- And it's my great pleasure to introduce our opening keynote speaker, Atul Butte, who really needs no introduction. I think this is an encore performance after last year. Atul and I go back many years but I've grown to admire him, not only as a friend but as a professional colleague. And what he has done, particularly in his role as the Chief Data Officer at UC Health, is really charted that pathway. And I'm really excited about you starting off today because one of the things that I admire about you is that you have a technical ability to think how do we get all of this information together but the thing that you always remind us to do is that the most valuable quantity is asking the right questions. In other words, how can that data speak to us in meaningful ways? So with that, I look forward to your opening remarks. Thank you so much for coming back and joining us.
- Thanks. Let's get right to the point. I don't need to insult you guys to tell you that we're in this big data deluge at this point. Every magazine, every cover has something about big data, data sciences. People say he Economist cover at the bottom right was great equating data as the new oil. I like to think of data as the new soil, that you plant your seeds for ideas, right? It's not something where I can grab this and you can't have it. Data's something where I can grab it and you can grab it. And we can actually make different things with data. But certainly data's around and all that data now is really being forecast to change the world, of course in medicine and health care as well. And we're really forecasting a future driven by artificial intelligence and machine learning and deep learning and I really wanna start off this morning, just level setting everyone on what these terms actually mean, right? Let's make sure we understand what these terms mean. Artificial intelligence, machine learning, deep learning. Yeah, a lot of people use them interchangeably but artificial intelligence now is this concept where we're trying to get computers to mimic various aspects of human intelligence. Like translating things from one language to another, driving cars, of course trying to predict what's going on, what's happening in the future. Machine learning is that aspect of artificial intelligence driven by data. Now you would say, how else would you teach computers without data? Well, in the old days, we'd use rules. We'd have experts say this is the rule and that's the rule and that's the rule and we'd have rules based ways of teaching computers. Now we're just dispensing with the experts and going right to the data, right? That's the idea with machine learning and then in machine learning we have to kinds; supervised and unsupervised. Supervised means that I have a right answer. The pathologist gave me the right answer, the radiologist gave me the right answer. I want the computer to get as close to that right answer as it can, right? So you've got sensitivity, specificity, accuracy, ROC curves for the engineers. Unsupervised means I have no idea what a right answer is here, right? I'm drowning in data, make some sense of it. Cluster it, show me different diagrams, data visualization. That's unsupervised, there's no accuracy there, there's no right answer. Right so that's supervised and unsupervised. And then under that is deep learning which is a teach computer's using this kind of weird hierarchy of neurons that kind of mimics human intelligence, how neurons connect to neurons. It's just one aspect of machine learning. Now all of these sound super sophisticated, but I have to remind you, there are literally dummies books on these topics, okay? They are still $19 each. I just checked last night, Amazon Prime. So part of this talk is really to demystify what all this is. These are software tool kits, okay? If you wanna write some software you borrow some of these tool kits in and add them to your software and then you get these kinds of capabilities. So it's nothing, there's no wizard behind the curtain here. These are software tool kits that you use. A few common ones that people use to get some of these tasks actually done. Now AI in medicine goes way back. This is not a new thing. I picked out a couple papers here from late 60's, early 70's. The paper on the left there is from Ted Shortliffe, based on a system that they had created back at Stanford in the late 60's to help doctor's figure out which antimicrobial to use, which antibiotic. Some of you know if you practice in the hospital those antimicrobial grams that you get with all the resistance patterns? That was kinda coded in at the time. So software would tell you how to do that. And even back then, on the right there, is a New England Journal of Medicine article titled Medicine and the Computer. Forecast is really the future for the field of medicine, as early as the 1970's okay? So we've been talking about this for a long time and as most hype cycles go, it's up and down and there was a trough, a desert of AI for a long time because a lot of things were over hyped and now it's all back again. It's all back for a number of different reasons but here are four of them; first we have incredible hardware, we'll talk about that in a second. We have incredible software, I'll talk about that. More data sets to train with and the data sets we're really talking about, there are two major data sets in biomedicine that we're blessed with now. Of course the genomics revolution, we're not too far