1 00:00:15,316 --> 00:00:24,316 Speaker 1: Pushkin. Think of everything everybody in the United States buys 2 00:00:24,636 --> 00:00:31,276 Speaker 1: in a year, all the food, all the clothes, insurance, education, housing, travel, iPhones, 3 00:00:31,916 --> 00:00:36,396 Speaker 1: everything of all the stuff we buy. Out of all 4 00:00:36,436 --> 00:00:40,996 Speaker 1: the money we spend, around twenty percent, around one in 5 00:00:41,236 --> 00:00:46,516 Speaker 1: five dollars, is spent on healthcare. This is extraordinary. It 6 00:00:46,676 --> 00:00:49,836 Speaker 1: is completely out of line with what other people in 7 00:00:49,916 --> 00:00:52,836 Speaker 1: other countries spend. It's about twice as much as what 8 00:00:52,956 --> 00:00:56,396 Speaker 1: other rich countries spend relative to their GDPs on healthcare. 9 00:00:56,876 --> 00:01:00,436 Speaker 1: And crucially, we in the US do not get better 10 00:01:00,516 --> 00:01:04,836 Speaker 1: health outcomes, So we're spending lots of money without much 11 00:01:04,876 --> 00:01:08,316 Speaker 1: to show for it. For a long time, people have 12 00:01:08,396 --> 00:01:10,836 Speaker 1: looked at this problem and thought, what if instead of 13 00:01:10,876 --> 00:01:13,836 Speaker 1: paying doctors and hospitals to treat us when we get sick, 14 00:01:14,276 --> 00:01:17,596 Speaker 1: we could pay them to keep us healthy. Is there 15 00:01:17,676 --> 00:01:21,196 Speaker 1: some way we could spend less on healthcare and get 16 00:01:21,236 --> 00:01:29,276 Speaker 1: better results. I'm Jacob Goldstein, and this is what's your problem. 17 00:01:29,276 --> 00:01:32,356 Speaker 1: My guest today is Farzad Mosta Shari. He's the co 18 00:01:32,516 --> 00:01:36,956 Speaker 1: founder and CEO of a healthcare company called Alde. Farzod's 19 00:01:36,956 --> 00:01:39,956 Speaker 1: problem is this, how can you pay doctors to keep 20 00:01:40,036 --> 00:01:43,036 Speaker 1: us healthy rather than treating us after we get sick. 21 00:01:44,236 --> 00:01:46,796 Speaker 1: People have been struggling to solve this problem for decades, 22 00:01:47,156 --> 00:01:49,196 Speaker 1: but for a bunch of reasons you'll hear about on 23 00:01:49,196 --> 00:01:52,316 Speaker 1: the show, Farzad and his fellow travelers may be the 24 00:01:52,356 --> 00:01:58,276 Speaker 1: ones to finally solve them. Before he became an entrepreneur, 25 00:01:58,316 --> 00:02:01,476 Speaker 1: Farzad went to medical school, but he told me that 26 00:02:01,716 --> 00:02:03,956 Speaker 1: back in the mid nineties, when he was doing his 27 00:02:04,036 --> 00:02:08,036 Speaker 1: clinical training, his residency and internal medicine, he started to 28 00:02:08,116 --> 00:02:10,996 Speaker 1: question what he was and what he was not learning. 29 00:02:11,756 --> 00:02:15,836 Speaker 1: I kept looking at stuff that was happening and being like, 30 00:02:15,876 --> 00:02:19,236 Speaker 1: this doesn't make sense to me, Like why, like you're 31 00:02:19,276 --> 00:02:22,076 Speaker 1: telling you're teaching me how to you know, treat someone 32 00:02:22,116 --> 00:02:24,876 Speaker 1: who's coming in here with difficulty breeding in the emergency room. 33 00:02:24,916 --> 00:02:26,996 Speaker 1: I get that, Like it's really important that that person 34 00:02:27,076 --> 00:02:30,036 Speaker 1: get their treatment, their nebulizer so they can breathe again, 35 00:02:30,356 --> 00:02:33,796 Speaker 1: that's really I get that. But why is this person 36 00:02:33,916 --> 00:02:37,836 Speaker 1: coming in today? Why is it all of a sudden 37 00:02:38,076 --> 00:02:42,036 Speaker 1: we're seeing people coming in with breeding problems. Why why 38 00:02:42,116 --> 00:02:43,796 Speaker 1: is it that the people who tend to come in 39 00:02:44,076 --> 00:02:46,596 Speaker 1: with breeding problems tend to be from these neighborhoods, not 40 00:02:46,636 --> 00:02:51,956 Speaker 1: other neighborhoods. And it was those questions that were between 41 00:02:52,356 --> 00:02:55,956 Speaker 1: medicine and public health and ibidemology that really grabbed me 42 00:02:56,796 --> 00:02:59,236 Speaker 1: so far as Odd finished his residency and went into 43 00:02:59,236 --> 00:03:02,756 Speaker 1: public health, and he had this this trait that at 44 00:03:02,756 --> 00:03:05,676 Speaker 1: the time was kind of a superpower. He was really 45 00:03:05,716 --> 00:03:09,836 Speaker 1: into computers. So I came of age at a time 46 00:03:09,836 --> 00:03:14,276 Speaker 1: when in a way, you know, asking the question can 47 00:03:14,436 --> 00:03:20,076 Speaker 1: you use a computer for that was was like, uh, 48 00:03:20,436 --> 00:03:23,076 Speaker 1: you know, unlocked a lot of a lot of value. 49 00:03:23,316 --> 00:03:25,436 Speaker 1: And so I joined the New York City Health Department 50 00:03:25,436 --> 00:03:28,396 Speaker 1: and I said, oh, public health surveillance, can we use 51 00:03:28,396 --> 00:03:33,196 Speaker 1: a computer for that? Right? So one of my first startup, Jacob, 52 00:03:33,676 --> 00:03:36,316 Speaker 1: was a within the New York City Department of Health 53 00:03:36,436 --> 00:03:38,956 Speaker 1: where I started. Was one of the group of people 54 00:03:38,956 --> 00:03:43,476 Speaker 1: who started this national idea of why don't we why 55 00:03:43,476 --> 00:03:46,956 Speaker 1: don't we track when when someone gets logged into the 56 00:03:46,996 --> 00:03:50,476 Speaker 1: emergency room for just for their registering the patient in 57 00:03:50,516 --> 00:03:53,636 Speaker 1: the emergency room, or when an ambulance gets dispatched, or 58 00:03:53,676 --> 00:03:56,636 Speaker 1: when when when some item in the pharmacy goes beep 59 00:03:56,716 --> 00:04:00,596 Speaker 1: across the scanner. Like before all those things were on paper. 60 00:04:00,916 --> 00:04:03,316 Speaker 1: Now they're all electronic, which means I can get a 61 00:04:03,316 --> 00:04:06,196 Speaker 1: copy and if I can get a copy, then I 62 00:04:06,236 --> 00:04:10,516 Speaker 1: can tell you in real time that something's happening to 63 00:04:10,556 --> 00:04:14,076 Speaker 1: the health of the city. Right that was That was 64 00:04:14,076 --> 00:04:17,276 Speaker 1: like consumed me for five to ten years, and it 65 00:04:17,316 --> 00:04:19,756 Speaker 1: was really really cool. It was really really fun, and 66 00:04:19,836 --> 00:04:23,596 Speaker 1: that system TILT today is the system that we use, 67 00:04:23,676 --> 00:04:27,196 Speaker 1: for example, during COVID to see that what was happening 68 00:04:27,196 --> 00:04:31,116 Speaker 1: in the city. But then something fundamentally shifted in my life, 69 00:04:31,556 --> 00:04:35,876 Speaker 1: which was Mike Bloomberg asked the Commissioner of Health at 70 00:04:35,876 --> 00:04:39,196 Speaker 1: the time, who turned to me. I was like the 71 00:04:39,276 --> 00:04:43,996 Speaker 1: data guy, and the question he asked was, don't worry 72 00:04:44,036 --> 00:04:46,636 Speaker 1: about the politics, don't worry about the budget, don't worry 73 00:04:46,636 --> 00:04:49,156 Speaker 1: about any of that. Your job is to answer the question. 74 00:04:49,276 --> 00:04:52,716 Speaker 1: What's the question? And the question was what's the problem? 75 00:04:52,756 --> 00:04:57,636 Speaker 1: What's your problem? As you might say, the question was, hey, 76 00:04:57,756 --> 00:05:01,396 Speaker 1: how do we save the most lives? And I was 77 00:05:01,436 --> 00:05:08,716 Speaker 1: like can I swear? Oh yeah? Fuck? That was like 78 00:05:09,356 --> 00:05:13,476 Speaker 1: what am I doing? Like? What am I doing? Why 79 00:05:13,476 --> 00:05:17,796 Speaker 1: am I not starting with that question? That is such 80 00:05:17,876 --> 00:05:20,196 Speaker 1: the right question, how do we save the most lives? 