Dale Sanders is a leader in the area of applying data to improve outcomes across healthcare, however, he sees a potential to do this in a manner that may become a burden on healthcare practitioners. I always learn from Dale, hope you enjoy.
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Bill Russell: 00:48 Welcome to this week in health it where we discuss the news information and emerging thought with leaders from across the health care industry. This is episode number number 20 it’s Friday, June 1st today we do a deep dive into the world of healthcare Ai. This podcast is brought to you by health lyrics. Visit healthlyrics.com to schedule a free consult. My name is Bill Russell, recovering healthcare CIO, writer and consultant with the previously mentioned health lyrics. Today our guest is Dr. Anthony Chang, one of the leaders in healthcare Ai. Welcome to the show. Thanks to bill. Um, I’m going to give people a little bit of your background. We are, first of all, I’ve got to give context to our background here. So we are at the Innovation Institute in Newport beach on the seventh floor overlooking the Pacific Ocean. And, uh, and today I think you could see Catalina. It’s kind of Nice and we have some guests in the audience our first time.
Bill Russell: 01:38 We’ve had guests in the audience. Yeah. Dont Clap. Uh, so let me give people a little bit of your bio real quick here. So, uh, Dr. Chang attended, John’s Hopkins for his in molecular biology prior territory, Georgetown University School of Medicine, uh, for his MD. He then completed a pediatric residency at Children’s Hospital, a national medicine center, and his pediatric cardiology fellowship at Children’s Hospital Philadelphia. He then accepted a position of attending cardiologist in the cardiovascular intensive care unit at Boston Children’s hospital. And as assistant professor at Harvard Medical School, uh, he has been the medical director of several pediatric cardiac intensive care programs including children’s Hospital of La, Miami Children’s Hospital, Texas children’s hospital as well. He served as medical director of the heart institute at Children’s Hospital. He serves, are you still serving as the, as the director of the Heart Institute of Children’s Hospital of Orange County? No, you’re not. So what’s your role at Orange County right now?
Anthony Chang: 02:39 Um, well I’m the chief intelligence and innovation officer and I’m the medical director of the heart failure program here. And I’m also the founding director of the and Disney Lund supported medical intelligence and innovation institute or MI3 for short.
Bill Russell: 02:57 So you have the three or four jobs going right. It has a lot of fun. And the reason we’re here at the innovation institute as you had as a team meeting today, here you’re talking about the AI med conference and, and this year you’ve been at, I’m on three continents and you’re looking to expand that even further. In fact, I’ll give a shout out at the end of the, at the end of the episode for the website cause I went to the website and you have one of the few conferences and you put up every piece of material you can on that. And it’s, it was great. As I was preparing for this, all the, uh, all the slide decks are there and all the background, it was really helpful.
Anthony Chang: 03:36 And the reason we do that as opposed to I think most meetings, I only put up a teaser trailer for a talk, whatever. Cause we really want this to be a widely available around the world.
Bill Russell: 03:46 Yeah. You’re almost like open sourcing AI knowledge. Correct. Which is, which is phenomenal. Correct. Um, so you completed it, you completed your Mba as well from Miami School of business and graduated with the Mccall Award for academic excellence, a complete your masters in public health in healthcare policy at the Jonathan Fielding School of Public Health at Ucla and graduated with the Dean’s Award and academic excellence. And you graduated with a masters in science and biomedical data science with a sub specialization in artificial intelligence from Stanford School of medicine. And you’re also a computer scientist in residence and a member of the Dean Scientific Council at Chapman University. Um, so do you have a student loan debt then?
Anthony Chang: 04:32 Actually, I’m proud to say I don’t have a penny of it. That’s amazing. That’s why the MBA came in handy.
Bill Russell: 04:37 And, and you’re, uh, you’re, you went back to Stanford. That was, there was a little bit of a time period between there. Did you like all of a sudden decide, hey, AI is where it’s at and I need to know more. And Stanford was the place to go.