from the Broad Institute, right across the river here, so you got the genomics side, DNA, but you've got all the medical records as well and all the images, pathology and a lot of digitalization happening in that field. So you've got two streams of big data in medicine and we still have all these unsolved problems. We need better drugs, we need to forecast what's happening with patients and populations. Hardware, perhaps you've heard of this game Fortnight or many others, right? This is a gaming world now. And it turns out, Nvidia, the company that makes these video boards for gamers, well those video boards, those chip sets, are also incredibly useful for deep learning. It's kind of the same kinds of tools, same kind of hardware that you need. In fact, it's gotten to the point where gamers are having a hard time buying these boards because AI folks like us buy thousands of these for data centers, right? So companies like Nvidia and others are just ramping up like crazy. Intel makes these now. IBM has a neuromorphic chip. And many others, even Google's trying to make their own chip. Apple has their own kind of chip, so a lot of people trying to put that kind of deep learning methodology which is sophisticated, it's non-linear, it helps you do hard problems, into hardware like this. So hardware, great hardware, cheap hardware is one thing. Software, we have incredible software tools now. TensorFlow is a toolkit from Google. Spark and Cafe and Jupiter, Jupiter and I think Cafe come from Berkeley. Not only do we have a lot of software tools, our data science language at the bottom there, not only do we have all these tools every one I'm showing you here is free. You don't have to pay anything so it's not just tools, they're open source tools. They're freely available tools. That's another level beyond and you have an incredible developer community now who can get all these tools for paying nothing, all around the world, you know? A kid in Bangladesh can actually code up a TensorFlow classifier now, right? If they have a computer, you can just download this and actually that kind of sophistication is available for free. And, of course, companies are going crazy with all this, right? So it's been just 18/19 months now and even the FDA has approved, if I'm keeping count, keeping score, six different software toolkits or combination devices that have involved machine learning or deep learning. Six in the past 18 months or so. Here are the first three. The top one is for clinical cloud-based deep learning in healthcare, that was Arterys. That's for interpreting cardiac imaging to make sure the right image is there but also to figure out different cardiac parameters from, if I remember correctly, cardiac MRI. The second one actually, Viz.ai was for triage, I think it's stroke triage in the emergency room. Who should be prioritized, obviously, to get to care faster? And the bottom one is diabetic retinopathy. You all know when you have type 2 diabetes one of the things we look at is for diabetic retinopathy, it's a treatable condition but it's easier, seemingly, to get the pictures than to get the ophthalmologist to see the pictures and so now companies like Google have been training their systems. Viz.ai I think was the first, or IDX is the first to get their software toolkit actually approved. And these are just three of six and there are probably many, many more in the pipeline now. And it's not just deep learning but it's actually deep learning on the cloud. You don't even have to have the hardware here. The FDA is saying, "Yeah you could run this "on someone else's computer, it's on the cloud, "in someone else's data centers." So clearly this is a growing field now. Just to be clear, it's up from zero, I think zero just 18 months ago. Now, we haven't been asleep at the wheel at UCFS as well, so just to put our press releases out there just to give an idea what a health system does at actual medical center, how we can do these things. A lot of these happened before I got there. I'll spend a little bit of time talking about the Google one at the bottom. A good example is the one at the top, so Mike Blum, who runs our Center for Digital Health Innovation, had this partnership with GE so obviously we take chest x-rays, right? Like most academic medical centers, right? If you have a cough or pneumonemia or chronic obstructive pulmonary disorder you can get a chest x-ray, COPD, and we're not even talking about diagnosing those, right? We'll talk about two high risk conditions that are often missed by radiologist. One is what's call pneumothorax, if you have a collapsed lung. Now if you have a really collapsed lung it's easy to see. If you have subtle collapsed lung, it's kind of really hard to tell. High risk because it's treatable, you shouldn't miss that so that's one. And a second's, if you're really sick and you're in an intensive care unit, right? You need to have a tube down your throat to help you breath, an endotracheal tube, you're hooked up to a ventilator. Where the other end of that tip is, is critically important because if you're using two lungs of air to fill up the lungs it shouldn't be in one of the lungs or the other, right? That would be a bad thing so you have to make sure it's above the branch point for the lungs and where that is, it's also very high risk. You don't want to get that wrong. Now, software engineers at UCSF got deep learning to work with those two parameters and of course it helps