81 00:05:20,676 --> 00:05:24,716 Speaker 1: And it turned out that no one in like, we're 82 00:05:24,756 --> 00:05:28,316 Speaker 1: spending three four trillion dollars, We're doing all this activity right, 83 00:05:28,556 --> 00:05:31,476 Speaker 1: and no one was starting from that viewpoint. There were 84 00:05:31,556 --> 00:05:35,796 Speaker 1: hundreds of thousands of research articles written about various things 85 00:05:35,836 --> 00:05:39,396 Speaker 1: that we're doing, and no one was saying, what is 86 00:05:39,436 --> 00:05:43,276 Speaker 1: this the scientific, evidence based answer to the question how 87 00:05:43,316 --> 00:05:45,116 Speaker 1: do we save the most lives? And it turned out, 88 00:05:45,116 --> 00:05:47,036 Speaker 1: in New York City in the year of Our Lord 89 00:05:47,076 --> 00:05:49,516 Speaker 1: two thousand and two, the answer to that was smoking. 90 00:05:50,756 --> 00:05:53,436 Speaker 1: The answer to that was we're going to attack smoking. 91 00:05:53,916 --> 00:05:57,756 Speaker 1: So that meant that we were going to dedicate more 92 00:05:57,756 --> 00:06:01,356 Speaker 1: of our living waking hours to how do we reduce 93 00:06:01,396 --> 00:06:03,596 Speaker 1: smoking in New York City than we do to anything else. 94 00:06:04,516 --> 00:06:08,596 Speaker 1: And within the time span of four years, we drove 95 00:06:09,276 --> 00:06:11,796 Speaker 1: I was a small part of a big effort to 96 00:06:11,876 --> 00:06:14,556 Speaker 1: drive teen smoking from fourteen to seven percent in New 97 00:06:14,636 --> 00:06:17,596 Speaker 1: York City, to drive adult smoking from twenty two point 98 00:06:17,636 --> 00:06:21,236 Speaker 1: eight percent to sixteen point eight percent. That saved more 99 00:06:21,276 --> 00:06:24,756 Speaker 1: lives than anything else we could have possibly done. And 100 00:06:24,796 --> 00:06:29,316 Speaker 1: then the next question was, Okay, that's public health. That's great, 101 00:06:29,596 --> 00:06:32,716 Speaker 1: that's changing the environment within which people make decisions. But 102 00:06:32,796 --> 00:06:34,716 Speaker 1: we know what we're spending all this money on healthcare? 103 00:06:35,076 --> 00:06:38,236 Speaker 1: What can healthcare do to save the most lives? Just 104 00:06:38,476 --> 00:06:41,796 Speaker 1: stay with that question. That's such a clarifying question that is, 105 00:06:41,876 --> 00:06:47,156 Speaker 1: so that is my flaming sword, right, It's like, how 106 00:06:47,196 --> 00:06:50,716 Speaker 1: can we save how can medicine? How can healthcare save 107 00:06:50,756 --> 00:06:54,516 Speaker 1: the most lives? And again no one had the answer. 108 00:06:54,596 --> 00:06:58,636 Speaker 1: So we published this paper, Randy analysis. Do you want 109 00:06:58,676 --> 00:07:01,996 Speaker 1: to take a guess, Jacob, as to what thing in healthcare? 110 00:07:02,236 --> 00:07:05,556 Speaker 1: If we you know, we have thousands of evidence based 111 00:07:06,596 --> 00:07:10,116 Speaker 1: guidelines and protocols and quality measures, and like, what is 112 00:07:10,116 --> 00:07:13,116 Speaker 1: the one thing? And then phrase the question, well that 113 00:07:13,196 --> 00:07:15,836 Speaker 1: if you took the performance of the American healthcare system 114 00:07:15,876 --> 00:07:18,036 Speaker 1: from where it is today to where the best American 115 00:07:18,076 --> 00:07:22,436 Speaker 1: healthcare institutions can perform, would reduce the number of premature 116 00:07:22,476 --> 00:07:25,276 Speaker 1: deaths by the greatest amount. Do not have any idea 117 00:07:25,356 --> 00:07:29,956 Speaker 1: what that would be? Based on my preparation for this interview, 118 00:07:30,316 --> 00:07:35,316 Speaker 1: I'm gonna guess lowering people's blood pressure. Yeah all right, 119 00:07:35,356 --> 00:07:39,836 Speaker 1: see that's not fair because you've been listening. I did 120 00:07:39,876 --> 00:07:44,436 Speaker 1: my homework. Homework, you did your homework. But the funny 121 00:07:44,476 --> 00:07:47,236 Speaker 1: thing is that if you ask that question of you know, 122 00:07:47,436 --> 00:07:51,756 Speaker 1: hospital CEOs, they'll say, like getting stroke patients to the 123 00:07:51,796 --> 00:07:55,316 Speaker 1: hospital ten minutes faster something. Yeah. Yeah, they'll say, like, 124 00:07:55,396 --> 00:07:59,036 Speaker 1: let's not kill people in the hospital through infections, they'll say, right, well, 125 00:07:59,036 --> 00:08:02,796 Speaker 1: because it's not really traditionally a hospital CEO's job to 126 00:08:03,316 --> 00:08:05,276 Speaker 1: keep people out of the hospital, which is what you 127 00:08:05,276 --> 00:08:06,956 Speaker 1: want to do if you want people not to die, 128 00:08:07,116 --> 00:08:09,036 Speaker 1: right exactly. But even if you if you talk to 129 00:08:09,156 --> 00:08:12,756 Speaker 1: health planned people that they don't know right there, if 130 00:08:12,796 --> 00:08:15,316 Speaker 1: you if you look at the number of quality measures 131 00:08:15,356 --> 00:08:19,996 Speaker 1: that doctors are incentivized and measured against, right, like, there's 132 00:08:20,036 --> 00:08:23,076 Speaker 1: not a there's not a ten x difference between the 133 00:08:23,036 --> 00:08:25,636 Speaker 1: amount of importance placed on blood pressure control. Then there 134 00:08:25,716 --> 00:08:29,236 Speaker 1: is the amount of importance placed on mamograms and colonoscopies 135 00:08:29,236 --> 00:08:31,436 Speaker 1: and flu vaccines and all that other stuff. Right, it's 136 00:08:31,436 --> 00:08:33,596 Speaker 1: all good stuff. And you're saying the evidence as the 137 00:08:33,676 --> 00:08:36,556 Speaker 1: efficacy of lowering blood pressure means there should be a 138 00:08:36,596 --> 00:08:39,636 Speaker 1: ten x difference between blood pressure and right, it's just 139 00:08:39,796 --> 00:08:43,316 Speaker 1: gigantic and it's sable, right like, And it's just science 140 00:08:43,436 --> 00:08:46,756 Speaker 1: of blow Like we know there's cheap, cheap, safe drugs, 141 00:08:46,836 --> 00:08:50,036 Speaker 1: there's life like, we know how to lower people's blood pressure. 142 00:08:50,356 --> 00:08:53,556 Speaker 1: And and then that was the that became my problem. Right, 143 00:08:53,596 --> 00:08:56,276 Speaker 1: It's like, well, why aren't we why aren't we why 144 00:08:56,316 --> 00:09:00,436 Speaker 1: aren't we controlling blood pressure, and I came to the 145 00:09:00,476 --> 00:09:04,236 Speaker 1: wrong answer. And the reason I came to the wrong 146 00:09:04,276 --> 00:09:07,516 Speaker 1: answer was because I tended to be the kind of 147 00:09:07,556 --> 00:09:11,476 Speaker 1: person who said, can use a computer for that. You 148 00:09:11,516 --> 00:09:15,196 Speaker 1: saw the computer as it was, It was your hammer. 