opened up, right,

0:15:49.196 --> 0:15:51.356
<v Speaker 1>And I was like, oh my god. And I was like,

0:15:51.876 --> 0:15:54.996
<v Speaker 1>primary care docs don't realize the power they have in

0:15:55.036 --> 0:16:00.756
<v Speaker 1>this new world. And the only problem, right was that

0:16:00.796 --> 0:16:05.636
<v Speaker 1>if you're if you're an independent, small primary care practice,

0:16:06.476 --> 0:16:10.476
<v Speaker 1>you don't really have the scale to to band together

0:16:10.556 --> 0:16:13.196
<v Speaker 1>with one hundred other groups to pool your patients, to

0:16:13.316 --> 0:16:16.876
<v Speaker 1>take that actuarial risk, to understand the regulations, to have

0:16:16.916 --> 0:16:19.556
<v Speaker 1>the data systems to know what the playbook is to

0:16:19.636 --> 0:16:23.196
<v Speaker 1>actually reduce costs, right. And I was like, yeah, but

0:16:23.876 --> 0:16:28.116
<v Speaker 1>that's easy. That's the easy stop you for that. You

0:16:28.116 --> 0:16:31.836
<v Speaker 1>can use the computer for that. Someone's good. I was like,

0:16:32.196 --> 0:16:37.036
<v Speaker 1>someone's gonna build a business doing this. So I just

0:16:37.116 --> 0:16:40.036
<v Speaker 1>want to I just want to make sure that we're

0:16:40.076 --> 0:16:45.356
<v Speaker 1>really clear about what rules are actually changing here, right Yeah,

0:16:45.436 --> 0:16:52.356
<v Speaker 1>So okay, so um so the traditional way that almost everybody,

0:16:52.596 --> 0:16:57.636
<v Speaker 1>including medicare, the federal program that pays for old people's healthcare.

0:16:57.676 --> 0:17:00.196
<v Speaker 1>It's but probably the most important sort of payer and

0:17:00.236 --> 0:17:04.316
<v Speaker 1>healthcare in this country. Right, they pay doctors fee for service.

0:17:04.356 --> 0:17:06.276
<v Speaker 1>You do your doctor, you do something for a patient,

0:17:06.396 --> 0:17:09.036
<v Speaker 1>you get money. And so what's happening in two thousand

0:17:09.236 --> 0:17:15.076
<v Speaker 1>twelve is the government is saying Medicare is offering doctors,

0:17:15.196 --> 0:17:19.076
<v Speaker 1>primary care doctors in particular, a different way to get paid, right,

0:17:19.556 --> 0:17:22.796
<v Speaker 1>and they call it accountable care, Like, what exactly does

0:17:22.876 --> 0:17:27.716
<v Speaker 1>that mean? So that means that if you submit an

0:17:27.756 --> 0:17:30.916
<v Speaker 1>application to the government, your primary care doctor, you and

0:17:30.956 --> 0:17:34.076
<v Speaker 1>your friends submit an application to the government, and the

0:17:34.116 --> 0:17:39.476
<v Speaker 1>government says, oh, you care for these named human beings, right,

0:17:39.556 --> 0:17:43.956
<v Speaker 1>you're the primary care doctor for these Medicare beneficiaries. So

0:17:43.996 --> 0:17:48.516
<v Speaker 1>if you have ten thousand Medicare beneficiaries who are getting

0:17:48.556 --> 0:17:52.636
<v Speaker 1>primary care from you, the Medicare actuaries, that's their job.

0:17:53.116 --> 0:17:56.076
<v Speaker 1>They look to see who are those people, what have

0:17:56.116 --> 0:17:58.716
<v Speaker 1>their costs been in the past, how sick are they?

0:17:58.796 --> 0:18:01.636
<v Speaker 1>And they're going to say, ah, next year, we think

0:18:01.676 --> 0:18:05.956
<v Speaker 1>those ten thousand lives are going to cost us a

0:18:06.156 --> 0:18:11.596
<v Speaker 1>hundred million dollars. Now, if the actual costs, once all

0:18:11.596 --> 0:18:14.036
<v Speaker 1>the claims are submitted and all the costs are counted up,

0:18:14.316 --> 0:18:16.996
<v Speaker 1>all of the hospitalizations, all the specialist visits, all the

0:18:17.036 --> 0:18:20.196
<v Speaker 1>medications and scans and everything else that gets done to

0:18:20.276 --> 0:18:23.196
<v Speaker 1>the patient. If that actual cost comes in not at

0:18:23.236 --> 0:18:26.156
<v Speaker 1>one hundred million, which was our budget, but at ninety million,

0:18:26.356 --> 0:18:30.356
<v Speaker 1>there's ten million dollars of savings, and we will share

0:18:30.636 --> 0:18:34.676
<v Speaker 1>those savings with these docs. We will give say half

0:18:34.996 --> 0:18:37.796
<v Speaker 1>of those savings to the docks. So of that ten

0:18:37.796 --> 0:18:40.556
<v Speaker 1>million dollars of savings, the government gets to keep five

0:18:40.636 --> 0:18:45.396
<v Speaker 1>million they spent less, and the docks get five million.