that the radiologist are calling these, you don't have to get new radiologists, it's in the notes, right? So you have images, you have these gold standard radiologists reading these things, you can teach the computer how to do it and then UCSF use that to enter in a co-development partnership with GE where, if all goes well in the next one or two or three years, those portable x-ray units you get that you bring to the intensive care unit, will automatically read those two conditions, right? The same way, I'm guessing like how at EKG give a preliminary read, you know? A lot of people ignore that but I can imagine now the future portable chest x-ray unit will have that as a preliminary read. That's one of several different collaborations that medical centers all over the place, and there's some good and bad stories, right? We know that the recent press as well, you have to do this in a very safe, respectful way. These are patients, patient's lives, patient's data. You cannot be cavalier about this but if you do this in a safe, respectful way using that data you can bring quality care now even closer to the point of care or to the hands of people who actually wouldn't know how to do this. Intel is working on new chips. Nvidia, of course, I already mentioned. I'll spend a moment talking about our partnership with Google, I think that's the next slide here. Yeah, see, you can't read any of this. So we had a paper out about six months ago so this is a complicated paper but, and this is with Google Brain, the Brain Team, now known as Google AI and if you even try to ask where is healthcare in Google, your answer is as good as mine because it's scattered, it's changing every day. As you know, some of their recent hires, so I really have no particular insight there either but the Google Brain Team really knows what they're doing in terms of creating some of the these software tools and so this is a partnership between University of Chicago and UCSF to work with Google to answer three important questions, three questions. The first is if a patient gets admitted, what are the chances they're gonna die during that admission, okay? That's the first question. The second question is, if the patient's discharged, right? They make it out, most of our patients do well in the hospital, they get discharged, what discharge codes would we use? For example, ICD9 codes, right? Something that a coder might or billing office might do. Just given the stream of data, if you ordered this and you give this drug and you got this lab test result, what would we say the patient has at the end, right? Without asking the doctor. And then third is, if the patient gets discharged, what is the likelihood they're gonna come back unexpectedly? Our 30 day readmission problem, which we know is a marker for quality care. So all three of those, now, the papers shows, the work shows that deep learning can actually get you to reasonable accuracy. That doesn't mean you can do something about it, right? A good doctor argues with me all the time saying that a good doctor can tell if a patient's going to die in the hospital, too. I mean, they know, obviously they're sick patients that get admitted to the hospital. Just because they're admitted doesn't mean you can prevent that death, right? They're usually pretty sick patients. The future then is in the trials, the clinical trials of this as an intervention, right? Just because you know a prediction doesn't mean you can do anything about it, today. But in terms of this, it shows, and the graphic on the right there which you can barely see is one particular patient that was predicted to die during the admission, she indeed died within 10 days and you can see that in red are some of the code words. Metastatic breast cancer, she's on antifungals, she's on a drug to help her immune system kick out some more white blood cells so these are pretty bad things to have in your medical record, you're probably pretty sick. A good doctor could probably tell that too in this particular case but now we can do this with Google. And just to be very clear here, this was an effort with de identified data, so from our side, from UCSF, it's just a structured data element that we get out of Epic, no text, no images and no identifiers so this is the HIPA 18 category of identifiers, shifting the dates, all of that so we don't have to give identifiers to do any of this stuff, this is just research here. Alright, so now let's think even broader here. I'm really pleased now to be this new Chief Data Scientist at the University of California Health System and just to introduce you or reintroduce you to the University of California, look I was at Stanford for 10 years, I never knew any of this and I was just right down the road, I'm particularly proud of this, so University of California, it's enormous, right? We have 10 campuses and three national labs, three supercomputer centers, right? The Lawrence Berkeley, Lawrence Livermore and the San Diego Supercomputer Center. We have 200,000 employees which actually is one of the larger employers in the United States and a quarter million students a year and then we have six medical schools, right? So that's UCSF, UCLA which are both top 10, Irvine, Davis, San Diego and Riverside. Riverside is brand new, they're tiny. They have a couple thousand patients. The other five, each have comprehensive cancer centers, NCIs, comprehensive cancer centers and all five have their own Clinical and Translational Science Award so you can anomaly say they're the best of the best. Our clinical revenue, if you just add it all together, right, that's one way to count, is to add them? It's about 11.4 billion per year. We have 5,000 faculty not only on staff but if you'll just look at the records, we have about 100,000 physicians taking care of our patients. 