149 00:09:15,236 --> 00:09:18,156 Speaker 1: So every problem looked like a nail that could be 150 00:09:18,236 --> 00:09:21,316 Speaker 1: hit with the computer. And look, there was some truth 151 00:09:21,356 --> 00:09:24,396 Speaker 1: to it, right, Like the truth to it was, you 152 00:09:24,436 --> 00:09:30,596 Speaker 1: can't improve blood pressure control if you can't even know 153 00:09:30,676 --> 00:09:33,996 Speaker 1: what blood pressure control rates are, right, Because you actually 154 00:09:34,156 --> 00:09:36,996 Speaker 1: couldn't tell what blood pressure control rates were in any 155 00:09:37,036 --> 00:09:42,676 Speaker 1: given practice, any given population, because the data was trapped 156 00:09:42,916 --> 00:09:45,716 Speaker 1: in deadwood that there were you would write it down, 157 00:09:45,716 --> 00:09:47,836 Speaker 1: You would write down the person's bloodressure control. How would 158 00:09:47,836 --> 00:09:50,316 Speaker 1: I even know, if I'm a doctor what percent of 159 00:09:50,356 --> 00:09:53,636 Speaker 1: my patients had their blood pressure control? I literally couldn't, 160 00:09:53,676 --> 00:09:55,876 Speaker 1: with a room full of paper charts, answer that question. 161 00:09:56,276 --> 00:09:59,716 Speaker 1: I couldn't have quality improvement. I couldn't have decisions support 162 00:09:59,756 --> 00:10:02,556 Speaker 1: that would prompt me when I'm seeing a patient. I 163 00:10:02,556 --> 00:10:04,596 Speaker 1: couldn't have a registry that made a list of the 164 00:10:04,596 --> 00:10:08,596 Speaker 1: patients to say, can you use a computer for that? Right? 165 00:10:08,636 --> 00:10:13,676 Speaker 1: As your friend that problem with the computer, yeah, so 166 00:10:13,156 --> 00:10:16,356 Speaker 1: then so then we and we had a big concern 167 00:10:16,396 --> 00:10:19,276 Speaker 1: around around what was now called health equity. So we 168 00:10:19,316 --> 00:10:24,116 Speaker 1: went around to the doctors in New York City's poorest neighborhoods, 169 00:10:24,516 --> 00:10:29,756 Speaker 1: in Harlem, in the South Bronx, in Central Brooklyn, bed Stye, 170 00:10:30,156 --> 00:10:32,436 Speaker 1: and we said, hey, guess what you've been Medicine has 171 00:10:32,476 --> 00:10:35,676 Speaker 1: been using paper and pen for thousands of years. We're 172 00:10:35,716 --> 00:10:38,196 Speaker 1: going to give you a free electronic health record with 173 00:10:38,356 --> 00:10:40,716 Speaker 1: all of these functionalities built into it. And we spent 174 00:10:40,716 --> 00:10:43,076 Speaker 1: the next year and a half building into the h 175 00:10:43,156 --> 00:10:46,716 Speaker 1: ARE the functionality electronic health record, the functionality that we 176 00:10:46,796 --> 00:10:51,076 Speaker 1: thought should exist in those electronic health records, and then 177 00:10:51,196 --> 00:10:53,556 Speaker 1: we did that. We rolled it out to half of 178 00:10:53,596 --> 00:10:56,996 Speaker 1: all those primary care docs in New York cities poorest neighborhoods. 179 00:10:57,036 --> 00:11:01,116 Speaker 1: So mission accomplished. But you know, you want to guess 180 00:11:01,156 --> 00:11:05,436 Speaker 1: what happened to blood pressure control rates? They did not improve. 181 00:11:06,876 --> 00:11:09,876 Speaker 1: They did not improve, And the number of strokes and 182 00:11:09,956 --> 00:11:12,036 Speaker 1: the number of heart attacks and the number of kidney 183 00:11:12,036 --> 00:11:16,236 Speaker 1: failures from that continued just as much. And so that 184 00:11:16,476 --> 00:11:20,196 Speaker 1: was my continued frustrating He said, what was your frustration. 185 00:11:20,556 --> 00:11:26,276 Speaker 1: That's my frustration is, um, I didn't feel like I 186 00:11:26,316 --> 00:11:29,716 Speaker 1: felt like we'd done all this work, and at the 187 00:11:29,836 --> 00:11:31,836 Speaker 1: end of the day, the reason we'd done all that 188 00:11:31,916 --> 00:11:36,956 Speaker 1: work hadn't hadn't been accomplished. And I reluctantly, you know, 189 00:11:36,996 --> 00:11:39,436 Speaker 1: for twenty years, Jacob, I had like I was a 190 00:11:39,436 --> 00:11:43,436 Speaker 1: public health guy, I was a government person, like like 191 00:11:43,716 --> 00:11:46,876 Speaker 1: I was not a private sector person. And in fact, 192 00:11:46,996 --> 00:11:50,196 Speaker 1: I kind of thought that the profit motive was the 193 00:11:50,276 --> 00:11:56,116 Speaker 1: problem in many cases. And that's right, and it's wrong. 194 00:11:56,796 --> 00:12:02,516 Speaker 1: And what I reluctantly came to see is that the 195 00:12:02,596 --> 00:12:08,236 Speaker 1: financial incentives are the water we swimming and and and 196 00:12:08,316 --> 00:12:11,476 Speaker 1: that sometimes we and even see it likes it shapes 197 00:12:12,276 --> 00:12:16,116 Speaker 1: the force of everything around us, the gravity that pulls 198 00:12:16,156 --> 00:12:20,396 Speaker 1: on us or not right, like everything around us is 199 00:12:20,476 --> 00:12:23,716 Speaker 1: there's this invisible thing which is the financial incentives of 200 00:12:23,756 --> 00:12:27,556 Speaker 1: the system. And I was trying to ignore them. I 201 00:12:27,596 --> 00:12:30,916 Speaker 1: was trying to pretend like they didn't exist. And in fact, 202 00:12:31,276 --> 00:12:36,396 Speaker 1: the financial incentives of the healthcare system are to treat strokes. 203 00:12:37,276 --> 00:12:39,876 Speaker 1: They are not to No one makes billions of dollars 204 00:12:40,156 --> 00:12:45,636 Speaker 1: preventing strokes. No one makes you make money waiting until 205 00:12:45,836 --> 00:12:51,116 Speaker 1: literally waiting until someone's kidney fails, and then there's billions 206 00:12:51,116 --> 00:12:55,996 Speaker 1: of dollars available to you. For treatment of like taking 207 00:12:56,036 --> 00:12:58,276 Speaker 1: someone's blood out of their body, running it through a 208 00:12:58,316 --> 00:13:00,636 Speaker 1: machine and returning it to them. There's a lot of 209 00:13:00,676 --> 00:13:04,316 Speaker 1: money in that, not needing that. There's not a lot 210 00:13:04,316 --> 00:13:08,876 Speaker 1: of money doing that. You're talking about strokes and kidney 211 00:13:08,876 --> 00:13:14,156 Speaker 1: fail because we know that lowering blood pressure, which is 212 00:13:14,996 --> 00:13:18,756 Speaker 1: relatively cheap to do, lowers the risk of strokes and 213 00:13:18,876 --> 00:13:21,796 Speaker 1: kidney failure. But you're saying we don't lower blood pressure, 214 00:13:21,836 --> 00:13:27,236 Speaker 1: and that happens because that's literally the money in healthcare flows, 215 00:13:27,436 --> 00:13:31,636 Speaker 1: because doctors and hospitals get money for treating strokes and 216 00:13:32,076 --> 00:13:35,796 Speaker 1: kidney failure and not for lowering blood pressure. Yeah. I 217 00:13:35,836 --> 00:13:39,636 Speaker 1: mean there's one the one part of the healthcare system 218 00:13:39,676 --> 00:13:42,196 Speaker 1: that is supposed to be in charge of the controlling 219 00:13:42,236 --> 00:13:44,636 Speaker 1: the blood pressure, which is primary care. Right, it's the 220 00:13:44,836 --> 00:13:48,676 Speaker 1: primary care part. And guess what part of our healthcare 221 00:13:48,756 --> 00:13:52,356 Speaker 1: system makes the least money, makes the you know, has 222 00:13:52,396 --> 00:13:56,836 Speaker 1: the least prestige in in where I trained, right, it's 223 00:13:56,956 --> 00:13:59,676 Speaker 1: it's Oh, those those people who you know they talk 224 00:13:59,716 --> 00:14:01,996 Speaker 1: to patients, they don't they don't you know, they don't 225 00:14:01,996 --> 00:14:04,796 Speaker 1: cut the patients, they don't do procedures on them, they 226 00:14:04,796 --> 00:14:08,076 Speaker 1: don't do expensive scans on them, right, They sit in 227 00:14:08,116 --> 00:14:10,316 Speaker 1: a little room and they talk to the patient about 228 00:14:10,356 --> 00:14:13,476 Speaker 1: why they should take their medicine. Right. Oh, those those 229 00:14:13,636 --> 00:14:17,476 Speaker 1: those poor, those poor little primary care doctors. We spend 230 00:14:17,516 --> 00:14:21,156 Speaker 1: five percent of healthcare costs on primary care, five percent. 