0:18:45.796 --> 0:18:49.516
<v Speaker 1>And you have to prove that you actually improved quality

0:18:49.596 --> 0:18:53.836
<v Speaker 1>while you reduced cost of care. It's a fundamental rewriting

0:18:53.836 --> 0:18:57.556
<v Speaker 1>of the incentive structure of American healthcare. So you see

0:18:57.596 --> 0:19:00.716
<v Speaker 1>this new thing in the world, and what do you think.

0:19:02.236 --> 0:19:05.636
<v Speaker 1>I think, Who's someone's going to build this business that

0:19:06.156 --> 0:19:08.956
<v Speaker 1>does this? And I went to the Brookings Institute where

0:19:08.996 --> 0:19:10.756
<v Speaker 1>they had done a lot of the foundational work on

0:19:10.796 --> 0:19:13.876
<v Speaker 1>this idea, and I created a learning network to teach

0:19:13.876 --> 0:19:15.676
<v Speaker 1>other people how to do it, and we published a

0:19:15.716 --> 0:19:18.356
<v Speaker 1>tool kit, and we had a learning collaborative. And I

0:19:18.436 --> 0:19:21.716
<v Speaker 1>kept waiting for nine months. I kept waiting for I

0:19:21.836 --> 0:19:24.396
<v Speaker 1>was like literally every morning I'd wake up and I'd

0:19:24.396 --> 0:19:27.756
<v Speaker 1>be like, Who's Who's starting a company to do this.

0:19:27.956 --> 0:19:30.796
<v Speaker 1>Where where is that come? It's such an obvious idea,

0:19:30.916 --> 0:19:33.556
<v Speaker 1>I thought to myself, it is like and the obvious

0:19:33.596 --> 0:19:35.636
<v Speaker 1>thing for me was, well, I ain't going to be

0:19:35.636 --> 0:19:40.836
<v Speaker 1>the hospitals, right, the most powerful people in healthcare today

0:19:41.116 --> 0:19:45.756
<v Speaker 1>are hospitals and health systems under one incentive structure. But

0:19:46.036 --> 0:19:50.796
<v Speaker 1>getting those people, the incumbents, the ones who are powerful

0:19:50.836 --> 0:19:54.036
<v Speaker 1>in the current system, the goliaths, getting them to change

0:19:54.076 --> 0:19:56.996
<v Speaker 1>their economic model, well, that's never going to happen. Whereas

0:19:57.276 --> 0:19:58.996
<v Speaker 1>you go to a you know, we got a group

0:19:59.036 --> 0:20:01.436
<v Speaker 1>of primary care dogs, You're like, hey, you got nothing

0:20:01.436 --> 0:20:05.396
<v Speaker 1>to lose but your chains. Yeah, they already don't like

0:20:05.516 --> 0:20:07.876
<v Speaker 1>the system. Right with primary care dots. If you're starting

0:20:07.916 --> 0:20:11.716
<v Speaker 1>with people who are disgruntled as a as a general rule,

0:20:12.076 --> 0:20:14.836
<v Speaker 1>are appropriately pissed off of the current system, doesn't help

0:20:14.876 --> 0:20:16.716
<v Speaker 1>them take the care of their patients that they believe

0:20:16.996 --> 0:20:20.076
<v Speaker 1>their patients should be getting. So okay, every day you're

0:20:20.116 --> 0:20:24.156
<v Speaker 1>looking for somebody to start this obvious company doesn't happen.

0:20:24.876 --> 0:20:29.116
<v Speaker 1>And so I went for a walk with the VC

0:20:30.316 --> 0:20:37.316
<v Speaker 1>and that metaphor or literal did you actually go literal

0:20:35.676 --> 0:20:40.636
<v Speaker 1>without Yes? Yes I did, Yes I did. They got

0:20:40.676 --> 0:20:43.316
<v Speaker 1>it right, away, and they were, you know, they saw

0:20:43.356 --> 0:20:49.076
<v Speaker 1>the long term opportunity for combining information and incentives in

0:20:49.116 --> 0:20:51.236
<v Speaker 1>a new way and creating a new business model, and

0:20:51.276 --> 0:20:54.916
<v Speaker 1>so they like, literally we had one one pitch meeting

0:20:55.276 --> 0:20:58.996
<v Speaker 1>after that, and they they they gave us the money

0:20:59.036 --> 0:21:01.876
<v Speaker 1>to start the company. To to my co founder and

0:21:01.916 --> 0:21:06.556
<v Speaker 1>I to Guvees who had never started business before. Were

0:21:06.596 --> 0:21:11.156
<v Speaker 1>there moments as your getting the company going, as it's growing,

0:21:11.556 --> 0:21:14.956
<v Speaker 1>when there were things that were harder than you expected,

0:21:15.076 --> 0:21:17.556
<v Speaker 1>things that didn't work that you thought would work. I mean,

0:21:17.676 --> 0:21:21.436
<v Speaker 1>is this obvious idea to you? Yeah? Was it obvious