Anthony Chang: 04:49 Yeah, basically as a, um, senior pediatric cardiologists, I realized from going to meetings and giving talks and writing books on the topic that a lot of decisions, a lot of the management issues are not always data, data science driven and that, um, senior physicians tend to espouse their own principles without sometimes data science or artificial intelligence. And a lot of the controversies in my 30, 40 years have not been resolved. So I wanted to resolve them and think about another dimension as Einstein says, you know, insanity is doing the same things over and over and expect a different result. And I feel like we have these um, debates at various meetings year in year out, but no one ever resolved them. And I felt like, well, maybe I should think about this differently. And from a data scientist perspective,
Bill Russell: 05:53 it’s really good as we start to talk about artificial intelligence when, um, when we really start to bend the paper for people, they, they almost get nervous because things are going to significantly change. And we’ll talk about the different levels of artificial intelligence and where we’re really at the hype curve and those kinds of things. But it, you know, once it gets through the hype curve and the in the trough and what we’re looking at out there is pretty fantastic in terms of precision medicine, in terms of robotics, in terms of
Anthony Chang: 06:24 just overwhelming in terms of what the dividends can be. Um, I know you have a lot of CIOs in the audience and I think 10 years from now we’ll be talking about chief intelligence officers, different healthcare organizations, and not only chief information officers or that job will be just transformed into someone that understands the intelligence as well as the data
Bill Russell: 06:49 As if you didn’t have enough going on and you have a Tedx talk here, singularity university faculty. So did you speak at singularity university that their, uh, their conference down in and
Anthony Chang: 07:01 I have some, a couple of times they’re good friends with Daniel Craft. Yeah. That’s a phenomenal conference. I, yes. It’s a beautiful venue. Where’s your, you have one in Laguna, right? Right. Are Us, um, Ai Med meeting is usually in southern California in the last two years. It’s been at the Ritz Carlton Resort in Dana Point. And that’s awesome. But I think when we talk about something as intense, uh, and esoteric, as you know, artificial intelligence, it helps to have that wide expansive ocean in front of you to decompress when you’re brain brain is hurting from all that important information from smart people.
Bill Russell: 07:45 And did I read this right to, did I see this right, that you actually did like a shark tank out on the beach? Uh, we had a slide about that and people were buying to go on the beach, but we did have a shark tank event, um, where we had five startups in AI, in medicine, health care space pitched their companies.
Anthony Chang: 08:05 Man, that’s, that’s, that’s a lot of fun. And then you’re also CEO, cofounder of two startups. Uh, do you want to tell us a little bit about those two cheery, the two startups? One is cardiac genomic intelligence and it focuses on precision medicine for cardiovascular disease. Since I’m a cardiologist and my co founder, Doctor Spero Moses is a genomics phd, we thought that was a natural, um, coupling up our strengths. And then, um, medical intelligence is a resource company for artificial intelligence in healthcare organizations. So if I’m a healthcare leader at, I don’t know where to go with this. I give you a call, you’ll come in and sure it can work with us. I mean, do you have a team there or is that we were putting together the team and then as you can imagine they’re not a lot of people well versed in this space. So we’re trying to put together thought leaders in AI as well as an it. I’m putting together this team of I think a incredible people.
Bill Russell: 09:01 Um, so great. So you offer a service for healthcare leaders, but you’re going to bring, you’re going to bring me and our listeners up to speed. So here we are, um, a crash course in 20 minutes, crash course, 20 minutes on Ai. I’m going to know everything I need to know. Um, actually I looked at the materials on your website. There’s no way to know everything there is to know. I see that there’s sub specialties and people are really focusing in on cognitive, general, intelligent. It just isn’t that different than a doctor being a subspecialist basically. So yeah. So we’ll, we’ll see what we can do. So here’s what we’re going to do. I’m going to, I’m going to play the healthcare executive and we’re going to, the frameworks would be three things. One is primer on why AI, what it is, what its potential is. Uh, the second area we’re going to look at it as, uh, where should I be thinking about it and where is it in use today? And then the third is how do I get started and how do I, how do I get started? How do I stay relevant in this space? So let’s, let’s start at the beginning. What’s the promise of Ai? Why, why are you so excited about it? Why are you looking at it?
Anthony Chang: 10:03 After four years of education, I realized that it’s kind of like wearing a different Lens and looking at the world, um, you see so many little places where data science or computer programming can really make impact. And if you think about our world in general, so many facets of our life are already being replaced by automation. Um, how we order books and how we get referred books. It’s all algorithm driven. It’s not perfect, but it’s pretty good. Um, how we, uh, have now autonomous driving vehicles now, at least in the picture. And I think 10 years ago, if you were to ask people, do you think there be autonomous driving vehicles? And everyone will be thinking this is like 20, 30, 40 years down the road. But it’s not uncommon to see one in, in Silicon Valley now. And I think there’s so many aspects. And then it was February 14th, 2011 Valentine’s Day. So it’s a special day for cardiologists that the human contestants were beaten soundly by the super computer, Watson from IBM. And that was the night that I downloaded the application for the data science and AI program at Stanford. I realized that, um, this time AI is here and it’s probably going to be here for a long time.