231 00:14:21,316 --> 00:14:23,396 Speaker 1: We spend as much on ICUs as we do on 232 00:14:23,396 --> 00:14:28,316 Speaker 1: primary and even primary care doctors traditionally do not get 233 00:14:28,356 --> 00:14:31,556 Speaker 1: more money when their patients stay healthy. Right. And I 234 00:14:31,636 --> 00:14:36,116 Speaker 1: know primary care doctors are not motivated by money explicitly, 235 00:14:36,276 --> 00:14:40,796 Speaker 1: but but still the incentives don't push primary care doctors 236 00:14:40,836 --> 00:14:44,196 Speaker 1: to reduce their patient's blood pressure though they I'm sure 237 00:14:44,276 --> 00:14:47,396 Speaker 1: they want to, they don't get paid more if they do. Traditionally, 238 00:14:49,236 --> 00:14:55,396 Speaker 1: until twenty twelve, big moment, big narrative moment happening, big moment, 239 00:14:59,876 --> 00:15:04,316 Speaker 1: say healthcare economics, Cliffhanger. We'll get to that big moment 240 00:15:04,596 --> 00:15:07,076 Speaker 1: and to how it led Farza to start his company 241 00:15:07,556 --> 00:15:15,476 Speaker 1: in just a minute. Okay, now back to the show 242 00:15:15,636 --> 00:15:19,596 Speaker 1: to find out what exactly happened in twenty twelve. In 243 00:15:19,636 --> 00:15:25,916 Speaker 1: twenty twelves, the rules for accountable care are published by 244 00:15:26,116 --> 00:15:28,916 Speaker 1: the Center for Medicare and Medicaid Services. That basically says, 245 00:15:29,396 --> 00:15:31,956 Speaker 1: if you get a group of primary care dots together, 246 00:15:32,756 --> 00:15:37,196 Speaker 1: and you gain accountability for the total cost of care 247 00:15:37,356 --> 00:15:40,516 Speaker 1: for the patients they care for. If those costs come down, 248 00:15:41,396 --> 00:15:45,596 Speaker 1: we will give you half of that those savings. And 249 00:15:45,676 --> 00:15:48,836 Speaker 1: it was like it was like the heavens had opened up, right, 250 00:15:49,196 --> 00:15:51,356 Speaker 1: And I was like, oh my god. And I was like, 251 00:15:51,876 --> 00:15:54,996 Speaker 1: primary care docs don't realize the power they have in 252 00:15:55,036 --> 00:16:00,756 Speaker 1: this new world. And the only problem, right was that 253 00:16:00,796 --> 00:16:05,636 Speaker 1: if you're if you're an independent, small primary care practice, 254 00:16:06,476 --> 00:16:10,476 Speaker 1: you don't really have the scale to to band together 255 00:16:10,556 --> 00:16:13,196 Speaker 1: with one hundred other groups to pool your patients, to 256 00:16:13,316 --> 00:16:16,876 Speaker 1: take that actuarial risk, to understand the regulations, to have 257 00:16:16,916 --> 00:16:19,556 Speaker 1: the data systems to know what the playbook is to 258 00:16:19,636 --> 00:16:23,196 Speaker 1: actually reduce costs, right. And I was like, yeah, but 259 00:16:23,876 --> 00:16:28,116 Speaker 1: that's easy. That's the easy stop you for that. You 260 00:16:28,116 --> 00:16:31,836 Speaker 1: can use the computer for that. Someone's good. I was like, 261 00:16:32,196 --> 00:16:37,036 Speaker 1: someone's gonna build a business doing this. So I just 262 00:16:37,116 --> 00:16:40,036 Speaker 1: want to I just want to make sure that we're 263 00:16:40,076 --> 00:16:45,356 Speaker 1: really clear about what rules are actually changing here, right Yeah, 264 00:16:45,436 --> 00:16:52,356 Speaker 1: So okay, so um so the traditional way that almost everybody, 265 00:16:52,596 --> 00:16:57,636 Speaker 1: including medicare, the federal program that pays for old people's healthcare. 266 00:16:57,676 --> 00:17:00,196 Speaker 1: It's but probably the most important sort of payer and 267 00:17:00,236 --> 00:17:04,316 Speaker 1: healthcare in this country. Right, they pay doctors fee for service. 268 00:17:04,356 --> 00:17:06,276 Speaker 1: You do your doctor, you do something for a patient, 269 00:17:06,396 --> 00:17:09,036 Speaker 1: you get money. And so what's happening in two thousand 270 00:17:09,236 --> 00:17:15,076 Speaker 1: twelve is the government is saying Medicare is offering doctors, 271 00:17:15,196 --> 00:17:19,076 Speaker 1: primary care doctors in particular, a different way to get paid, right, 272 00:17:19,556 --> 00:17:22,796 Speaker 1: and they call it accountable care, Like, what exactly does 273 00:17:22,876 --> 00:17:27,716 Speaker 1: that mean? So that means that if you submit an 274 00:17:27,756 --> 00:17:30,916 Speaker 1: application to the government, your primary care doctor, you and 275 00:17:30,956 --> 00:17:34,076 Speaker 1: your friends submit an application to the government, and the 276 00:17:34,116 --> 00:17:39,476 Speaker 1: government says, oh, you care for these named human beings, right, 277 00:17:39,556 --> 00:17:43,956 Speaker 1: you're the primary care doctor for these Medicare beneficiaries. So 278 00:17:43,996 --> 00:17:48,516 Speaker 1: if you have ten thousand Medicare beneficiaries who are getting 279 00:17:48,556 --> 00:17:52,636 Speaker 1: primary care from you, the Medicare actuaries, that's their job. 280 00:17:53,116 --> 00:17:56,076 Speaker 1: They look to see who are those people, what have 281 00:17:56,116 --> 00:17:58,716 Speaker 1: their costs been in the past, how sick are they? 282 00:17:58,796 --> 00:18:01,636 Speaker 1: And they're going to say, ah, next year, we think 283 00:18:01,676 --> 00:18:05,956 Speaker 1: those ten thousand lives are going to cost us a 284 00:18:06,156 --> 00:18:11,596 Speaker 1: hundred million dollars. Now, if the actual costs, once all 285 00:18:11,596 --> 00:18:14,036 Speaker 1: the claims are submitted and all the costs are counted up, 286 00:18:14,316 --> 00:18:16,996 Speaker 1: all of the hospitalizations, all the specialist visits, all the 287 00:18:17,036 --> 00:18:20,196 Speaker 1: medications and scans and everything else that gets done to 288 00:18:20,276 --> 00:18:23,196 Speaker 1: the patient. If that actual cost comes in not at 289 00:18:23,236 --> 00:18:26,156 Speaker 1: one hundred million, which was our budget, but at ninety million, 290 00:18:26,356 --> 00:18:30,356 Speaker 1: there's ten million dollars of savings, and we will share 291 00:18:30,636 --> 00:18:34,676 Speaker 1: those savings with these docs. We will give say half 292 00:18:34,996 --> 00:18:37,796 Speaker 1: of those savings to the docks. So of that ten 293 00:18:37,796 --> 00:18:40,556 Speaker 1: million dollars of savings, the government gets to keep five 294 00:18:40,636 --> 00:18:45,396 Speaker 1: million they spent less, and the docks get five million. 295 00:18:45,796 --> 00:18:49,516 Speaker 1: And you have to prove that you actually improved quality 296 00:18:49,596 --> 00:18:53,836 Speaker 1: while you reduced cost of care. It's a fundamental rewriting 297 00:18:53,836 --> 00:18:57,556 Speaker 1: of the incentive structure of American healthcare. So you see 298 00:18:57,596 --> 00:19:00,716 Speaker 1: this new thing in the world, and what do you think. 