0:21:21.436 --> 0:21:28.916
<v Speaker 1>to everybody else? Well, obviously not. And I think the

0:21:30.116 --> 0:21:32.996
<v Speaker 1>biggest thing that didn't make sense to people was the

0:21:33.436 --> 0:21:40.916
<v Speaker 1>going after these independent practices community docs as opposed to

0:21:40.996 --> 0:21:47.716
<v Speaker 1>the big, gleaming health centers. But the thing that really

0:21:48.916 --> 0:21:53.636
<v Speaker 1>shook us was we we went out and we signed

0:21:53.676 --> 0:21:58.916
<v Speaker 1>up our first groups of doctors in four states, and

0:21:59.876 --> 0:22:03.236
<v Speaker 1>it felt just charmed. Everything was magic, And we built

0:22:03.236 --> 0:22:06.676
<v Speaker 1>our software system and we started analyzing the data and

0:22:06.716 --> 0:22:08.916
<v Speaker 1>we started doing the stuff that we knew was the

0:22:09.156 --> 0:22:14.556
<v Speaker 1>right things to do for patients, and wholly commole hospitalization

0:22:14.716 --> 0:22:19.396
<v Speaker 1>rates came down measurably within that first year seven percent.

0:22:20.316 --> 0:22:23.196
<v Speaker 1>It worked. It worked, and it was like and it

0:22:23.276 --> 0:22:24.836
<v Speaker 1>was you know a lot of elbow grease and grit

0:22:24.916 --> 0:22:28.876
<v Speaker 1>and whatever, but just just churning the focus of these

0:22:28.916 --> 0:22:34.236
<v Speaker 1>primary care docs on reducing r visits, reducing hospitalizations was working.

0:22:35.036 --> 0:22:38.436
<v Speaker 1>And I was like, oh my god, charmed life, right, Like,

0:22:38.476 --> 0:22:40.956
<v Speaker 1>this thing is, this thing is gonna work, gonna totally

0:22:40.956 --> 0:22:44.916
<v Speaker 1>take off. And then we got the results from our

0:22:44.996 --> 0:22:51.156
<v Speaker 1>first year's performance, and sure enough, total hospitalizations went down

0:22:51.196 --> 0:22:55.036
<v Speaker 1>by seven percent, the biggest source of suffering in American healthcare.

0:22:55.116 --> 0:23:00.116
<v Speaker 1>Quality went up and costs did not come down. Huh

0:23:00.156 --> 0:23:02.636
<v Speaker 1>So it didn't work. It seemed like it was working,

0:23:02.676 --> 0:23:06.916
<v Speaker 1>but the core bottom line things, they didn't come down,

0:23:07.436 --> 0:23:09.996
<v Speaker 1>and we only get paid. Like we had done all

0:23:09.996 --> 0:23:13.876
<v Speaker 1>this work, burned all this cash, hired all these people,

0:23:13.956 --> 0:23:17.036
<v Speaker 1>gotten all the hopes of these doctors up right, and

0:23:17.276 --> 0:23:21.116
<v Speaker 1>and we and these investors and we told them, hey,

0:23:21.116 --> 0:23:24.116
<v Speaker 1>it's working. We're gonna get paid. And you don't get

0:23:24.116 --> 0:23:27.356
<v Speaker 1>paid for improving quality or reducing hospitalizations. You get paid

0:23:27.356 --> 0:23:30.036
<v Speaker 1>if total cost of care comes down. And when we

0:23:30.196 --> 0:23:33.236
<v Speaker 1>looked into it and we actually published a paper, you're like,

0:23:33.796 --> 0:23:36.596
<v Speaker 1>this sucked. Let's tell the world, how much pain we're

0:23:36.596 --> 0:23:39.956
<v Speaker 1>in and what happened? And so what we learned was

0:23:40.476 --> 0:23:45.156
<v Speaker 1>that the hospitals, when faced with a decrease in utilization,

0:23:45.996 --> 0:23:50.596
<v Speaker 1>changed their coding so they got paid more for admission.

0:23:50.996 --> 0:23:53.996
<v Speaker 1>And to be clear, changing their coding means changing the

0:23:53.996 --> 0:23:56.916
<v Speaker 1>way they bill, so they're billing so that they get more.

0:23:57.316 --> 0:23:59.476
<v Speaker 1>There are fewer patients coming to the hospital, they're billing

0:23:59.516 --> 0:24:04.116
<v Speaker 1>more for each patient. For each patient. Yeah, so okay,

0:24:04.156 --> 0:24:06.516
<v Speaker 1>so so far you're not making any money, right because

0:24:06.556 --> 0:24:09.876
<v Speaker 1>you make money by saving money. What do you how

0:24:09.876 --> 0:24:13.116
<v Speaker 1>do you respond? Well, we had also in the in

0:24:13.196 --> 0:24:17.956
<v Speaker 1>the intim we had, we had massively expanded, So not

0:24:18.036 --> 0:24:20.476
<v Speaker 1>only were we waiting to see if we were going

0:24:20.556 --> 0:24:22.036
<v Speaker 1>to get paid, but we went out there and on

0:24:22.076 --> 0:24:25.156
<v Speaker 1>the success of the vision and the mission and the

0:24:25.196 --> 0:24:28.036
<v Speaker 1>tools we built and the early results we got, we

0:24:28.076 --> 0:24:32.676
<v Speaker 1>signed up a hundred practices in five new states and

0:24:33.276 --> 0:24:36.516
<v Speaker 1>we had to make it work. So we went to

0:24:36.596 --> 0:24:40.476
<v Speaker 1>five states and fortunately in that first year with this

0:24:40.596 --> 0:24:43.876
<v Speaker 1>new batch, one out of five states the docs got paid.

