Artificial Intelligence is the next frontier in Healthcare. This Week we speak with Anthony Chang, Medical Director of CHOC Heart Failure Program and founder and Chief Intelligence Officer for the Sharon Disney Lund Medical Intelligence and Innovation Institute (MI3) and an M.S. in Biomedical Data Science with subspecialization in Artificial Intelligence from the Stanford School of Medicine. Wonderful conversation on the future of healthcare and medicine.

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Bill Russell:                   00:01               Hey podcast listeners, thanks for listening. If you’re enjoying this, we can help it. We just wanted to give you some information on how you can support these conversations to keep them going. This weekend. Health. Its goal is to keep you your organization and your employees updated with the emerging thought and trends in the healthcare industry. Through our conversations with healthcare and technology leaders, the best and easiest way you can show your support is to go over to this week in health it on itunes and leave us a review. Also, you can subscribe on Itunes, Google play, or stitcher, or go over to our youtube page and subscribe and hit the notification bell. Again, we really appreciate you spending your valuable time listening to this podcast.

Bill Russell:                   00:48               Welcome to this week in health it where we discussed news information and emerging thought leaders from across the healthcare industry. This is episode number number

Bill Russell:                   00:54               20. It’s Friday, June first. Today we do a deep dive into the world of healthcare Ai. This podcast is brought to you by health lyrics, visit [inaudible] dot 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. 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 we’ve had guests in the audience. Yeah, no clap. Uh, so let me give people a little bit of your bio real quick here.

Bill Russell:                   01:44               So, um, Dr Chang attended, uh, uh, John John’s Hopkins for his Ba in molecular biology prior to entering 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 of Philadelphia. He then accepted a position of attending cardiologist in the cardiovascular intensive care unit at Boston Children’s Hospital and as assistant professor of law 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. It 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? While 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 siren.

Bill Russell:                   02:51               Disney lund supported medical intelligence and innovation institute, or mid three for short. So you have the three or four jobs going right. And it was a lot of fun. And the reason we’re here at the Innovation Institute, as you had a team meeting today, here, you’re talking about the Afa I med conference and this year you’ve been at on three continents that you’re looking to expand that even further. In fact, I’ll, I’ll give a shout out at the end of the, at the end of the episode for the website because I went to the website and you have one of the few conferences that 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 talk whatever, because 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. So you completed your, you completed your Mba as well from Miami School of business and graduated with the Mccall Award for academic excellence, completed your masters in public health. And healthcare policy at the, uh, Johnathan fielding school of Public Health at Ucla, graduated with the Dean’s Award and academic excellence. And you graduated with a masters in science and biomedical data science with a subspecialization in artificial intelligence from Stanford School of medicine. And you’re also a computer scientist in residence and a member of the Deans 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 why the NBA 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 senior, a pediatric cardiologist, 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 senior physicians tend to espouse their home principles without sometimes data science or artificial intelligence and a lot of the controversies in my 30 slash 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 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 interesting as we start talking about artificial intelligence, when, um,

Anthony Chang:            05:58               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 once it gets through the hype curve and 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 just it’s overwhelming in terms of what the dividends can be. And I think, um, I know you have a lot of ceos in the audience and I think 10 years from now we’ll be talking about chief intelligence officers in 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 it is. If you didn’t have enough going on, you, you have a Tedx talk here.

Anthony Chang:            06:53               Singularity University faculty. So did you speak at singularity university that their, uh, their conference down in a couple of times. They’re good friends with Daniel Kraft. Yeah. That’s a phenomenal conference. I, yes. It’s in a beautiful venue where, where’s, where’s your, you have one in Laguna, right? Right. Our US 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 for anybody. Anybody think when we talk about something as intense 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. And did I read this? I see this right, that you actually did like a shark tank out on the beach and we had a slide about that and people were buying to go on the beach, but we did have a shark tank event where we had five startups in ai, in medicine, healthcare space pitch their companies.

Anthony Chang:            08:04               Yes man, that’s, that’s a lot of fun. And then you’re also a ceo co founder of two startups. Do you want to tell us a little bit about those two jewelry to 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 Dr Speyer and loses his genomics phd, we thought that was a natural, um, coupling of our strengths. And then, um, medical intelligence is a resource company for artificial intelligence in healthcare organizations. So if I’m a health care leader at, I don’t know where to go with this, it gives you a call, you’ll come in and sure didn’t work with us. I mean do you have a team there? Is that we were putting together the team and then she can imagine they’re not a lot of people well versed in this space. So we’re trying to put together thought leaders, ai as well as an it and putting together this team of I think a incredible people.