100,000 physicians that ordered something in Epic, across the campuses here. We're now partnered, right, with United Health Group which is why I keep showing up to this meeting again and again because this is public, right? As of two years ago. That aspirationally we're going make a single, accountable care organization for the entirety of the University of California, right? So even the press release said 10 year strategic problem. Five to 10 years aspirationally, that we will call this thing UC Care, or University of California Health System and within five to 10 years, in partnership with United Health Group, we're gonna learn how to take on risk and how to manage populations but it's amazing, the minute you have a business reason like this it becomes so much easier to share data, right? Because there is this national narrative that is it so hard to get electronic health record systems to doctors, right? Everyone's heard this narrative. It's so much easier when you have a business reason to do it, right? Because everyone thinks we want competitors to share data. Of course we're not gonna share data, right? We'll say it's a technical thing but it's not a technological thing, right? We don't want to share with our next door neighbor who's competing for the same patients, right? It's not a technological thing but here we have this amazing constellation of six medical schools, five academic medical centers that all want to work together for this goal here and I can't really understate that enough at the strategic level, once you have a business reason, it is much easier to want to share the data. I wouldn't be an IT guy if I didn't show boxes pointing to boxes, so here's the one slide of boxes pointing to boxes. These are the six silos going to another silo. We shouldn't use silos here but you can see it's UCSF, UCLA, Irvine, David, San Diego and Riverside all into one UC Heath data warehouse at the top there. And so just to be clear, we're all on Epic. We weren't always on Epic. Irvine was the last to move to Epic, I think it was about a year ago, year and half now. And to make this even more complicated, San Diego, Irvine and Riverside are all on one instance of Epic and UCLA, UCSF and UC Davis are each on their own instance of Epic so we have merge model and an individual model, we got it all. Four instances across the six different hospital systems here. Alright so if you add it all up, it's kind of an incredible view of the entire medical system. The number we love to tout around is 15 million patients. So we have seen, the University of California, has seen 15 million patients in the past 15 or so years. That is 5% of the US population has received some care in the University of California so it's an astounding number. Now I admit, many of them just got a flu shot with us, right? There is that, and it's a long tail but some of the most complex care is also in our system. So as I like to say, we have everything in Epic from Tylenol to CAR T cells now, right? There are people who have ordered CAR T cells in Epic now. We have those records as well. Now that's 15 million, if you just look in the modern era when we put in Epic, at least at UCSF, UCLA, around 2012 or so, it's about 5.2 million patients. Still a respectable number and even to be clear here, I'm only counting the main hospitals and the main clinics on the main campuses. So for example at San Francisco, if you know our town, we also have San Francisco General Hospital, we have Oakland Children's Hospital, those are part of UCSF now, I'm not even counting those yet. Or Rady Children's in San Diego or any of the equivalent affiliates, I'm just talking the main hospitals, main campuses, even that's more than 5 million patients now. You can see the numbers, 100 million encounters, half a billion vital signs, half a billion blood test results, half a billion diagnosis codes. And this changes now, we have a monthly refresher, we're moving to a weekly refresh as well. 100 million encounters sounds unbelievably impressive and scary, just not that every phone call is documented in Epic, right? Every time anyone calls any one of doctors or nurses on any of our patients, every phone call is documented. Imagine training computers for just the phone call records. I mean you can see what you can do. Think about what you can do with all this data. Now we also have claims data, which we'll talk about in a moment, that we have claims data on our self-funded plans so I'll explain what those are. We're harmonizing all these elements because we've been harmonizing for more than seven years so you call it this, we call it that, we got all those codes working. Normalizing something called the Unified Medical Language System, UMLS, so that we got these dashboards, which I'll show you. This is proof that it works. This is about 4.3 million UC patients in the California area here, so the green is UCSF, the blue is UC Davis, yellow is UCLA, I think that color's teal? I would just call it light blue, I guess, is UC Irvine and orange is UC San Diego and Riverside, you can barely see, is in red, just a little bit north and east