0:24:44.276 --> 0:24:48.596
<v Speaker 1>If the initial problem is that, as the doctors you're

0:24:48.596 --> 0:24:52.276
<v Speaker 1>working with, our lowering admission rates to hospitals, hospitals are

0:24:52.276 --> 0:24:56.076
<v Speaker 1>effectively charging more for each admission. How do you solve that?

0:24:56.836 --> 0:24:59.516
<v Speaker 1>How do you how do you fix that? There's only

0:24:59.516 --> 0:25:01.516
<v Speaker 1>a limit to how much you can play those games,

0:25:01.676 --> 0:25:05.036
<v Speaker 1>how much the hospitals can play those games? Yeah? Yeah, yeah,

0:25:05.076 --> 0:25:07.916
<v Speaker 1>so you so you just drive down admissions even more,

0:25:08.356 --> 0:25:11.996
<v Speaker 1>even more. Yeah, And were you able to do that? Yeah? Yeah,

0:25:11.996 --> 0:25:18.116
<v Speaker 1>So we're now down seventeen percent. Hospitalizations are down seventeen

0:25:18.196 --> 0:25:23.556
<v Speaker 1>percent in for our patients compared to what they would

0:25:23.596 --> 0:25:27.476
<v Speaker 1>be if they were just like everybody else in their communities.

0:25:27.476 --> 0:25:30.596
<v Speaker 1>So that's gigantic. So you're saying, same kind of patients

0:25:30.596 --> 0:25:33.476
<v Speaker 1>you match the you know, big batch of patients, thousands

0:25:33.476 --> 0:25:37.276
<v Speaker 1>of patients, if they're working with the doctors you work with,

0:25:37.356 --> 0:25:41.956
<v Speaker 1>they're seventeen percent less likely to be admitted to the hospital. Correct?

0:25:42.276 --> 0:25:44.596
<v Speaker 1>Is that at all a function of selection bias? The

0:25:44.636 --> 0:25:47.116
<v Speaker 1>fact that docs who sign up with you already have

0:25:47.196 --> 0:25:49.796
<v Speaker 1>a lower admission rate among their patients because that's what

0:25:49.836 --> 0:25:53.036
<v Speaker 1>they're into. I mean, that's such a big effect. It is,

0:25:53.156 --> 0:25:58.276
<v Speaker 1>And this is compared to themselves, So it's it's it's

0:25:58.276 --> 0:26:03.476
<v Speaker 1>comparing our practices compared to themselves before, compared to non

0:26:04.196 --> 0:26:08.036
<v Speaker 1>practices compared to themselves before right. So it's the same docs,

0:26:08.076 --> 0:26:11.796
<v Speaker 1>the same doctors, the patients get admitted to the hospital

0:26:11.916 --> 0:26:17.156
<v Speaker 1>seventeen percent less often once they sign up with you. Then, yeah,

0:26:17.196 --> 0:26:19.516
<v Speaker 1>how how do you do that? I mean, I understand

0:26:19.556 --> 0:26:23.076
<v Speaker 1>that incentives are powerful. Is it just the money? No,

0:26:25.316 --> 0:26:30.236
<v Speaker 1>can use a computer for that. Um, it's it's actually

0:26:30.756 --> 0:26:37.076
<v Speaker 1>creating data to target the right people for the right interventions.

0:26:37.236 --> 0:26:41.716
<v Speaker 1>It's doing the basics better. So the fundamental thing that

0:26:41.756 --> 0:26:44.596
<v Speaker 1>we're doing is we're increasing more access to primary care.

0:26:44.676 --> 0:26:47.996
<v Speaker 1>So we get thirty four percent more visits to primary

0:26:48.036 --> 0:26:50.316
<v Speaker 1>care doctors for the patients in particular who need it

0:26:50.356 --> 0:26:52.396
<v Speaker 1>the most. And I just want it. There's there was

0:26:52.396 --> 0:26:54.356
<v Speaker 1>a phrase you used in there that I just want

0:26:54.356 --> 0:26:57.436
<v Speaker 1>to grab for a second. It was not just that

0:26:57.476 --> 0:26:59.316
<v Speaker 1>it's easier to see the doctor. It's easier to see

0:26:59.316 --> 0:27:01.876
<v Speaker 1>the doctor for the patients that need it the most.