Bill Russell: 11:24 Yeah. Knows those stories. Just continue. I mean, it’s, you know, one of the things you shared was, I think I have a picture of this. Yes. So, yeah, and I’ll, I’ll put this up on the screen. So how a robot pass China’s medical licensing exam. Yeah.
Anthony Chang: 11:37 I think going, he knows about that. Um, so it’s a, uh, obviously a robot with what we call natural language processing capability and understanding. So NLP that you hear about a lot is natural language processing or a computer can understand the human spoken language or written language and then Nlu which is even more important is understanding. So obviously just like Watson on the show, Jack Pretty, this Chinese robot is able to understand the context and the content of the questions in a medical exam and paths
Bill Russell: 12:14 but still not ready for, we’re not going to put that robot into a clinic and have people go see it just yet.
Anthony Chang: 12:20 No, think again, I think one of the misconceptions is that AI is all about the robot and it’s not helping that the public media is often having pictures of robots and then having an AI, a headline and what I say and I, my intellectual partner, a spare mousses and I kind of made it up together, is that AI is about making the visible invisible. So by then I mean if you walk into a doctor’s office and you sat down, inevitably that physician’s going to be tapping away on a computer and not paying attention to you 100% of the time. I’m lucky my doctor doesn’t do that. But, but, um, in my exam rooms, um, computers are not allowed in the exam because I think that’s distracting. So if AI is really good at some point in the near future, then the entire conversation, the exam, we’ll all be extracted onto the medical record automatically with AI and LP. And you wouldn’t need the doctor tapping on the computer. So it’s making those things go away.
Bill Russell: 13:25 Yeah. Are you, you in the last conversation we had, you likened it too. Uh, the, the, the doctor isn’t a Sherlock Holmes. It’s more like Watson, right? It’s your system. It’s there to make the invisible visible, right? It’s going through reams of data, genomic data, whatever the data and it’s coming back and things that you couldn’t read if you took a week. It’s saying, right. Just process this
Anthony Chang: 13:48 making, that’s the, um, uh, sort of the corollary, which is making the invisible visible. So now, um, medical information is doubling at an incredible rate every three to four months or so. So there’s no way a doctor can understand all of the medical literature pore through all of the patient’s record, even if it’s just the record in front of him or her. Um, and then come up with the best decision. So we have to make the invisible visible by data mining and data science. That’s amazing.
Bill Russell: 14:18 So you give, um, again, this was a great slide for me. You gave us three, three different categories. Can you give, you know, just break it down for us so that we can think about it, right, because it’s not just the robot and the cars.
Anthony Chang: 14:31 So basically artificial intelligence can be sort of sub divided in a number of ways. One way we can subdivide artificial intelligence, which is the generic general term, is think about the different types of artificial intelligence. So there is the assistant artificial intelligence, which means the machines doing the work. It doesn’t really interact with the humans. And it’s something that’s symbolized by the, um, the robotic vacuum cleaner that we have now. Oh, so it’s like the Roomba bump correct. Each thing until the writers out the room. But it’s on sort of automatic pilot is doing its own thing. The humans don’t need to teach it. Um, the intermediate kind of artificial intelligence is something called augmented intelligence. So that means there’s an interaction interplay between the human and the machine. So the example, um, would be something like the, um, uh, book choices that Amazon may have for you.
Anthony Chang: 15:31 So that’s an sort of an augmented intelligence machine learns what you are doing and it teaches itself to relate to you in that way. And then you give a feedback. So it’s a constant interactions like RPA fall in. Okay. So like we’re, we’re, we’re actually telling the machine here, right? There’s the redundant repetitive tasks that I normally do. I think RPA, and it’s a very popular term right now, robotic process automation, RPA. So for the clinicians out there, it’s not right pulmonary artery anymore. So Rpa is I think somewhere between assistant and augmented. And as you know, we have a computer science phd here sitting behind me. So, um, I think he can, uh, give you his opinion too. But I think it’s automated in the sense that would just tell me a machine what to do. But there is a little bit element of learning there.
Anthony Chang: 16:26 So, um, I think it’s mostly automated but perhaps an element of assistant. And then, um, the third category sort of is the autonomous intelligence category. And that’s symbolized by the autonomous driving vehicle. We don’t have anything like that in medicine yet per se. So in medicine there’s only assisted, uh, AI. Um, and that’s something like a robotic, um, pharmacy service where the robot is on delivering medications, augmented would be some of the decisions, support, um, medical image, computer vision type of project. So I think that by far the most exciting areas is going to be in the next decade or two is going to be autonomous AI in medicine.