of San Diego. And then there's a big splotch in Nevada, that's Las Vegas, right? Of course our patients are in Las Vegas. If you're sick in this part of the world, you come to UC, right? It's not just a California patient thing. The Hawaiian Islands are covered with our colors here. Race, ethnicity, demographics, we can of course do all of that stuff. Alright, so then it gets really interesting and these are some of the new slides here. So look, we love all of our 15 million patients and the 5 million we've seen recently. But there's about 100,000 patients or so that I would argue we love even more than any others. And those are own employees, right? So we're the kind of rare kind of beast in the United States where we take care of patients but we can also have our own employees sign up for us for care, right? This is open enrollment now and about a third of our employees sign up for us for healthcare. It's obvious to this audience but like Apple can't do that, Google can't do that. You can't just have them come to you for healthcare if you don't deliver healthcare but we take care of patients but we also are a self-funded plan. This is what the benefits booklet looks like. We have a PPO and an HMO. Blue and Gold is HMO and UC Care is the PPO, and you can sign up for these and they keep getting changed every year and Optum I think is one of the providers for one of the medications for one of these plans. What we can do now is look at this set first and in fact, let me restate the obvious here, right? Whenever you're trying to change practice, right? Let me go back before you see this. Whenever you're trying to change practice, look if I use all this data and point out to a health systems CEO, this is really bad care. Why are we using this medication? It looks like there's an excess spend of, let's say a million dollars here, because we're using this one medication. If I fix that, that actually impacts revenue, right? To be crystal clear, and some of you are nodding your head so you know what I mean here, right? Yes, it's the good thing, it's the right thing, it fixes the American healthcare system but it also hurts that person's revenue. So that's hard to take sometimes when you're trying change things. But here, for these 100,000 or so covered lives, healthcare is a cost center for us. As a health enterprise, it's our cost center, right? So in other words, if one of our doctors, sees one of our employees and generates, let's say inefficient care, generates a bill? We promptly pay that bill to ourselves, right? That's basically equivalent to burning money on the front lawn, right? So this we can actually address first and try to fix. And here, to me, it seems like the health system CEOs got it, that all of a sudden we ourselves have a problem with escalating healthcare costs for our own employees, even though we take care of patients. It's a kind of mind shift there but it's one that they successfully got and so we've started to focus just on our own self-funded plans to see if we could be better as a pair, as a self-funded plan here. So here's an example of metformin, this you could really argue is true waste in the system. It's hard to find waste, waste. Branded metformin, instead of generic metformin, right? Metformin is very commonly used, obviously it's been on generic for a long time, it's a very old molecule and now we can go point by point, formulation by formulation, we see a doctor using this, this is the actual generic they should have used. Each category there is exact type of formulations of Glumetza 500 mg XL and we literally see each claim. How many dollars? How many doctors used this? How many patients were involved? And we have the phone numbers now, we can make the calls to actually change these folks off. Now, again, if a prescription's already written, I know the patients love seeing their color pill. They're not gonna want to change the color of the pill and all of that, but we have had some success here at least in switching patients from the brand name to generic. And we're doing this with Fluoxetine, metformin and one other psychiatric drug, I'm blanking on it now, but that's one way to fix it is to call people and change, right? The other is just to change it in Epic, right? So now, if you're at UCLA, if you want to order a brand name metformin there are two additional approval screens and you can see the big blue line at the top there has just plummeted. Actually it's the yellow line, it starts to plummet down just as of January, so this is just about seven months data. The minute you start to look at these things and we have that insight now, that we can actually start to make these changes. So you can't just write a prescription for brand name metformin anymore at UCLA unless you go through these screens and of course that little nudge just brings that down. And so we're piloting a lot of these with our own system here. But of course the intent is to fix it for our own self-funded plans, but oh yeah, by the way, we're fixing it for everyone here, right? This is not just for our own employees. We're gonna put this in, we're gonna fix this for everyone here. So this would be a clear cut lowest hanging fruit. This is so low hanging fruit, its probably spoiled fruit at this point, right? Always metformin to metformin, we're not even going crazier than that here, right? So those are easy ones. We're looking at quality of care. So by the way, just for the tech aficionados for a second