0:27:02.636 --> 0:27:05.156
<v Speaker 1>That's right. It seems like that's a really important phase

0:27:05.476 --> 0:27:08.476
<v Speaker 1>is figuring out who those patients that actually need to

0:27:08.516 --> 0:27:11.356
<v Speaker 1>see their price marycare doctor or seeing them when they

0:27:11.396 --> 0:27:15.876
<v Speaker 1>need to be seen like that seems hard, Yeah, and

0:27:16.116 --> 0:27:19.596
<v Speaker 1>that is where the technology comes in. My father in

0:27:19.716 --> 0:27:25.276
<v Speaker 1>law was CEO of a health plan and before that

0:27:25.316 --> 0:27:28.156
<v Speaker 1>he had started one of the health insurance company health

0:27:28.156 --> 0:27:31.356
<v Speaker 1>insurance company, and before that he had started one of

0:27:31.476 --> 0:27:35.076
<v Speaker 1>the first HMOs in the country. And he said to me,

0:27:35.156 --> 0:27:37.156
<v Speaker 1>as far as how's this different than what we did

0:27:37.156 --> 0:27:42.276
<v Speaker 1>fifty years ago? And I showed him our our technology

0:27:42.276 --> 0:27:45.676
<v Speaker 1>and he was like, Okay, that's what's different. So how

0:27:45.716 --> 0:27:48.396
<v Speaker 1>does it actually work? So how does it work that

0:27:48.516 --> 0:27:51.716
<v Speaker 1>a doctor knows what patients they actually need to see

0:27:51.996 --> 0:27:56.476
<v Speaker 1>or need to be available for. Yeah, so nine of

0:27:56.476 --> 0:28:04.676
<v Speaker 1>our practices use our platform, our software platform every day. Yesterday,

0:28:05.276 --> 0:28:07.236
<v Speaker 1>our practices were in the tool and what they do.

0:28:07.276 --> 0:28:09.756
<v Speaker 1>When you log into the tool, the first thing you

0:28:09.796 --> 0:28:13.076
<v Speaker 1>see is you had three patients who went to the

0:28:13.116 --> 0:28:18.836
<v Speaker 1>emergency room yesterday, and we think that within forty eight

0:28:18.836 --> 0:28:22.356
<v Speaker 1>hours you should call them. And here's here's the tool

0:28:22.596 --> 0:28:24.756
<v Speaker 1>that helps you call them and you can now send

0:28:24.796 --> 0:28:27.876
<v Speaker 1>them a text message. And just to be clear, like

0:28:28.156 --> 0:28:31.276
<v Speaker 1>most doctors when their patient goes to the emergency room,

0:28:32.596 --> 0:28:36.236
<v Speaker 1>how do they find out or that they don't They don't.

0:28:36.916 --> 0:28:39.556
<v Speaker 1>They don't they don't because how would they be right?

0:28:39.636 --> 0:28:41.596
<v Speaker 1>It's just like how would the doctor is over here

0:28:41.636 --> 0:28:44.116
<v Speaker 1>in some clinic and then the patient is just driving

0:28:44.156 --> 0:28:46.276
<v Speaker 1>to the emergency room or whatever getting driven to the

0:28:46.316 --> 0:28:50.516
<v Speaker 1>emergency room, and they're like separate islands exactly, even when

0:28:50.676 --> 0:28:53.076
<v Speaker 1>even when the doctor's part of the same system as

0:28:53.076 --> 0:28:56.756
<v Speaker 1>the emergency room, there's no system to inform them, there's

0:28:56.796 --> 0:28:59.316
<v Speaker 1>no system to call the patient. There's no system for that.

0:28:59.556 --> 0:29:03.916
<v Speaker 1>Why because there's no incentive to do that. Yeah, nobody

0:29:03.916 --> 0:29:06.116
<v Speaker 1>gets paid more to if they do that, And now

0:29:06.156 --> 0:29:10.596
<v Speaker 1>there is, and now there is. Right, So that's that's

0:29:10.636 --> 0:29:12.116
<v Speaker 1>one of the things you'll see. The other thing you'll

0:29:12.116 --> 0:29:14.556
<v Speaker 1>see is you'll see it that says, hey, you know what,

0:29:15.236 --> 0:29:16.996
<v Speaker 1>here's a list of patients who you haven't seen in

0:29:17.036 --> 0:29:19.636
<v Speaker 1>a while, who you know they might need your help.

0:29:19.996 --> 0:29:23.036
<v Speaker 1>They're pretty got pretty complex conditions, or their last blood

0:29:23.036 --> 0:29:25.956
<v Speaker 1>pressure was pretty high or whatever. Right, here's a list.

0:29:25.996 --> 0:29:27.596
<v Speaker 1>But you don't need to worry about that. Here's a

0:29:27.596 --> 0:29:30.076
<v Speaker 1>list of patients for your scheduling person, the front desk

0:29:30.116 --> 0:29:33.436
<v Speaker 1>person between patients. When she's got a few minutes, she

0:29:33.436 --> 0:29:35.476
<v Speaker 1>should call these patients and make an appointment for them

0:29:35.516 --> 0:29:38.356
<v Speaker 1>to come back into primary care. Right, So that's the

0:29:38.436 --> 0:29:41.116
<v Speaker 1>wellness work Listen, when they come in once a year,

0:29:41.796 --> 0:29:45.396
<v Speaker 1>like let's have a visit. That's not based on you

0:29:45.676 --> 0:29:47.956
<v Speaker 1>reaching out to me because you've got something wrong with you.