Bill Russell:                   09:02               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 go and crash course in 20 minutes. Crash course, 20 minutes on Ai. I want to know everything I need to know. I’m actually, I looked at the materials on your website. There’s no way to know everything. There is something I see that there’s sub specialties and people are really focusing in on cognitive general intelligence is no 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 is where should I be thinking about it and wears 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 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, you see so many little places where data science or computer programming can really make an impact. And if you think about our world in general, so many facets of our life is, are already being replaced by automation. Um, how we order books and how we get referred books is 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’ll 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 silicon valley now and I think there’s so many aspects. And then it was February 14th, 2011 start 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 ais here and it’s going to probably going to be here for a long time.

Bill Russell:                   11:24               Yeah. Those, uh, those stories just continue. I mean, one of the things you shared was think I have a picture of this. Yeah. So yeah, and I’ll put this up on the screen. So how a robot past China’s medical licensing exam. Yeah,

Anthony Chang:            11:37               I think one knows about that. Um, so it’s a, a, obviously a robot with, um, 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, Jeff Pretty, this Chinese robot is able to understand the context and the content of the questions in a medical exam and passed

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 have an ai, a headline. And what I say and I, my intellectual partner, spare mousses and I kind of made it up together is that ai is about making the visible invisible. So by that 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 a hundred percent 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 room 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 will all be extracted onto the medical record automatically with ai and NLP. 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 in the last conversation we had, you liked it too? Um, yeah. The, the doctor isn’t a Sherlock Holmes. It’s more like Watson, right? It’s your assistant. It’s there to make the invisible visible. Right? So it’s, 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 and say, just process this

Anthony Chang:            13:48               smoking. That’s the sort of the corollary which is making the invisible visible. So now a medical information is doubling and 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 and then come up with the best decision. So we have to make the invisible visible by data mining and data science.

Bill Russell:                   14:17               So you give ’em the. Again, this was a great slide for me. You gave us three, three different categories. Can you give, just break it down for us so that we can think about it, right? Because it’s not just the robot and the.

Anthony Chang:            14:31               So basically artificial intelligence can be sort of subdivided 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 machine’s the work. It doesn’t really interact with humans and it’s something that symbolized by the, um, the robotic vacuum cleaner that we have now. Oh, so like the room, but each thing until 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, the interplay between the human and the machine. So the example would be something like the um, um, book choices that Amazon may have for you. 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.

Anthony Chang:            15:44               So it’s a constant RPA falling. Okay. So like we’re, we’re, we’re actually telling the machine here, here’s the redundant repetitive tasks normally do, I think Rpa and it’s a very popular term right now, robotic process automation, RPA. So for the clinicians out there, that’s not right pulmonary artery anymore. So I’m 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 give you his opinion too, but I think it’s automated in the sense that will just tell me a machine what to do, but there is a little bit element of learning there. So, um, I think it’s mostly automated but perhaps an element of assistant. And then 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.

Anthony Chang:            16:44               So in medicine there’s only assisted ai and that’s something like a robotic pharmacy service where the robot is on delivering medications. Augmented would be some of the decisions support, medical image, computer vision type of project. So much I think that by far the most exciting areas is going to be in the next decade or two is going to be the autonomous ai in medicine. So. So you told two stories. One was the robot exam, the other you told was the a machine actually beating the champion is getting. I’ll go. So that falls into that category. Yeah, that will be more autonomous because the computer is playing a game on its own. It and figuring things out, looking at, looking at 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. Um, well except the, um, so that’s a great story about the Alphago software program.

Anthony Chang:            17:49               Google me that human contestant and go, uh, so handily actually, um, and everyone publicized 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 a move out of nowhere and then, but yet that move was instrumental in winning. The second game was not publicized is in the fourth game, a, one of the moves the human champion made was sort of in the category of that really creative. So some eerie moments there, right? Because maybe the computer thought or it was created for the first time and now the man, the human champion is learning from the computer. But I think it’s a wonderful example how man and machine can learn from each other. Right?