here, I missed a couple points, slides, a couple back. So the central database is in a format called OMOP, O-M-O-P, observational medical something partnership. Very old concept from Columbia University with the FDA for the Sentinel Projects. What's old is new again, I see it, I'm taking OMOP because it's not Epic, okay? We're not paying anything more to Epic now for this central data warehouse. And so each campus generates their own OMOP feeds. It all gets agglomerate and harmonized and concatenated in the center. So it's nice because each campus then has OMOP experience, local docs, local researchers can get their hands dirty with their own local data and then when they're ready to scale, literally the same query, the same sequel of query runs on one campus runs in the central database as well once they're authorized and ready to scale. So everything you're seeing here is actually written with OMOP as a central backend here. This is quality, so we're in California, we're a Medicaid wavier state, right? So dollars go to the state, the state partitions it out for us but we have to report quality measures to the state. We have a quality measure system called Prime and now we're moving to a new one called QIP, so these are measures. About two dozen or so measures. And this is, for example, at UC Irvine, literally this is what the health system management can get. For example, the second from the top there is a blue bar there, that's colorectal cancer screening and the state says we should be at 67 point whatever percent and were at 68%, we're slightly above that so that bar is in blue and then each orange there means that we're not meeting the bar. So for example, as of this time, as of June 5th I think I dumped out this slide, controlling blood pressure, we don't have enough patients with blood pressure controlled as the state was hoping we'd get to here, so that's in orange there. And then at bottom there you can see potential incentive, that's a third of a million dollars, right? So each orange bar there's a third of a million, third of million, third of a million, so this immediately pays for itself. Now a lot of people looking at the high level dashboard, you can do this with Vizient, you can do this with a lot of tools, but if you click on that, you'll literally get an output like this and now it's massively blurred, right? So you can't really see anything but these are patients coming to UC Irvine tomorrow, right? Like the list there is mammography, 9:00 AM, 9:10, 9:15, 9:20. These are the patients that are missing some elements and literally time slot by time slot, send someone to the clinic tomorrow and document in Epic you've asked them if they're on opioids, if there's an abuse problem there, counsel them to stop smoking. You know, you can see four elements missing, three elements, two elements missing. A lot of people get the dashboards but because we have scheduling data we got it right down to who's coming in tomorrow, who needs to get this fixed right now, right? So we got both in the same system now, right, that's the nice part here, scheduling to the dashboards. We got other kinds of the uses of this data. These are all new uses since last year. At the University of California, right, we have five NCI designated cancer centers. We're now acting as one, where we can, and we have system problems there too, and challenges but what we realized is that the future is in getting some of these newer clinical trials, immunotherapies, CAR T cells and stuff, to patients. We are stronger if we act together in the University of California and so we announced this just about a year ago, with an AstraZeneca trial where the entire University of California negotiated for the trial. It wasn't principal investigator by principal investigator. Entire UC negotiated for that trial and then got that whole thing to work. There is a graphic we love to show, 141,000 cancer patients we saw, this was in 2016. Our best guess is probably it's triple MD Anderson. There's probably folks from MD Anderson here, we don't know the actual numbers but we're stronger and larger if we work together. That's why I love this construct. To me I'm not aware of five academic medicals centers that actually work together like we do in UC. Indeed, in this one town, there's one academic medical center that doesn't even work with itself. To get five, I spent 10 years training here, so I know. Even within the one health system and partners, will bring out and compete for patients, right? We don't compete for patients, in general, right? Because we're geographically distinct, it really makes that happen. Now there's this one avenue in Orange County where UC Irvine and UCLA are abutting and I know there's some kind of border skirmishes there but besides that, in general, we all cooperate and want to actually share. This is an example of what happens when you put academic medical centers together. I'm predicting many, many more of these happening in the next five to 10 years. Not strictly mergers and acquisitions, but these kinds of partnerships to obviously get scale. Alright so I showed these donuts last years, gotta show them again this year, show you what we're doing next with this. Now, that's kind of easy, low hanging fruit stuff. Let's talk about more complicated stuff, things like type 2 diabetes. Before I show you that, let's go back for second. So what I'm gonna show you now is on the research side on where we're going next. So obviously