0:29:48.196 --> 0:29:50.396
<v Speaker 1>But once a year, let's have a visit where we

0:29:50.396 --> 0:29:51.916
<v Speaker 1>can just sit and we can talk, and we can

0:29:51.956 --> 0:29:54.076
<v Speaker 1>see how do we keep you healthy? What are the

0:29:54.076 --> 0:29:56.276
<v Speaker 1>things that are going to be problems for you and

0:29:56.356 --> 0:30:00.116
<v Speaker 1>prevent them? And you've found that it's profitable under this

0:30:00.156 --> 0:30:05.356
<v Speaker 1>new system to do that. The practices make about fifty

0:30:05.356 --> 0:30:12.436
<v Speaker 1>percent more per Medicare patient who's in these models fifty

0:30:12.476 --> 0:30:16.756
<v Speaker 1>percent more, and they're they're like scrapping and begging for

0:30:16.836 --> 0:30:19.196
<v Speaker 1>like a two or three or four percent rate increase,

0:30:19.436 --> 0:30:22.476
<v Speaker 1>and they're getting a fifty percent increase through this model.

0:30:23.076 --> 0:30:26.756
<v Speaker 1>And is allidate's business model to take some chunk of

0:30:26.796 --> 0:30:32.276
<v Speaker 1>the additional Medicare pay basically that the doctors get. Is

0:30:32.316 --> 0:30:35.316
<v Speaker 1>that the model, that's the model. The model is we

0:30:35.436 --> 0:30:38.476
<v Speaker 1>only get paid when the practices get rewarded and the

0:30:38.516 --> 0:30:41.596
<v Speaker 1>government saves money. Are the practices on the hook if

0:30:41.636 --> 0:30:46.956
<v Speaker 1>their patients wind up costing medicare more than expected? Great question.

0:30:47.436 --> 0:30:51.756
<v Speaker 1>So we do start with what's called one sided models,

0:30:51.876 --> 0:30:56.516
<v Speaker 1>where it's it's it's like reward only, but then we

0:30:56.596 --> 0:31:01.236
<v Speaker 1>move pretty quickly to two sided models and we backstop

0:31:01.356 --> 0:31:04.756
<v Speaker 1>that risk for the practices. So so you take the

0:31:04.956 --> 0:31:06.876
<v Speaker 1>you're on the hook and the practice is not on

0:31:06.916 --> 0:31:09.636
<v Speaker 1>the hook. Correct. So I feel like I where you

0:31:09.676 --> 0:31:12.956
<v Speaker 1>came from and where you are now, and I'm curious

0:31:13.276 --> 0:31:17.116
<v Speaker 1>where you're going next, and in particular, you know, what

0:31:17.196 --> 0:31:19.996
<v Speaker 1>are the things you're trying to figure out that you

0:31:20.076 --> 0:31:25.316
<v Speaker 1>haven't yet cracked. We're in this in order to reduce

0:31:25.396 --> 0:31:30.476
<v Speaker 1>hospitalizations and complications and bad stuff happening to patients, and

0:31:30.516 --> 0:31:32.956
<v Speaker 1>we need to continue and there's so much every day

0:31:32.996 --> 0:31:37.076
<v Speaker 1>I see the opportunity that we're leaving behind. Tell me

0:31:37.156 --> 0:31:39.396
<v Speaker 1>more about that, I mean, and specifically when you talk

0:31:39.436 --> 0:31:42.916
<v Speaker 1>about seeing opportunities that you're leaving on the table, Like,

0:31:42.956 --> 0:31:45.076
<v Speaker 1>what's a specific example of a thing there you look

0:31:45.076 --> 0:31:46.876
<v Speaker 1>at you're like, oh my god, we can help people

0:31:46.876 --> 0:31:51.276
<v Speaker 1>and we're not doing it. What's an example of that? Kidney?

0:31:51.396 --> 0:31:57.196
<v Speaker 1>Let me talk about kidney. So when someone's kidneys fail,

0:31:58.236 --> 0:32:02.316
<v Speaker 1>it's a terrible thing, right where your blood is no

0:32:02.396 --> 0:32:08.116
<v Speaker 1>longer being cleaned. They oftentimes it happens unexpectedly and the

0:32:08.516 --> 0:32:13.356
<v Speaker 1>so called crash into needing dialysis, they get hospitalized. There's

0:32:13.396 --> 0:32:16.676
<v Speaker 1>actually a big mortality rate for that when people actually

0:32:17.116 --> 0:32:20.076
<v Speaker 1>die during that and never get a chance right to

0:32:20.116 --> 0:32:24.916
<v Speaker 1>make it to the next stage. We can predict who's

0:32:25.276 --> 0:32:31.036
<v Speaker 1>going to crash into dioss with pretty good accuracy. So