Bill Russell:                   18:45               It’s still, the human brain is unbelievable. I saw some slides on the med, it talked about processing power and the extra bite. I mean that’s just the.

Anthony Chang:            18:55               Yeah, well the, 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:                   19:05               Yeah, it is 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. An infant is doing crawley around it, so we really are. It’s still at the beginning stage of this.

Anthony Chang:            19:23               Well, it’s really interesting analogies. One is, you know, when man wanted to fly, it looked at birds and then the Greek times that people even got dressed lightbars thinking that that’s all you need to fly and in fact we need to understand aerodynamics and then build machines that are now flying much higher, much faster than birds. And I think I see that for ai. So Ai Right now, and I disagree 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 you want a man and a computer combined to take care of you in the future.

Bill Russell:                   20:21               Oh 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, you know, some of the pushback you’re going to get is, here’s Gartner hype cycle at just about everything on this left curtain. So it really hasn’t gone through the, uh, the, the virtual reality is coming out, augmented reality is coming out of the hype cycle. Uh, it looks like the autonomous vehicles is actually also coming over the curve, but all these other things are just on the other side. So we have huge promise that, 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 had just before show me sort of the evolution of ai would 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. We’ve sort of been. We awaken to the potential of RPA and something about ai that’s good to remember is don’t go for the moonshot project with millions of dollars in fact, that the, the biggest and fastest Roi could be the mundane project that 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 essentially 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, you know, 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 it actually, when people say to me where I should start with ai, we’re getting ahead of ourselves, but where I should start with the, I, I, I rarely point them in the clinical direction. High was always point them in a claims processing, fraud detection, security. There’s so many areas you can point it at that it’s not life and death yet. I’m 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 improved the cost of healthcare, improve access,

Anthony Chang:            22:36               sorta like, um, multidimensional. So if you think of, um, 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 hospitals, healthcare organizations going to be able to have amazing music with all the sections, plane in a wonderful symphony. And also together. Because right now there’s a lot of the company, everyone’s doing their own thing. Um, you can have 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:                   23:27               One of the things that surprised me as I was going through the material from your website is we were using an ai and I 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 a diagram and it talks about the opportunity within ai in the different categories. Um, the second one, I’m not sure what the sources for this one, but. No, that’s me, you’re the source. So Ai in medicine and uh, the, the third is hospital operations. So workflows. 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, I’m looking at the accenture slide for the first time, so I respectfully have some disagreements. I think ai is really up to our imagination in terms of what the return is 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 a cybersecurity 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.

Bill Russell:                   24:50               And if I could drive that one home, we had implemented a lot of tools and each one of those generates log files. The log files got so massive, right? I couldn’t build a big enough team to go through those log files. We had to implement, um, 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 concern that they may be out of a job. Um, and I think medical imaging is exponentially increasing volume and complexity. Um, most of medical imaging data has really been informed if you look at just sheer volume has really been generated in 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, um, 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 a, 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 Gary 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 deep learning software was able to be the human contestants. But biomedicine and healthcare is Kinda 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 to go, even though it’s not simple.

Bill Russell:                   26:39               So what should I be telling my healthcare leader? What should I be telling my radio? So, computer vision now can read these images. Back fact, you know, again, another slide here. Algorithm better at diagnosing pneumonia than radiologists. Right? So, um, the other thing I heard about, you know, looking at these images that the computer can look for 100,000 things, whereas usually the, the cardiologist or the radiologist 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 were family.

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 radiologist by relieving the burden of particularly the normal studies. And I think the radiologists get focused on what’s abnormal. And what the repercussions of that finding would be, which

Bill Russell:                   27:43               it really is moving them up to practice at the top of their license. 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 I was like, wow, these autonomous cars are going to put me out of business. Like, well, not for the foreseeable future. I mean, even the, even the autonomous cars in Phoenix, they had a pilot going, they had drivers right behind the wheel because there are going to be situations that the computer can’t figure out. We’re not sure how long that’s going to be before the computer takes that step.