type 2 diabetes is a really major problem for the United States, for the world, and for us in the University of California, not just our patients but our own employees. Our own costs for type 2 diabetes care for our own employees is skyrocketing so all hands on deck for type 2 diabetes. Now I already showed you switching brand name to generic metformins, those are easy ones. What about harder ones? DPP-4 inhibitors? GLP? Some of these are expensive drugs and in general it's not really crystal clear why and when a diabetes doctor or a primary care doctor should use one drug over another. And just to be kind of obvious and kind of even almost insulting for a moment here, you know when a drug is approved in the United States, a lot of these trials are non inferiority studies, right? A drug gets approved by saying, at least we're not worse than the others, right? Very few want to pay all the money to prove they're better than all the others, right? So they're non inferiority studies. The drug is approved, people start to use it and then more studies come out. Wink, wink, nudge, nudge, it's better for eye, better for kidney, right? With not so clear data on some of those elements there so it's in our interest to actually study what do our doctors actually do for type 2 diabetes? So like I said before, we call these diabetes donuts. We used to called these diabetes donuts, then realized that's inappropriate for diabetes so now we call these lifesavers, right? And it's like a pie chart, and pie also is inappropriate dor diabetes but think of that ring as a pie chart where this is the first medication we can find us starting on type 2 diabetes patients. 12,007 patients in this ring here. A third of them are on metformin, that's good. That's what you're supposed to start patients on. That's yellow. The red at the bottom means we're starting patients on insulin, that's interesting, I guess. These are type 2 diabetes patients, we've confirmed that, I guess some doctors do that. Grays are self monitors and you got all sorts of other combinations that patients are started on. We literally have one doc at UCSF literally that said, oh I like to start them on everything and then peel it back as they go. Nowhere in any guideline does it say to do that. And then the black bar at the top are so many combinations of drugs and things, that I can't even show you all the slices here. Alright so now, this is the first choice for drugs for type 2 diabetes. Now the more I see data like this, the more I work on these problems, the more I realize that medicine is like a game, right? Practicing medicine's like a game. I don't mean to belittle having a disease. It's rotten, it's miserable having a disease but it's amazing how much our practice of medicine is like playing a game, meaning that we make a move and then we wait to see what doe the patient's disease do and then we make another move, right? It's very synchronous like this. We go on morning rounds. We write orders, we wait to see what happens that day. We write more orders the next day. Or we're gonna write orders in clinic, come back in 90 days, we write more orders, right? It's almost like a moved based game, if you think about it, and computers are good at learning how to play games so this is the first move we've made. Patient goes home, comes back in 90 days, here's the next move, right? So everyone in yellow, right, everyone in yellow that doesn't have a second ring there, they were happy on that does of metformin, we've seen them back, we've never had to change that dose. Yellow to yellow means we've changed the dose of metformin. They're still on metformin. And any color switching to any other color means we added a drug, subtracted a drug, swapped a drug. They go home, they come back in 90 days, here's the third move. They go home, they come back in 9 days, here's the fourth move. So you are four moves out in this game and then you realize, you see at the top, we have 1,600 different ways of doing this at UCSF. Right? Probably too many. Unnecessary practice variation. Can we get this down to 1,000? Maybe 500? Maybe one. I don't know one, maybe 10. But clearly there's probably some practice here. And you know some of those purple circles are DPP-4 inhibitors and all the rest are super expensive trajectories here. Tom Peterson did all this work. Oh and then by the way, because we can get this work at UCSF, boom, we can scale this across the entire University of California in just two days. On the right now you see 71,000 patients with type 2 diabetes now, right? This is now five centers of patients immediately multi-center study but then at the bottom we have 6,500 different ways of doing this at UC, probably too many here. So this game aspect is kind of interesting to me. I'm thinking about what to do here. This is new data and this is where we're going to be, I'm guessing, I'm predicting, super annoying to pharmaceutical companies of the future. We have more than five years of follow up data now on our patients and we have 71,000 patients with type 2 diabetes and now we can really ask, in our hands do we see a difference in major adverse cardiovascular event, MACE, eye problems? Do we see a difference in kidney health? BMI changes? Not seeing too many here, okay? And I'm not gonna go through these graphics here but this is a paper that's in review right now. Again, Tom Peterson did this, but what would it look like in the future if you saw something coming from the