299 00:19:02,236 --> 00:19:05,636 Speaker 1: I think, Who's someone's going to build this business that 300 00:19:06,156 --> 00:19:08,956 Speaker 1: does this? And I went to the Brookings Institute where 301 00:19:08,996 --> 00:19:10,756 Speaker 1: they had done a lot of the foundational work on 302 00:19:10,796 --> 00:19:13,876 Speaker 1: this idea, and I created a learning network to teach 303 00:19:13,876 --> 00:19:15,676 Speaker 1: other people how to do it, and we published a 304 00:19:15,716 --> 00:19:18,356 Speaker 1: tool kit, and we had a learning collaborative. And I 305 00:19:18,436 --> 00:19:21,716 Speaker 1: kept waiting for nine months. I kept waiting for I 306 00:19:21,836 --> 00:19:24,396 Speaker 1: was like literally every morning I'd wake up and I'd 307 00:19:24,396 --> 00:19:27,756 Speaker 1: be like, Who's Who's starting a company to do this. 308 00:19:27,956 --> 00:19:30,796 Speaker 1: Where where is that come? It's such an obvious idea, 309 00:19:30,916 --> 00:19:33,556 Speaker 1: I thought to myself, it is like and the obvious 310 00:19:33,596 --> 00:19:35,636 Speaker 1: thing for me was, well, I ain't going to be 311 00:19:35,636 --> 00:19:40,836 Speaker 1: the hospitals, right, the most powerful people in healthcare today 312 00:19:41,116 --> 00:19:45,756 Speaker 1: are hospitals and health systems under one incentive structure. But 313 00:19:46,036 --> 00:19:50,796 Speaker 1: getting those people, the incumbents, the ones who are powerful 314 00:19:50,836 --> 00:19:54,036 Speaker 1: in the current system, the goliaths, getting them to change 315 00:19:54,076 --> 00:19:56,996 Speaker 1: their economic model, well, that's never going to happen. Whereas 316 00:19:57,276 --> 00:19:58,996 Speaker 1: you go to a you know, we got a group 317 00:19:59,036 --> 00:20:01,436 Speaker 1: of primary care dogs, You're like, hey, you got nothing 318 00:20:01,436 --> 00:20:05,396 Speaker 1: to lose but your chains. Yeah, they already don't like 319 00:20:05,516 --> 00:20:07,876 Speaker 1: the system. Right with primary care dots. If you're starting 320 00:20:07,916 --> 00:20:11,716 Speaker 1: with people who are disgruntled as a as a general rule, 321 00:20:12,076 --> 00:20:14,836 Speaker 1: are appropriately pissed off of the current system, doesn't help 322 00:20:14,876 --> 00:20:16,716 Speaker 1: them take the care of their patients that they believe 323 00:20:16,996 --> 00:20:20,076 Speaker 1: their patients should be getting. So okay, every day you're 324 00:20:20,116 --> 00:20:24,156 Speaker 1: looking for somebody to start this obvious company doesn't happen. 325 00:20:24,876 --> 00:20:29,116 Speaker 1: And so I went for a walk with the VC 326 00:20:30,316 --> 00:20:37,316 Speaker 1: and that metaphor or literal did you actually go literal 327 00:20:35,676 --> 00:20:40,636 Speaker 1: without Yes? Yes I did, Yes I did. They got 328 00:20:40,676 --> 00:20:43,316 Speaker 1: it right, away, and they were, you know, they saw 329 00:20:43,356 --> 00:20:49,076 Speaker 1: the long term opportunity for combining information and incentives in 330 00:20:49,116 --> 00:20:51,236 Speaker 1: a new way and creating a new business model, and 331 00:20:51,276 --> 00:20:54,916 Speaker 1: so they like, literally we had one one pitch meeting 332 00:20:55,276 --> 00:20:58,996 Speaker 1: after that, and they they they gave us the money 333 00:20:59,036 --> 00:21:01,876 Speaker 1: to start the company. To to my co founder and 334 00:21:01,916 --> 00:21:06,556 Speaker 1: I to Guvees who had never started business before. Were 335 00:21:06,596 --> 00:21:11,156 Speaker 1: there moments as your getting the company going, as it's growing, 336 00:21:11,556 --> 00:21:14,956 Speaker 1: when there were things that were harder than you expected, 337 00:21:15,076 --> 00:21:17,556 Speaker 1: things that didn't work that you thought would work. I mean, 338 00:21:17,676 --> 00:21:21,436 Speaker 1: is this obvious idea to you? Yeah? Was it obvious 339 00:21:21,436 --> 00:21:28,916 Speaker 1: to everybody else? Well, obviously not. And I think the 340 00:21:30,116 --> 00:21:32,996 Speaker 1: biggest thing that didn't make sense to people was the 341 00:21:33,436 --> 00:21:40,916 Speaker 1: going after these independent practices community docs as opposed to 342 00:21:40,996 --> 00:21:47,716 Speaker 1: the big, gleaming health centers. But the thing that really 343 00:21:48,916 --> 00:21:53,636 Speaker 1: shook us was we we went out and we signed 344 00:21:53,676 --> 00:21:58,916 Speaker 1: up our first groups of doctors in four states, and 345 00:21:59,876 --> 00:22:03,236 Speaker 1: it felt just charmed. Everything was magic, And we built 346 00:22:03,236 --> 00:22:06,676 Speaker 1: our software system and we started analyzing the data and 347 00:22:06,716 --> 00:22:08,916 Speaker 1: we started doing the stuff that we knew was the 348 00:22:09,156 --> 00:22:14,556 Speaker 1: right things to do for patients, and wholly commole hospitalization 349 00:22:14,716 --> 00:22:19,396 Speaker 1: rates came down measurably within that first year seven percent. 350 00:22:20,316 --> 00:22:23,196 Speaker 1: It worked. It worked, and it was like and it 351 00:22:23,276 --> 00:22:24,836 Speaker 1: was you know a lot of elbow grease and grit 352 00:22:24,916 --> 00:22:28,876 Speaker 1: and whatever, but just just churning the focus of these 353 00:22:28,916 --> 00:22:34,236 Speaker 1: primary care docs on reducing r visits, reducing hospitalizations was working. 354 00:22:35,036 --> 00:22:38,436 Speaker 1: And I was like, oh my god, charmed life, right, Like, 355 00:22:38,476 --> 00:22:40,956 Speaker 1: this thing is, this thing is gonna work, gonna totally 356 00:22:40,956 --> 00:22:44,916 Speaker 1: take off. And then we got the results from our 357 00:22:44,996 --> 00:22:51,156 Speaker 1: first year's performance, and sure enough, total hospitalizations went down 358 00:22:51,196 --> 00:22:55,036 Speaker 1: by seven percent, the biggest source of suffering in American healthcare. 359 00:22:55,116 --> 00:23:00,116 Speaker 1: Quality went up and costs did not come down. Huh 360 00:23:00,156 --> 00:23:02,636 Speaker 1: So it didn't work. It seemed like it was working, 361 00:23:02,676 --> 00:23:06,916 Speaker 1: but the core bottom line things, they didn't come down, 362 00:23:07,436 --> 00:23:09,996 Speaker 1: and we only get paid. Like we had done all 363 00:23:09,996 --> 00:23:13,876 Speaker 1: this work, burned all this cash, hired all these people, 364 00:23:13,956 --> 00:23:17,036 Speaker 1: gotten all the hopes of these doctors up right, and 365 00:23:17,276 --> 00:23:21,116 Speaker 1: and we and these investors and we told them, hey, 366 00:23:21,116 --> 00:23:24,116 Speaker 1: it's working. We're gonna get paid. And you don't get 367 00:23:24,116 --> 00:23:27,356 Speaker 1: paid for improving quality or reducing hospitalizations. You get paid 368 00:23:27,356 --> 00:23:30,036 Speaker 1: if total cost of care comes down. And when we 369 00:23:30,196 --> 00:23:33,236 Speaker 1: looked into it and we actually published a paper, you're like, 370 00:23:33,796 --> 00:23:36,596 Speaker 1: this sucked. Let's tell the world, how much pain we're 371 00:23:36,596 --> 00:23:39,956 Speaker 1: in and what happened? And so what we learned was 372 00:23:40,476 --> 00:23:45,156 Speaker 1: that the hospitals, when faced with a decrease in utilization, 373 00:23:45,996 --> 00:23:50,596 Speaker 1: changed their coding so they got paid more for admission. 