Bill Russell: 17:12 So you told two stories. One was the, the robot passing the exam. You can see the other you told, was the machine actually beating the champion that falls into that category?
Anthony Chang: 17:28 Yeah. That will be more autonomous because the computer is playing a game on its own it
Bill Russell: 17:34 and figuring things out, looking at, looking at, you know, years of playing and then saying, okay, this is the best way to do it. Right. That’s what we can look forward to. Is that, yeah.
Anthony Chang: 17:43 Well except the, um, so that’s a great story about the Alphago software program. Google being the human contestant and go ah, so handily actually, um, but, and everyone publicize the second game 31st move because it seemed like the computer made a move that it had not learned from any human based on hundreds of thousands or millions of games before it was like the move out of nowhere and then, but yet that move was instrumental in winning the second game. What’s not publicize is in the fourth game, a, one of the moves the human champion made was sort of in the category of that really creative move. So some eerie moments there, right. Cause he be the computer thought or it was creative for the first time and now the man, the human champion is learning from the computer.
Anthony Chang: 18:39 But I think that’s a wonderful, uh, example how man and machine can learn from each other.
Bill Russell: 18:44 Right? It’s still the human brain is, is unbelievable. I saw some slides on, on it and it talked about, you know, processing power, the extra bite, right? I mean that’s just the, yeah,
Anthony Chang: 18:56 well the number one takeaway for me after learning for four years in school is just how amazing and how cheap the human brain is.
Bill Russell: 19:07 Actually. The more you dive into this, you realize the things we’re trying to get computers to figure out and to learn, which is an amazing thing in and of itself. You know, an infant is doing the right crawling around it. So we really are at still at the beginning stage of this.
Anthony Chang: 19:22 Right? Well, it’s, um, I think it’s really interesting analogies. One is, you know, when man wanted to fly, it looked at birds and in the Greek Times that people even got dressed like birds thinking that that’s all you need to fly.
Anthony Chang: 19:38 And in fact, we need to understand aerodynamics and then build machines that are now flying much higher, a much faster than birds. And I think I see that for AI. So Ai Right now, um, and I disagreed that people say all the time, computers are fast and stupid and humans are slow, but smart, I think we are getting to, you know, middle ground where we both can contribute to overall intelligence. And one of the things I said at a recent interview is that artificial intelligence plus clinician intelligence equals something new and special medical intelligence. Because when do you want a man and a computer combined to take care of you in the future?
Bill Russell: 20:21 Absolutely. Because the computer can process every New England Journal of Medicine. Right, right. And have that readily available. 6 million pages and second. Yeah. It’s, it’s unbelievable. So I, you know, some of the pushback you’re going to get is here’s the Gartner hype cycle. It just about every single of those left curtain say, ai. Uh, so it really hasn’t gone through the, uh, the virtual reality is
Bill Russell: 20:48 coming out. Augmented reality is coming out of the hype cycle. Uh, it looks like the autonomous vehicles is actually you also coming over the curve. Yes. But all these other things are just on the other side. So we have huge promise. I mean, we usually, what that says is we can see the promise, but we just can’t get it yet. Right.
Anthony Chang: 21:04 And I think this is a wonderful graphic that you have just before showing me some of the evolution of Ai let the last few decades and in the future. So Rpa, which is making a comeback ironically has been around for a long time. We just haven’t really sort of, been reawaken to the potential of RPA and something about AI that’s good to remember is, you know, don’t go for the moonshot project with millions of dollars. In fact, that the biggest and the fastest Roi could be the mundane project that a little RPA can take care of in your revenue cycle or in your decision making.
Bill Russell: 21:43 Absolutely. At the JP Morgan Conference, Tony Tersigni a CEO for Ascension got up there and started talking about Rpa and how they applied it to their call center and saved millions of dollars. In fact, what was interesting to me is he said, not only are we taking this service that we now done to other health systems, we’re actually doing it for organizations outside of healthcare because they had done such a good job with. So and actually, when people say to me where I should start with AI, we’re getting ahead of ourselves, but where I should start with AI, I rarely point them in the clinical direction high, almost always point them in. Um, you know, claims processing, fraud detection, security, there’s so many areas you can point at it that it’s not life and death yet. Um, not that you shouldn’t be looking at those other cases and we’ll, we’ll get into those. But there are a lot of places that improves the cost of healthcare. Improve access.