University of California, let's say in three years, an annual report, right? This is a UC report. This is our experience with every single drug, right? This year. And next year, here's the new report. Look, we're not saying anything. This is just what we've seen. Our benefits are from these drugs, right? Comparative effectiveness, looking at costs, right? I love PCORI, I can't wait for them anymore. And PCORI can't even look at the costs of the drugs, right, by law. We don't need a grant to do this because we've learnt it's in our self interest to do this now, right? We're going to do this and we don't need a grant or funding to do it. What I'm amazed is, why isn't every heath system doing this. Why is it, it's still stunning to me, that after a drug is approved in the United States, in general nobody bothers to see if it works in their patients. Right, that's stunning to me that actually we don't do this. You buy a car, you know what the features are don't you just like run the windshield wipers and stuff to see if it works? But in general after things are approved we buy these things and spend like crazy and don't actually see if they work. Now of course this is type 2 diabetes, we're gonna do this for everything, right? We see everything at University of California, there's nothing we can miss here. Yeah, and then we gotta start to predict the future, right? We're gonna play these chess games out to figure out what is the right way to play chess here. Do you make this move and metformin and then add insulin, right? Comparative effectiveness across strategies, not just drugs. Of all these 6,000 natural experiments going on at UC which ones were the right ways to go? Part of that's figuring out, if you make a move can you predict where the game is next? This is kind of cool that we can predict 90 days head of time, just the drugs patients are on, the socioeconomic status, we can predict what the A1C is gonna be in 90 days, right? That's what this is graph is, predicted/observed. Deep learning's good enough that we can tell where the A1C is gonna be in 90 days, this is a paper that's coming out. BMI as well, and we can start to make these kind of decision choices of metformin. Do we want to put patients on metformin or not? You remember the yellow bars? Half did okay, half did not. Simple decision tree works here. If your hemoglobin A1C was ever more than 8.8 or fasting glucose over a 206? You're in the red squares there. Don't even bother starting the metformin. I know what the American Diabetes Association says, but in our experience with 71,000 patients with type 2 diabetes, it's not gonna work, right? Data driven guidelines, not expert driven guidelines. We're gonna have our own nomograms of the future here to really tell where to move patients forward. Let me end with, I'm at zero time so I'm gonna end on the last minute with where we're going, my predictions here. I love to end with this. In the end I'm trying to build these maps. If you think about where you are and where you want to go next, in some ways that's a map. My job now is to build these maps of death and disease in California. How many of you use Google Maps or Waze or maybe Apple Maps? Nobody uses Apple Maps, my car makes me use Apple Maps. You know maps take you to pleasant destinations. I'm making a map of how you get diseases and die in California, the opposite of Google Maps. So Hannah and Jay have done this work. You can see patients showing up on the top left with alcoholism, each arrow is a year and then they zip to the right there and they get cirrhosis, and they zip to left and get liver abscess and you can die. The squares mean you die of these diseases. You don't die of alcoholism. You die of cirrhosis. Maps get more complicated. These are maps learned from our data, you show up with a heart attack on the left. You get heart failure within a year at the top. You got lung diseases because fluid backs up, that's the orange square and on the right we see patients dying of sepsis, you know? That's kind of interesting. I'm a pediatrician. I admit I didn't take care of many patients with heart attacks but I always thought it was the heart that killed you in the end. Indeed, many of our patients in California die of the sepsis in the end. That's because if you don't take the northern route with heart failure, you can take the southern route and kill your kidneys and end up in sepsis a year later, a year earlier. So the idea is to figure out where are our patients, learn from the data, figure out what's gonna happen next. But you know, it's nice and pretty to make these maps. The point isn't just to make the maps. The point is actually to show where are our patients on the map. This is a real prototype with real California data. This is literally as patients age, how Californians move from disease to disease to disease to death. The colors are getting brighter, the ages are going up, a whole bunch of them are gonna get sepsis there and hit that purple circle and die. There they go. It's okay, everyone chuckles here. And now we can start to predict, in real time, what's gonna happen in the next 90 days. What's gonna happen in the next year and what are we gonna do about it? And that to me is gonna be the new definition of an accountable care organization. One that knows how to account for the care of each one of it's 15 million patients and that is what I'm really proud to be building in the University of California. Thank you very much.