374 00:23:50,996 --> 00:23:53,996 Speaker 1: And to be clear, changing their coding means changing the 375 00:23:53,996 --> 00:23:56,916 Speaker 1: way they bill, so they're billing so that they get more. 376 00:23:57,316 --> 00:23:59,476 Speaker 1: There are fewer patients coming to the hospital, they're billing 377 00:23:59,516 --> 00:24:04,116 Speaker 1: more for each patient. For each patient. Yeah, so okay, 378 00:24:04,156 --> 00:24:06,516 Speaker 1: so so far you're not making any money, right because 379 00:24:06,556 --> 00:24:09,876 Speaker 1: you make money by saving money. What do you how 380 00:24:09,876 --> 00:24:13,116 Speaker 1: do you respond? Well, we had also in the in 381 00:24:13,196 --> 00:24:17,956 Speaker 1: the intim we had, we had massively expanded, So not 382 00:24:18,036 --> 00:24:20,476 Speaker 1: only were we waiting to see if we were going 383 00:24:20,556 --> 00:24:22,036 Speaker 1: to get paid, but we went out there and on 384 00:24:22,076 --> 00:24:25,156 Speaker 1: the success of the vision and the mission and the 385 00:24:25,196 --> 00:24:28,036 Speaker 1: tools we built and the early results we got, we 386 00:24:28,076 --> 00:24:32,676 Speaker 1: signed up a hundred practices in five new states and 387 00:24:33,276 --> 00:24:36,516 Speaker 1: we had to make it work. So we went to 388 00:24:36,596 --> 00:24:40,476 Speaker 1: five states and fortunately in that first year with this 389 00:24:40,596 --> 00:24:43,876 Speaker 1: new batch, one out of five states the docs got paid. 390 00:24:44,276 --> 00:24:48,596 Speaker 1: If the initial problem is that, as the doctors you're 391 00:24:48,596 --> 00:24:52,276 Speaker 1: working with, our lowering admission rates to hospitals, hospitals are 392 00:24:52,276 --> 00:24:56,076 Speaker 1: effectively charging more for each admission. How do you solve that? 393 00:24:56,836 --> 00:24:59,516 Speaker 1: How do you how do you fix that? There's only 394 00:24:59,516 --> 00:25:01,516 Speaker 1: a limit to how much you can play those games, 395 00:25:01,676 --> 00:25:05,036 Speaker 1: how much the hospitals can play those games? Yeah? Yeah, yeah, 396 00:25:05,076 --> 00:25:07,916 Speaker 1: so you so you just drive down admissions even more, 397 00:25:08,356 --> 00:25:11,996 Speaker 1: even more. Yeah, And were you able to do that? Yeah? Yeah, 398 00:25:11,996 --> 00:25:18,116 Speaker 1: So we're now down seventeen percent. Hospitalizations are down seventeen 399 00:25:18,196 --> 00:25:23,556 Speaker 1: percent in for our patients compared to what they would 400 00:25:23,596 --> 00:25:27,476 Speaker 1: be if they were just like everybody else in their communities. 401 00:25:27,476 --> 00:25:30,596 Speaker 1: So that's gigantic. So you're saying, same kind of patients 402 00:25:30,596 --> 00:25:33,476 Speaker 1: you match the you know, big batch of patients, thousands 403 00:25:33,476 --> 00:25:37,276 Speaker 1: of patients, if they're working with the doctors you work with, 404 00:25:37,356 --> 00:25:41,956 Speaker 1: they're seventeen percent less likely to be admitted to the hospital. Correct? 405 00:25:42,276 --> 00:25:44,596 Speaker 1: Is that at all a function of selection bias? The 406 00:25:44,636 --> 00:25:47,116 Speaker 1: fact that docs who sign up with you already have 407 00:25:47,196 --> 00:25:49,796 Speaker 1: a lower admission rate among their patients because that's what 408 00:25:49,836 --> 00:25:53,036 Speaker 1: they're into. I mean, that's such a big effect. It is, 409 00:25:53,156 --> 00:25:58,276 Speaker 1: And this is compared to themselves, So it's it's it's 410 00:25:58,276 --> 00:26:03,476 Speaker 1: comparing our practices compared to themselves before, compared to non 411 00:26:04,196 --> 00:26:08,036 Speaker 1: practices compared to themselves before right. So it's the same docs, 412 00:26:08,076 --> 00:26:11,796 Speaker 1: the same doctors, the patients get admitted to the hospital 413 00:26:11,916 --> 00:26:17,156 Speaker 1: seventeen percent less often once they sign up with you. Then, yeah, 414 00:26:17,196 --> 00:26:19,516 Speaker 1: how how do you do that? I mean, I understand 415 00:26:19,556 --> 00:26:23,076 Speaker 1: that incentives are powerful. Is it just the money? No, 416 00:26:25,316 --> 00:26:30,236 Speaker 1: can use a computer for that. Um, it's it's actually 417 00:26:30,756 --> 00:26:37,076 Speaker 1: creating data to target the right people for the right interventions. 418 00:26:37,236 --> 00:26:41,716 Speaker 1: It's doing the basics better. So the fundamental thing that 419 00:26:41,756 --> 00:26:44,596 Speaker 1: we're doing is we're increasing more access to primary care. 420 00:26:44,676 --> 00:26:47,996 Speaker 1: So we get thirty four percent more visits to primary 421 00:26:48,036 --> 00:26:50,316 Speaker 1: care doctors for the patients in particular who need it 422 00:26:50,356 --> 00:26:52,396 Speaker 1: the most. And I just want it. There's there was 423 00:26:52,396 --> 00:26:54,356 Speaker 1: a phrase you used in there that I just want 424 00:26:54,356 --> 00:26:57,436 Speaker 1: to grab for a second. It was not just that 425 00:26:57,476 --> 00:26:59,316 Speaker 1: it's easier to see the doctor. It's easier to see 426 00:26:59,316 --> 00:27:01,876 Speaker 1: the doctor for the patients that need it the most. 427 00:27:02,636 --> 00:27:05,156 Speaker 1: That's right. It seems like that's a really important phase 428 00:27:05,476 --> 00:27:08,476 Speaker 1: is figuring out who those patients that actually need to 429 00:27:08,516 --> 00:27:11,356 Speaker 1: see their price marycare doctor or seeing them when they 430 00:27:11,396 --> 00:27:15,876 Speaker 1: need to be seen like that seems hard, Yeah, and 431 00:27:16,116 --> 00:27:19,596 Speaker 1: that is where the technology comes in. My father in 432 00:27:19,716 --> 00:27:25,276 Speaker 1: law was CEO of a health plan and before that 433 00:27:25,316 --> 00:27:28,156 Speaker 1: he had started one of the health insurance company health 434 00:27:28,156 --> 00:27:31,356 Speaker 1: insurance company, and before that he had started one of 435 00:27:31,476 --> 00:27:35,076 Speaker 1: the first HMOs in the country. And he said to me, 436 00:27:35,156 --> 00:27:37,156 Speaker 1: as far as how's this different than what we did 437 00:27:37,156 --> 00:27:42,276 Speaker 1: fifty years ago? And I showed him our our technology 438 00:27:42,276 --> 00:27:45,676 Speaker 1: and he was like, Okay, that's what's different. So how 439 00:27:45,716 --> 00:27:48,396 Speaker 1: does it actually work? So how does it work that 440 00:27:48,516 --> 00:27:51,716 Speaker 1: a doctor knows what patients they actually need to see 441 00:27:51,996 --> 00:27:56,476 Speaker 1: or need to be available for. Yeah, so nine of 442 00:27:56,476 --> 00:28:04,676 Speaker 1: our practices use our platform, our software platform every day. Yesterday, 443 00:28:05,276 --> 00:28:07,236 Speaker 1: our practices were in the tool and what they do. 444 00:28:07,276 --> 00:28:09,756 Speaker 1: When you log into the tool, the first thing you 445 00:28:09,796 --> 00:28:13,076 Speaker 1: see is you had three patients who went to the 446 00:28:13,116 --> 00:28:18,836 Speaker 1: emergency room yesterday, and we think that within forty eight 447 00:28:18,836 --> 00:28:22,356 Speaker 1: hours you should call them. And here's here's the tool 448 00:28:22,596 --> 00:28:24,756 Speaker 1: that helps you call them and you can now send 449 00:28:24,796 --> 00:28:27,876 Speaker 1: them a text message. And just to be clear, like 450 00:28:28,156 --> 00:28:31,276 Speaker 1: most doctors when their patient goes to the emergency room, 451 00:28:32,596 --> 00:28:36,236 Speaker 1: how do they find out or that they don't They don't. 452 00:28:36,916 --> 00:28:39,556 Speaker 1: They don't they don't because how would they be right? 