Anthony Chang: 22:36 Yes. It’s sort of like multidimensional. So if you think of artificial intelligence as a orchestra right now, machine learning, deep learning is getting a lot of attention. So I sort of equate that to the string section. And obviously there are many sections of the orchestra. So someday soon, um, hospitals, healthcare organization’s going to be able to have amazing music with all the sections, playing in a wonderful symphony and also together, because right now there’s a lot of cacophany, everyone’s doing their own thing. Um, you can have it in the same hospital. You know, perhaps the management is doing a little RPA and not know that their clinical data science team is working on the septic, a sepsis prediction model. And yet it’s all under the umbrella of artificial intelligence.
Bill Russell: 23:28 One of the things that surprises me is, I’m just going through the material from your website is we were using AI and what I didn’t really categorize it as AI, which, which kind of surprised me. Um, I’m going to show you three different things that I’ll put these up on the screen. So the first is this is the Accenture, um, diagram and it talks about the opportunity within AI in the different categories. Uh, the second one, I’m not sure what the sources for this one, but no, you’re the source. So Ai in medicine and, and uh, the, the third is hospital operations and workflows. I actually, I was wondering if you could just sort of walk us through those in terms of, um, you know, where, where’s it in practice today and where, where should we be thinking about it?
Anthony Chang: 24:12 Yeah, I think I’m looking at the Accenture slide for the first time, so I have respectfully have some disagreements. I think AI is really up to our imagination in terms of what the return’s going to be. Um, I don’t think anyone can just tell you right now is I think these are perhaps projected projected. Um, but I think for instance, I think the area of cyber security could be much bigger because I think as we learned about data protection and cyber security, there’s a lot of, uh, instances where we need to have that, especially if we’re going to share data so that I think that’s under rated
Bill Russell: 24:50 great. If I could drive that one home, we, we had implemented a lot of tools and each one of those generates log files. The log files got so massive, I couldn’t build a big enough team to go through those log files. We had to implement, um, uh, the, the ability to essentially a big data store and then the ability for computers to identify anomalies and pull those out, which is a
Anthony Chang: 25:12 right. And I think if you look at automated image diagnosis and this ties into the radiologist’s concern that they may be out of a job. Um, and I think medicine imaging is exponentially increasing in volume and complexity. Um, most of medical imaging data, it’s really been for me, if you look at just sheer volume has really been generating the last three to five years. So if you can see that exponential curve, there’s plenty of work for everybody, man and machine combined. So I think that’s going to be a growing area as well. And then the future areas, RPA will be really big in terms of helping with the administrative burden as well as, uh, I think the eventual sort of AI promised land is going to be using cognitive architecture on top of deep learning so that you have something I, I called deep thinking, which is sort of in a way, barring Garry Kasparov’s book title, but I thought, you know, deep thinking or deep cognition is going to be what medicine’s going to need because in the game go was impressive that the, uh, deep learning software was able to be the him contestants.
Anthony Chang: 26:23 But biomedicine and healthcare is kind of like playing hundreds of games of go with thousands of players and the board configuring is changing. Uh, it’s, it’s much more complicated than a simple game go, even though it’s not simple.
Bill Russell: 26:39 So what should I be telling my healthcare leader? What should I telling my radio? So computer vision now can read these images of the fact that, you know, again, another slide here, algorithm better at diagnosing pneumonia than radiologists. Yeah. Right. So, um, the other thing I heard about, you know, looking at these images is that the computer can look for a hundred to a thousand things, whereas usually the, the cardiology or the radiologists anyway is looking for one specific thing or whatever. It isn’t looking for some of those things. And so even having a computer go back and look at all these images could identify some things that weren’t found.
Anthony Chang: 27:20 Well, we could reassure the radiologist is that you actually need both man and machine to have the best results. And I think, uh, the machine can help the radiologists by relieving leaving the burden of particularly to normal studies. And I think radiologists get focused on what’s abnormal and what the repercussions of that finding what the, which does it really is moving them up, just practice at the top of their lives. I think so. Yeah. Um,
Bill Russell: 27:47 and this is what we’re seeing today, even with autonomous cars, right? So we’re saying, okay, we’re going to, I was in an Uber car and the guy’s like, oh, these autonomous cars are going to put me out of business. And I’m like, well, not for the foreseeable future. I mean, even the, even the autonomous cars and in Phoenix they had a pilot going, uh, they had drivers behind the wheel because there are going to be situations that the computer can’t figure out. We’re not sure. Uh, you know how long that’s going to be before the computer. Right. It takes that step.