0:32:31.196 --> 0:32:34.036
<v Speaker 1>because we have all of this information on the patient

0:32:34.156 --> 0:32:38.076
<v Speaker 1>before upstream of when that happens in the earlier stages

0:32:38.516 --> 0:32:41.836
<v Speaker 1>of kidney impairment, we can do a really good job

0:32:41.956 --> 0:32:46.116
<v Speaker 1>using machine learning AI models of predicting who's going to

0:32:46.116 --> 0:32:49.996
<v Speaker 1>crash into diosis. We can steer those patients because we

0:32:50.036 --> 0:32:52.716
<v Speaker 1>have the primary care relationship with them. We can do

0:32:52.796 --> 0:32:58.316
<v Speaker 1>a handoff from primary care to kidney specialists and kidney

0:32:58.436 --> 0:33:01.756
<v Speaker 1>educators and work with the patients. And what we're seeing

0:33:01.876 --> 0:33:05.356
<v Speaker 1>is at twenty four percent lower rate of hospitalizations twenty

0:33:05.396 --> 0:33:08.196
<v Speaker 1>four percent for patients who've got that handoff into the

0:33:08.276 --> 0:33:12.076
<v Speaker 1>kidney care management program to dialysis. And when that happens,

0:33:12.116 --> 0:33:16.316
<v Speaker 1>presumably hospitalization goes down. Does mortality even go down? People

0:33:16.436 --> 0:33:19.276
<v Speaker 1>die less if you do this. I haven't proven that yet,

0:33:19.316 --> 0:33:21.676
<v Speaker 1>but I think it does well. So are you doing

0:33:21.716 --> 0:33:23.196
<v Speaker 1>that already or is that a thing you think you

0:33:23.236 --> 0:33:26.276
<v Speaker 1>can do but that you're not quite doing yet. So

0:33:26.316 --> 0:33:30.476
<v Speaker 1>we have a unit that does these tests, these experiments,

0:33:30.636 --> 0:33:34.916
<v Speaker 1>and then when they work, we scale them. So this

0:33:35.036 --> 0:33:37.636
<v Speaker 1>is literally hot off the presses. We got the results

0:33:37.636 --> 0:33:39.676
<v Speaker 1>from the pilot. It seems promising, and we're going to

0:33:39.716 --> 0:33:42.876
<v Speaker 1>scale it and you can imagine like you can imagine

0:33:42.956 --> 0:33:46.236
<v Speaker 1>that same sort of rinse and repeat right for a

0:33:46.276 --> 0:33:50.556
<v Speaker 1>whole host of other of other things of just finding

0:33:50.596 --> 0:33:53.436
<v Speaker 1>ways to be smarter about figuring out who is about

0:33:53.516 --> 0:33:57.956
<v Speaker 1>to get really sick essentially, that's right exactly, and intervening

0:33:58.036 --> 0:34:00.716
<v Speaker 1>either ideally to prevent them from getting really sick or

0:34:00.756 --> 0:34:04.476
<v Speaker 1>at least to reduce the bad outcomes associated with getting

0:34:04.476 --> 0:34:10.236
<v Speaker 1>really sick. You got it. We'll be back in a

0:34:10.276 --> 0:34:21.436
<v Speaker 1>minute with the lightning round. Now back to the show. Okay,

0:34:22.276 --> 0:34:28.876
<v Speaker 1>let's close with the lightning round. Okay, Um, what you

0:34:28.996 --> 0:34:32.356
<v Speaker 1>ran health? I t for the Obama administration. So what

0:34:32.556 --> 0:34:36.676
<v Speaker 1>is the least glamorous thing about working in the White House? Um?

0:34:37.236 --> 0:34:41.516
<v Speaker 1>We had to pay for our own water like literally,

0:34:41.716 --> 0:34:44.956
<v Speaker 1>what's yeah? Why? Literally we had a we had because

0:34:44.996 --> 0:34:48.396
<v Speaker 1>the government is I mean, you really learn frugality. People

0:34:48.436 --> 0:34:51.356
<v Speaker 1>have no idea Like in the government, we had a

0:34:51.396 --> 0:34:53.756
<v Speaker 1>water club and you had to put in twenty bucks

0:34:54.356 --> 0:34:57.556
<v Speaker 1>for to have the privilege of drinking from you know,

0:34:57.596 --> 0:35:01.156
<v Speaker 1>the filtered water from from the water cooler. Uh, that

0:35:01.356 --> 0:35:05.556
<v Speaker 1>is what you're That is the spirit of frugality that

0:35:05.596 --> 0:35:09.356
<v Speaker 1>people don't understand. The government agencies have. What's why? Piece

0:35:09.396 --> 0:35:11.196
<v Speaker 1>of advice you'd give to someone trying to solve a

0:35:11.196 --> 0:35:18.236
<v Speaker 1>hard problem. Spend a lot of time asking yourself, what's

0:35:18.276 --> 0:35:23.116
<v Speaker 1>the question? What's the what's the heart of the problem? Um,

0:35:23.116 --> 0:35:24.756
<v Speaker 1>and I think you should change the name of your

0:35:24.756 --> 0:35:28.316
<v Speaker 1>podcast to what's part of the problem? What do you

0:35:28.316 --> 0:35:31.316
<v Speaker 1>think I should change it to to to what's the

0:35:31.316 --> 0:35:35.516
<v Speaker 1>heart of the problem? Huh, you don't like it? I

0:35:35.756 --> 0:35:38.636
<v Speaker 1>like I wonder about the problem framing at all, frankly

0:35:38.876 --> 0:35:41.396
<v Speaker 1>as a as a as a name for the podcast

0:35:41.436 --> 0:35:44.596
<v Speaker 1>I've come. I love it. I love it. I love it.