453 00:28:39,636 --> 00:28:41,596 Speaker 1: It's just like how would the doctor is over here 454 00:28:41,636 --> 00:28:44,116 Speaker 1: in some clinic and then the patient is just driving 455 00:28:44,156 --> 00:28:46,276 Speaker 1: to the emergency room or whatever getting driven to the 456 00:28:46,316 --> 00:28:50,516 Speaker 1: emergency room, and they're like separate islands exactly, even when 457 00:28:50,676 --> 00:28:53,076 Speaker 1: even when the doctor's part of the same system as 458 00:28:53,076 --> 00:28:56,756 Speaker 1: the emergency room, there's no system to inform them, there's 459 00:28:56,796 --> 00:28:59,316 Speaker 1: no system to call the patient. There's no system for that. 460 00:28:59,556 --> 00:29:03,916 Speaker 1: Why because there's no incentive to do that. Yeah, nobody 461 00:29:03,916 --> 00:29:06,116 Speaker 1: gets paid more to if they do that, And now 462 00:29:06,156 --> 00:29:10,596 Speaker 1: there is, and now there is. Right, So that's that's 463 00:29:10,636 --> 00:29:12,116 Speaker 1: one of the things you'll see. The other thing you'll 464 00:29:12,116 --> 00:29:14,556 Speaker 1: see is you'll see it that says, hey, you know what, 465 00:29:15,236 --> 00:29:16,996 Speaker 1: here's a list of patients who you haven't seen in 466 00:29:17,036 --> 00:29:19,636 Speaker 1: a while, who you know they might need your help. 467 00:29:19,996 --> 00:29:23,036 Speaker 1: They're pretty got pretty complex conditions, or their last blood 468 00:29:23,036 --> 00:29:25,956 Speaker 1: pressure was pretty high or whatever. Right, here's a list. 469 00:29:25,996 --> 00:29:27,596 Speaker 1: But you don't need to worry about that. Here's a 470 00:29:27,596 --> 00:29:30,076 Speaker 1: list of patients for your scheduling person, the front desk 471 00:29:30,116 --> 00:29:33,436 Speaker 1: person between patients. When she's got a few minutes, she 472 00:29:33,436 --> 00:29:35,476 Speaker 1: should call these patients and make an appointment for them 473 00:29:35,516 --> 00:29:38,356 Speaker 1: to come back into primary care. Right, So that's the 474 00:29:38,436 --> 00:29:41,116 Speaker 1: wellness work Listen, when they come in once a year, 475 00:29:41,796 --> 00:29:45,396 Speaker 1: like let's have a visit. That's not based on you 476 00:29:45,676 --> 00:29:47,956 Speaker 1: reaching out to me because you've got something wrong with you. 477 00:29:48,196 --> 00:29:50,396 Speaker 1: But once a year, let's have a visit where we 478 00:29:50,396 --> 00:29:51,916 Speaker 1: can just sit and we can talk, and we can 479 00:29:51,956 --> 00:29:54,076 Speaker 1: see how do we keep you healthy? What are the 480 00:29:54,076 --> 00:29:56,276 Speaker 1: things that are going to be problems for you and 481 00:29:56,356 --> 00:30:00,116 Speaker 1: prevent them? And you've found that it's profitable under this 482 00:30:00,156 --> 00:30:05,356 Speaker 1: new system to do that. The practices make about fifty 483 00:30:05,356 --> 00:30:12,436 Speaker 1: percent more per Medicare patient who's in these models fifty 484 00:30:12,476 --> 00:30:16,756 Speaker 1: percent more, and they're they're like scrapping and begging for 485 00:30:16,836 --> 00:30:19,196 Speaker 1: like a two or three or four percent rate increase, 486 00:30:19,436 --> 00:30:22,476 Speaker 1: and they're getting a fifty percent increase through this model. 487 00:30:23,076 --> 00:30:26,756 Speaker 1: And is allidate's business model to take some chunk of 488 00:30:26,796 --> 00:30:32,276 Speaker 1: the additional Medicare pay basically that the doctors get. Is 489 00:30:32,316 --> 00:30:35,316 Speaker 1: that the model, that's the model. The model is we 490 00:30:35,436 --> 00:30:38,476 Speaker 1: only get paid when the practices get rewarded and the 491 00:30:38,516 --> 00:30:41,596 Speaker 1: government saves money. Are the practices on the hook if 492 00:30:41,636 --> 00:30:46,956 Speaker 1: their patients wind up costing medicare more than expected? Great question. 493 00:30:47,436 --> 00:30:51,756 Speaker 1: So we do start with what's called one sided models, 494 00:30:51,876 --> 00:30:56,516 Speaker 1: where it's it's it's like reward only, but then we 495 00:30:56,596 --> 00:31:01,236 Speaker 1: move pretty quickly to two sided models and we backstop 496 00:31:01,356 --> 00:31:04,756 Speaker 1: that risk for the practices. So so you take the 497 00:31:04,956 --> 00:31:06,876 Speaker 1: you're on the hook and the practice is not on 498 00:31:06,916 --> 00:31:09,636 Speaker 1: the hook. Correct. So I feel like I where you 499 00:31:09,676 --> 00:31:12,956 Speaker 1: came from and where you are now, and I'm curious 500 00:31:13,276 --> 00:31:17,116 Speaker 1: where you're going next, and in particular, you know, what 501 00:31:17,196 --> 00:31:19,996 Speaker 1: are the things you're trying to figure out that you 502 00:31:20,076 --> 00:31:25,316 Speaker 1: haven't yet cracked. We're in this in order to reduce 503 00:31:25,396 --> 00:31:30,476 Speaker 1: hospitalizations and complications and bad stuff happening to patients, and 504 00:31:30,516 --> 00:31:32,956 Speaker 1: we need to continue and there's so much every day 505 00:31:32,996 --> 00:31:37,076 Speaker 1: I see the opportunity that we're leaving behind. Tell me 506 00:31:37,156 --> 00:31:39,396 Speaker 1: more about that, I mean, and specifically when you talk 507 00:31:39,436 --> 00:31:42,916 Speaker 1: about seeing opportunities that you're leaving on the table, Like, 508 00:31:42,956 --> 00:31:45,076 Speaker 1: what's a specific example of a thing there you look 509 00:31:45,076 --> 00:31:46,876 Speaker 1: at you're like, oh my god, we can help people 510 00:31:46,876 --> 00:31:51,276 Speaker 1: and we're not doing it. What's an example of that? Kidney? 511 00:31:51,396 --> 00:31:57,196 Speaker 1: Let me talk about kidney. So when someone's kidneys fail, 512 00:31:58,236 --> 00:32:02,316 Speaker 1: it's a terrible thing, right where your blood is no 513 00:32:02,396 --> 00:32:08,116 Speaker 1: longer being cleaned. They oftentimes it happens unexpectedly and the 514 00:32:08,516 --> 00:32:13,356 Speaker 1: so called crash into needing dialysis, they get hospitalized. There's 515 00:32:13,396 --> 00:32:16,676 Speaker 1: actually a big mortality rate for that when people actually 516 00:32:17,116 --> 00:32:20,076 Speaker 1: die during that and never get a chance right to 517 00:32:20,116 --> 00:32:24,916 Speaker 1: make it to the next stage. We can predict who's 518 00:32:25,276 --> 00:32:31,036 Speaker 1: going to crash into dioss with pretty good accuracy. So 519 00:32:31,196 --> 00:32:34,036 Speaker 1: because we have all of this information on the patient 520 00:32:34,156 --> 00:32:38,076 Speaker 1: before upstream of when that happens in the earlier stages 521 00:32:38,516 --> 00:32:41,836 Speaker 1: of kidney impairment, we can do a really good job 522 00:32:41,956 --> 00:32:46,116 Speaker 1: using machine learning AI models of predicting who's going to 523 00:32:46,116 --> 00:32:49,996 Speaker 1: crash into diosis. We can steer those patients because we 524 00:32:50,036 --> 00:32:52,716 Speaker 1: have the primary care relationship with them. We can do 525 00:32:52,796 --> 00:32:58,316 Speaker 1: a handoff from primary care to kidney specialists and kidney 526 00:32:58,436 --> 00:33:01,756 Speaker 1: educators and work with the patients. And what we're seeing 527 00:33:01,876 --> 00:33:05,356 Speaker 1: is at twenty four percent lower rate of hospitalizations twenty 528 00:33:05,396 --> 00:33:08,196 Speaker 1: four percent for patients who've got that handoff into the 529 00:33:08,276 --> 00:33:12,076 Speaker 1: kidney care management program to dialysis. And when that happens, 530 00:33:12,116 --> 00:33:16,316 Speaker 1: presumably hospitalization goes down. Does mortality even go down? People 531 00:33:16,436 --> 00:33:19,276 Speaker 1: die less if you do this. I haven't proven that yet, 532 00:33:19,316 --> 00:33:21,676 Speaker 1: but I think it does well. So are you doing 533 00:33:21,716 --> 00:33:23,196 Speaker 1: that already or is that a thing you think you 534 00:33:23,236 --> 00:33:26,276 Speaker 1: can do but that you're not quite doing yet. So 535 00:33:26,316 --> 00:33:30,476 Speaker 1: we have a unit that does these tests, these experiments, 536 00:33:30,636 --> 00:33:34,916 Speaker 1: and then when they work, we scale them. So this 537 00:33:35,036 --> 00:33:37,636 Speaker 1: is literally hot off the presses. We got the results 538 00:33:37,636 --> 00:33:39,676 Speaker 1: from the pilot. It seems promising, and we're going to 539 00:33:39,716 --> 00:33:42,876 Speaker 1: scale it and you can imagine like you can imagine 540 00:33:42,956 --> 00:33:46,236 Speaker 1: that same sort of rinse and repeat right for a 541 00:33:46,276 --> 00:33:50,556 Speaker 1: whole host of other of other things of just finding 542 00:33:50,596 --> 00:33:53,436 Speaker 1: ways to be smarter about figuring out who is about 543 00:33:53,516 --> 00:33:57,956 Speaker 1: to get really sick essentially, that's right exactly, and intervening 544 00:33:58,036 --> 00:34:00,716 Speaker 1: either ideally to prevent them from getting really sick or 545 00:34:00,756 --> 00:34:04,476 Speaker 1: at least to reduce the bad outcomes associated with getting 546 00:34:04,476 --> 00:34:10,236 Speaker 1: really sick. You got it. We'll be back in a 547 00:34:10,276 --> 00:34:21,436 Speaker 1: minute with the lightning round. Now back to the show. Okay, 548 00:34:22,276 --> 00:34:28,876 Speaker 1: let's close with the lightning round. Okay, Um, what you 549 00:34:28,996 --> 00:34:32,356 Speaker 1: ran health? I t for the Obama administration. So what 550 00:34:32,556 --> 00:34:36,676 Speaker 1: is the least glamorous thing about working in the White House? Um? 551 00:34:37,236 --> 00:34:41,516 Speaker 1: We had to pay for our own water like literally, 552 00:34:41,716 --> 00:34:44,956 Speaker 1: what's yeah? Why? Literally we had a we had because 553 00:34:44,996 --> 00:34:48,396 Speaker 1: the government is I mean, you really learn frugality. People 554 00:34:48,436 --> 00:34:51,356 Speaker 1: have no idea Like in the government, we had a 555 00:34:51,396 --> 00:34:53,756 Speaker 1: water club and you had to put in twenty bucks 556 00:34:54,356 --> 00:34:57,556 Speaker 1: for to have the privilege of drinking from you know, 557 00:34:57,596 --> 00:35:01,156 Speaker 1: the filtered water from from the water cooler. Uh, that 558 00:35:01,356 --> 00:35:05,556 Speaker 1: is what you're That is the spirit of frugality that 559 00:35:05,596 --> 00:35:09,356 Speaker 1: people don't understand. The government agencies have. What's why? Piece 560 00:35:09,396 --> 00:35:11,196 Speaker 1: of advice you'd give to someone trying to solve a 561 00:35:11,196 --> 00:35:18,236 Speaker 1: hard problem. Spend a lot of time asking yourself, what's 562 00:35:18,276 --> 00:35:23,116 Speaker 1: the question? What's the what's the heart of the problem? Um, 563 00:35:23,116 --> 00:35:24,756 Speaker 1: and I think you should change the name of your 564 00:35:24,756 --> 00:35:28,316 Speaker 1: podcast to what's part of the problem? What do you 565 00:35:28,316 --> 00:35:31,316 Speaker 1: think I should change it to to to what's the 566 00:35:31,316 --> 00:35:35,516 Speaker 1: heart of the problem? Huh, you don't like it? I 567 00:35:35,756 --> 00:35:38,636 Speaker 1: like I wonder about the problem framing at all, frankly 568 00:35:38,876 --> 00:35:41,396 Speaker 1: as a as a as a name for the podcast 569 00:35:41,436 --> 00:35:44,596 Speaker 1: I've come. I love it. I love it. I love it. 570 00:35:44,836 --> 00:35:46,876 Speaker 1: That's why. That's that's that's why I love your podcast. 571 00:35:47,556 --> 00:35:50,796 Speaker 1: I think it's the most important thing is to be 572 00:35:50,836 --> 00:35:54,996 Speaker 1: working on the most important thing, and and we don't. 573 00:35:55,036 --> 00:35:57,596 Speaker 1: We don't work on the most important thing. It goes 574 00:35:57,636 --> 00:36:00,156 Speaker 1: back to that question you were talking about at the beginning, 575 00:36:00,156 --> 00:36:02,956 Speaker 1: how can we save the most lives? That is that's 576 00:36:02,996 --> 00:36:05,916 Speaker 1: my question, right, that's the that's that's that's the problem 577 00:36:05,956 --> 00:36:08,556 Speaker 1: that I'm trying to solve. But for every every entrepreneur, 578 00:36:09,356 --> 00:36:11,996 Speaker 1: what's the heart of the problem you're trying to solve 579 00:36:12,076 --> 00:36:15,516 Speaker 1: and how can you how can you get to that? 580 00:36:15,796 --> 00:36:20,356 Speaker 1: And what I found is people don't really don't really 581 00:36:20,556 --> 00:36:23,996 Speaker 1: ask themselves and iterate enough on do I have it right? 582 00:36:24,116 --> 00:36:26,116 Speaker 1: Is that really the heart of the problem is that 583 00:36:26,156 --> 00:36:31,916 Speaker 1: the question what was the last hike you went on? Ah? 584 00:36:31,316 --> 00:36:38,236 Speaker 1: I love hiking, and I walk on the Capitol Crescent 585 00:36:38,276 --> 00:36:41,476 Speaker 1: Trail and the Ciano Canal all day. Like some days 586 00:36:41,516 --> 00:36:45,756 Speaker 1: I'll walk eight hours during the workday with my phone 587 00:36:45,796 --> 00:36:50,036 Speaker 1: and my tablet and my backup battery. So the last 588 00:36:50,316 --> 00:36:52,316 Speaker 1: hike I did, was it a long hike on the 589 00:36:52,516 --> 00:36:56,116 Speaker 1: Ciano Canal towpath? And was it, in fact a workday 590 00:36:56,116 --> 00:36:59,076 Speaker 1: where you just walk in for eight hours and running 591 00:36:59,116 --> 00:37:03,276 Speaker 1: your company? Yeah, that's a nice move. That's a very 592 00:37:03,356 --> 00:37:06,556 Speaker 1: strong move. If everything goes well, What problem will you 593 00:37:06,596 --> 00:37:12,836 Speaker 1: be trying to solve in five years hospitals? I think 594 00:37:12,836 --> 00:37:18,036 Speaker 1: we've we've started where it's easier, where the incentive alignment 595 00:37:18,116 --> 00:37:21,876 Speaker 1: is easier with independent primary care, and now we're adding 596 00:37:21,916 --> 00:37:26,156 Speaker 1: on people around that. But particularly in rural areas, we're 597 00:37:26,156 --> 00:37:29,476 Speaker 1: going to need to solve the rural hospital problem and 598 00:37:29,636 --> 00:37:32,476 Speaker 1: maybe maybe maybe we can help flip them to a 599 00:37:32,516 --> 00:37:40,316 Speaker 1: different model. Farzad Mosta Shari is the co founder and 600 00:37:40,436 --> 00:37:44,796 Speaker 1: CEO of Allidate. Today's show was produced by Edith Russlo, 601 00:37:45,036 --> 00:37:48,596 Speaker 1: edited by Robert Smith, and engineered by Amanda k Wong. 602 00:37:49,476 --> 00:37:51,716 Speaker 1: I'd love to get your suggestions for who else I 603 00:37:51,716 --> 00:37:54,436 Speaker 1: should talk to for the show. You can email us 604 00:37:54,516 --> 00:37:57,876 Speaker 1: at problem at Pushkin dot fm, or you can find 605 00:37:57,876 --> 00:38:01,556 Speaker 1: me at Twitter at Jacob Goldstein. I am Jacob Goldstein 606 00:38:01,636 --> 00:38:04,196 Speaker 1: and we will be back next week with another episode 607 00:38:04,236 --> 00:38:09,876 Speaker 1: of What's Your Problem.