Anthony Chang: 28:16 Um, but having said that, the humans need to be alert though. Yes.
Bill Russell: 28:20 As we found out in Phoenix, in Tempe, actually the, uh, so if the same thing’s true here, we’re, we’re not at a point where I’m going to go there yet. Just have the computer give me the read. I’m good with that. I’m still gonna if, if there’s something serious I’m still gonna want,
Anthony Chang: 28:36 right. I’m going to want someone to look at that. Well, we did an interesting audience survey, um, about the question and the question was, would you, who would you trust with interpreting your cat scan? And the choices were radiologists alone, machine alone, radiologists and machine. It was 85, 90% one at all, both parties to be reading the CT. So give us some,
Bill Russell: 29:06 give some clinical examples. So your, your pediatrics, so where’s, where’s Ai being utilized in pediatrics today?
Anthony Chang: 29:15 Well, there’s some exciting areas. One is realtime analytics in the pediatric cardiac intensive care unit, which is as you know, my domain previously. So we could look at, uh, take all the vital signs from the pediatric patient after, before, after heart surgery, particularly during the unstable periods. And we can actually forewarned clinician what the, uh, the composite is telling us in terms of impending deterioration or cardiac arrest. So obviously the key to successful patient care is the avoidance of cardiac arrest. And that’s a very useful too. And if you combine that with other, um, information like in the electronic records and Lad Valley’s, that’s going to be immensely useful in the future as well. So that’s just one snapshot example of how that can be used.
Bill Russell: 30:09 So if I’m a hospital administrator, I should be looking at, uh, imaging today. So let’s get to, let’s get to the pragmatic part of it, which is I made you CEO of a health system. Where are you looking at? Where were the first couple of places you’d be looking at it? What would you be doing with your team to maybe bring them up to speed on what’s going on? Those kind of things?
Anthony Chang: 30:38 Well actually in the process of coming up with what I call him Ai score for a healthcare organization because that’s a great question. Like where do you spend the money to get not necessarily the best return but the best value? Right. And I think ironically, um, I tried not to look at AI projects because I think the, one of the major areas of deficiency as we think about AI projects is actually the quality and the management of the data right. In healthcare. And they think you remember I said that before? So I think my first investment, it just like if you look at the pyramid, the graphic showing, the bottom being the data, the next layer being a information on top of that is knowledge and then above knowledges, intelligence or wisdom. So I think in order to do good intelligence you wouldn’t want to do a lot of projects and intelligence and realize that the foundation data layer is actually problematic because that’s going to topple. So I would build a very strong foundation, make sure your data is um, very sound from the it perspective. And then the it, the Ai part is actually quite straight forward. And just someone who get someone well versed and they I projects I will focus on medical imaging and decision support because those are now maturing as areas of AI in the healthcare domain.
Bill Russell: 32:02 What are, what are some of the, alright so I’m the CIO, you’re the CEO, you’re looking at me saying how good is our data? Right? And I’m telling you, you know what, some of these physicians are not great data entry clerks or data’s really all over the place. Not only that, a lot of the information we’re getting is from our clinically integrated network and they don’t even work for us. And so we have all this data coming in. So, so I have a data cleanup project. That’s one of the things I have to figure out in order to get that data ready, uh, for these projects. Um, but there, there are some datasets that are really good. Right? Right. So the financial data set is typically relatively clean. The, uh, uh, the monitors, the bedside monitors, I mean, do you use that data? I mean it’s, it’s, uh, you know, you have a time series data. It’s, you know, a lot, a lot more data points, right?
Anthony Chang: 32:56 No, I think though, the, that’s exactly right. The ICU monitoring or data is fairly straightforward as relatively complete. And in terms of not having many missing points of data, there’s even publicly available Icu data and adult ICU is called the mimics three that’s available. So, uh, and you can easily do projects without actually involving your own patients. So they are publicly available databases and the healthcare and just not many. Ideally, you know, if you were to ask me 20 years from now what I would like to see, like to see every patient data imported into the cloud and it’ll be universally available for any healthcare stakeholder to look at that’s anonymized. Um, perhaps with blockchain or other types of security, um, mechanism and it’ll be all available because that’s going to help fix 20 years. I’m very optimistic that will be tackled within 20 years.