0:35:44.836 --> 0:35:46.876
<v Speaker 1>That's why. That's that's that's why I love your podcast.

0:35:47.556 --> 0:35:50.796
<v Speaker 1>I think it's the most important thing is to be

0:35:50.836 --> 0:35:54.996
<v Speaker 1>working on the most important thing, and and we don't.

0:35:55.036 --> 0:35:57.596
<v Speaker 1>We don't work on the most important thing. It goes

0:35:57.636 --> 0:36:00.156
<v Speaker 1>back to that question you were talking about at the beginning,

0:36:00.156 --> 0:36:02.956
<v Speaker 1>how can we save the most lives? That is that's

0:36:02.996 --> 0:36:05.916
<v Speaker 1>my question, right, that's the that's that's that's the problem

0:36:05.956 --> 0:36:08.556
<v Speaker 1>that I'm trying to solve. But for every every entrepreneur,

0:36:09.356 --> 0:36:11.996
<v Speaker 1>what's the heart of the problem you're trying to solve

0:36:12.076 --> 0:36:15.516
<v Speaker 1>and how can you how can you get to that?

0:36:15.796 --> 0:36:20.356
<v Speaker 1>And what I found is people don't really don't really

0:36:20.556 --> 0:36:23.996
<v Speaker 1>ask themselves and iterate enough on do I have it right?

0:36:24.116 --> 0:36:26.116
<v Speaker 1>Is that really the heart of the problem is that

0:36:26.156 --> 0:36:31.916
<v Speaker 1>the question what was the last hike you went on? Ah?

0:36:31.316 --> 0:36:38.236
<v Speaker 1>I love hiking, and I walk on the Capitol Crescent

0:36:38.276 --> 0:36:41.476
<v Speaker 1>Trail and the Ciano Canal all day. Like some days

0:36:41.516 --> 0:36:45.756
<v Speaker 1>I'll walk eight hours during the workday with my phone

0:36:45.796 --> 0:36:50.036
<v Speaker 1>and my tablet and my backup battery. So the last

0:36:50.316 --> 0:36:52.316
<v Speaker 1>hike I did, was it a long hike on the

0:36:52.516 --> 0:36:56.116
<v Speaker 1>Ciano Canal towpath? And was it, in fact a workday

0:36:56.116 --> 0:36:59.076
<v Speaker 1>where you just walk in for eight hours and running

0:36:59.116 --> 0:37:03.276
<v Speaker 1>your company? Yeah, that's a nice move. That's a very

0:37:03.356 --> 0:37:06.556
<v Speaker 1>strong move. If everything goes well, What problem will you

0:37:06.596 --> 0:37:12.836
<v Speaker 1>be trying to solve in five years hospitals? I think

0:37:12.836 --> 0:37:18.036
<v Speaker 1>we've we've started where it's easier, where the incentive alignment

0:37:18.116 --> 0:37:21.876
<v Speaker 1>is easier with independent primary care, and now we're adding

0:37:21.916 --> 0:37:26.156
<v Speaker 1>on people around that. But particularly in rural areas, we're

0:37:26.156 --> 0:37:29.476
<v Speaker 1>going to need to solve the rural hospital problem and

0:37:29.636 --> 0:37:32.476
<v Speaker 1>maybe maybe maybe we can help flip them to a

0:37:32.516 --> 0:37:40.316
<v Speaker 1>different model. Farzad Mosta Shari is the co founder and

0:37:40.436 --> 0:37:44.796
<v Speaker 1>CEO of Allidate. Today's show was produced by Edith Russlo,

0:37:45.036 --> 0:37:48.596
<v Speaker 1>edited by Robert Smith, and engineered by Amanda k Wong.

0:37:49.476 --> 0:37:51.716
<v Speaker 1>I'd love to get your suggestions for who else I

0:37:51.716 --> 0:37:54.436
<v Speaker 1>should talk to for the show. You can email us

0:37:54.516 --> 0:37:57.876
<v Speaker 1>at problem at Pushkin dot fm, or you can find

0:37:57.876 --> 0:38:01.556
<v Speaker 1>me at Twitter at Jacob Goldstein. I am Jacob Goldstein

0:38:01.636 --> 0:38:04.196
<v Speaker 1>and we will be back next week with another episode

0:38:04.236 --> 0:38:09.876
<v Speaker 1>of What's Your Problem.