Bill Russell: 33:54 So you’d like to see, um, you use essentially what your belief is that within 20 years we’re going to have a AI machine learning deep intelligence in, in the ability to point these things at that Dataset and come up with all sorts of new thought processes in terms of how to, how to attack certain disease states.
Anthony Chang: 34:17 Well I call it a, um, perhaps like a clinical gps for the clinicians. So they can actually think perhaps even more creatively than the gps as you would in a driving situation. You liked the gps but you may, may not, you know, want to adhere to it. So, and also occasionally, like it happened to me just a week ago, the gps wasn’t working, so you have to now rely on your human intelligence to get you,
Bill Russell: 34:44 my kids laugh at me cause I get in the car, I put gps to our home. They’re like, you don’t know how to get home
Anthony Chang: 34:49 right now. I’m just more comfortable with the GPS. Yeah. Well, and that’s how I like physicians eventually think about AI in medicine is it’s a gps that you just got to get used to it. Your routine. It’s not something that is so esoteric or advanced that you can’t understand. It’s going to be there quietly as your partner. Um, if you look down a Microsoft commercial 25 years ago, um, there was actually a segment saying, you know, imagine the future. And I laugh because it says imagine a future traveling across the country without fold out maps. And people were laughing, they just did not think that was going to be possible. And now with gps, you don’t think twice about driving across the country? No.
Bill Russell: 35:33 I pulled out a foldout map, uh, not too ago with my kids in the room and they’re just like, what is this? Is that a new puzzle? What are you doing
Anthony Chang: 35:43 within 10 or 20 years as more clinicians are getting educated and aware of Ai. I think it’s just going to be part of their routine. And I, that’s why I like to see it’s actually embedded in their clinical routine without disruption to their workflow, without any distraction from their usual routines and just be the silent partner.
Bill Russell: 36:06 Do you think we need to change how, um, how doctors are educated? Do you think we’ll start to see AI start tip built into those programs?
Anthony Chang: 36:15 Yes, I think, um, I’m starting to see clinicians that are becoming more seriously interested in data science and um, the younger generation considering dual education in both clinical medicine data scientists. Do you think like Hopkins and Stanford? My Alma Mater, um, ironically at Stanford I didn’t meet too many doctors in the data science program, but I think, uh, just right, just as clinicians that may want to go back to school and get a data science, um, education to be more of a specialist in this area. Like other specialties. I, interesting now that a young person was a data scientist. Who got so interested in the health care, she’s thinking about going to medical school. So it can be the other way around too, which is great for healthcare, that we have a cohort of young people that are going to be dedicated to data science. I couldn’t be happier to hear that.
Bill Russell: 37:13 Yeah, that would be phenomenal, so again, I’m playing the healthcare administrator. I’m going to say, you know, this last year I’ve had five different vendors come in to buy it. I mean, how should I be thinking about these vendors? You know, cause even some vendors from outside of healthcare coming in and going, hey, we’ve done this in other industries. The complexity of healthcare is such that I’m not, I mean, I’m not saying they can’t do it, but I’m just saying the learning curve for them on the, on the healthcare side, it’s pretty high. How should I think about that?
Anthony Chang: 37:42 Yeah, I think be careful and be very vigilant for then there’s that over promise and under deliver. Obviously. I think there’ve been some very big companies that, um, you know, that over hyped and under delivered and actually became unsuccessful with those big institutions. So I think, um, those are cautionary tales. I think we need to pay attention to and I think invest, you know, a relatively small amount of revenue into sort of this space and get to and use it cautiously and as, and as a way to learn about the limitations of AI in this space. But I think the promise is tremendous and I think the future is very bright for this area. But I think, um, you know, don’t overspend your resources now and just get to know it and education is key. I think
Bill Russell: 38:36 it’s a, it’s the same thing we did around big data. It’s, it’s small projects, uh, uh, with defined outcomes and you give those vendors like tasks to do and once they do it, you can then expand it and grow it. Um, is you’re going to be a problem scaling AI at this point?
Anthony Chang: 38:54 Not at all because I think, uh, the, the, um, potential is really limitless because of the computational power of being so cheap now. And so many algorithms are maturing and I think it’s going to be an amazing portfolio, things that are available. And I think if you look at how inefficient and how badly run healthcare is, it’s just going to be amazing transformation the next 10, 20 years.
Bill Russell: 39:19 The other thing that’s amazing to me is how much a open source there is out there. I mean, Google’s open source, right? You could tap into Amazon, you can tap into a Microsoft. There’s a lot of ways to tap into this without building it out on site. So there are, there are inexpensive ways to play around with it. You have to get your date of right and get it to the right place. Once you do, you could do some things.
Anthony Chang: 39:40 I think, um, as the common saying is you don’t have to be an engineer to drive a car. And I think that’s valid for this too. And, but I do think he needs to understand the general mechanics of the car and also the, the rules of the road. Just like with any, just like with Ai,
Bill Russell: 39:56 this is one of those things where the technologists and the clinicians are going to have to come together. And what, um, I think I saw one of your slides. I’ve CHla, the, the, uh, data scientists actually goes on rounds, right?
Anthony Chang: 40:08 Yeah. That was, I’m a very strong advocate of that. Our data scientists, um, at choc will see patients with me in a clinic and truly understand the clinical culture, just like I did with my school years where I spent a lot of time with computer scientists.
Bill Russell: 40:25 So what will the scientists get pull out from that. What will they, will they identify something and go, Hey, we could, we can run the numbers on that. Right,
Anthony Chang: 40:33 exactly. Or they, uh, understand the nuances that when we have health care data, it’s not as exact as they might think it is or why data is missing. And so I want them to focus not on just the AI potential, but also the acquisition and management of data.
Bill Russell: 40:50 So our tagline for the show is for the next generation of health it leaders, right. What am I going to do for my, my team? How am I, I clearly not everybody is good to get up to speed on this, but how, how do I [inaudible] what are some resources? Obviously the AI med website. What are some other ways I can get up to speed?
Anthony Chang: 41:10 Well, I think a med website’s going to have the, the Ebook I made publicly available for free. That’s a good start. Has a glossary of about 400 words. If you want to look up words, we’re in the process of producing a short video series on hot topics. Like what’s the difference between AI and deep learning, things like that we’ll have available by the end of the year. Um, there’s a, um, an academic magazine, I call it academic magazine because it’s not boring to read like an academic journal at the same time. It’s very entertaining with education. So in academic magazine is going to be free also to everybody on a bimonthly basis, we focus on a specific theme every other month. So if this was all through Ai med, this is all through us and we want to be, um, your sort of educational source for AI in medicine and their breakfast briefings all over the world. They will be seminars starting here in Orange County, but that will also spread all over the world. So I think just a sense of awareness and some education will go a long way. So I would also recommend that every person in the hospital just have some awareness that this is sort of the new paradigm in medicine.
Bill Russell: 42:28 And of course you’ll start your new podcasts here shortly. It will. All right. We’ll all tuned in. Yes, that’d be great. Um, well thanks. Thanks for your time. I, I’m actually going to interview your friend here in a minute, but I’m going to do the close and then we’ll all can’t come back to this. So, um, how can people follow you? Are you on social media or?
Anthony Chang: 42:48 I am, I’m, I’m just, um, that’s maturing right now, but I think the best source is still the website. Yes. Ai-med.io. It pretty much has everything to get started. Has the Ebook w magazine that you can go through. It has all of the upcoming activities loaded, um, most likely in your, near your region. We have three big meetings this year in three continents and we’ll go to five continents next year or so. You’ll be seeing a lot of us.
Bill Russell: 43:17 Yeah. Well I’m definitely going to look you up down in Dana Point. I know when it came up last year, somebody asked me to go when I was out of town. So,
Anthony Chang: 43:26 and also I’m finishing a book project with Elsevier. So there’ll be a textbook that’s written for everybody cause I don’t want this to be read by only clinicians or the data scientist. So I’m trying to write it for everybody. And uh, with enough,
Bill Russell: 43:40 you were running it for me. A book. Is it?
Anthony Chang: 43:43 Oh, have you a picture next to me when I write from now on? No, I think I’m one I’m really happy to see is there’s no longer a emotional pushback and there’s more now a natural curiosity and a sense of wonder about what this can be someday. So I’m happy to see that.
Bill Russell: 44:02 Yes, it is exciting. So, uh, uh, you know, just to close out, you can follow me on Twitter @thepatientsCIO, my writing on the health lyrics website and healthsystemcio.com. Don’t forget to follow the show @thisweekinhit. Check out our website thisweekinhealthit.com. Uh, if you like this show, take a few minutes and, uh, give us a review on Google play or iTunes and you can catch all the videos. We’re now up to 120 videos on the youtube channel this week in health it.com/video or you can just go to youtube and search for this week in health it, um, that’s all. Have police come back every Friday for more news information and commentary with industry leaders.
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