Anthony Chang:            28:16               Um, I haven’t 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 the same thing’s true here. We’re, we’re not at a point where I’m going to go, hey. Yeah, just have the computer gives you 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. Well, we did an interesting audience survey 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 that was 85, 90 percent one at all, both parties to be reading the CT. So give us some,

Bill Russell:                   29:06               give some clinical examples over 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 real time analytics in the pediatric cardiac intensive care unit, which is a genomic domain previously. So we could look at a, take all the vital signs from the pediatric patient after before, after heart surgery, particularly during the unstable periods. And we can actually for warned clinician what the, uh, the is telling us in terms of, um, 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 to. And if you combine that with other information like in the electronic records and lab values, 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 a 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 kinds of things.

Anthony Chang:            30:38               Well, actually in the process of coming up with what I call an api score for a healthy 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 try 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 in healthcare. And I 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 of 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 realized 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 very sound from the it perspective. And then the it, the ai part is actually quite straightforward and just someone kills someone well versed in ai projects. I will focus on medical imaging 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 art? So I do. 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. Our 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 already. 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.

Anthony Chang:            32:57               The, that’s exactly right. The icu monitoring data data’s fairly straightforward as relatively complete 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 you can easily do projects without actually involving your own patients. So they are publicly available databases and the healthcare just not many. Ideally, you know, if you were to ask me 20 years from now what I would like to see a like to see every patient’s data imported into the cloud and it will be universally available for any healthcare stakeholder to look at that’s anonymized, um, perhaps with blockchain or other types of security mechanism and it’ll be all available because that’s going to help 20 years. I’m very optimistic that we’ll be tackled within 20 years.

Bill Russell:                   33:54               So you would like to see you use essentially what your, 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 data set and come up with all sorts of new thought processes in terms of how to, how to attack certain disease states,

Anthony Chang:            34:18               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 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 your stuff.

Bill Russell:                   34:44               My kids laugh at me because I get into the car, I put gps to our home. They’re like, you don’t know where, how to get home. Now I’m just more comfortable with the gps.

Anthony Chang:            34:52               Well, and, and that’s how I like physicians eventually think about ai in medicine is it’s a gps that you just to get used to it a routine. It’s not something that is so esoteric or advanced that you can understand. It’s going to be there quietly as your partner. Um, if you looked at a microsoft commercial 25 years ago, um, there was actually a segment saying, you know, imagine the future. And I laughed because it says, imagine a future traveling across the country without fold out maps. And people were laughing. They just did nothing that was going to be possible. Now with gps, you don’t think twice about driving across the country.

Bill Russell:                   35:33               I pulled out a fold out map, uh, not too long ago with my kids in the room. And they’re just like, what is that? Is that a neW puzzle? What do you do?

Anthony Chang:            35:43               So 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 that’s why I like to see it’s actually embedded in their clinical routine without disruption to your workflow, without any distraction from your 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 sort of 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 the younger generation considering dual education in both clinical medicine and data scientists.

Bill Russell:                   36:32               Do you think like hopkins and stanford in my alma mater,

Anthony Chang:            36:37               um, ironically at stanford I didn’t meet too many doctors in the data science program, but I think just more right just as clinicians that may want to go back to school and get a data science education to be more of a specialist in this area. Like other specialties. I interesting now bad a data as a young person was a data scientist who got so interested in healthcare. 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:                   37:14               So again, I’m playing the healthcare administrator. I want to say, you know, this last year I’ve had five different vendors come into mind. I mean, how should I be thinking about these vendors because 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 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 the over promise and under deliver obviously I think there’ve been some very big companies that, um, you know, that overhyped and under delivered and actually became unsuccessful with those big institution. So I think 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 as an, 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 the same thing we did around big data. It’s, it’s small projects, 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 there going to be a problem scaling ai at this point?

Anthony Chang:            38:54               Not at all because I think the, the potential is really limited 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 an 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, you could tap into amazon, you get to tap into 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 data 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 rules of the road, just like with any, just like with the 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 I think I saw one of your slides, I’d see hla, the, the, uh, data scientists actually goes on route.

Anthony Chang:            40:08               Yeah. That was, um, a very strong advocate of that. Um, our data scientists at choc will see patients with me in the clinic and truly understand the clinical culture just like I did with my school years were I spent a lot of time with computer scientists.

Bill Russell:                   40:25               So what will the data scientists get pull out from that? What will they, what will they identify something and go, hey, we could, we can run the numbers on that.

Anthony Chang:            40:33               Exactly. Or they understand the nuances that when we have healthcare 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 a health it leaders, right? What am I going to do for my team? How am I, I’m clearly not everybody’s going to get up to speed on this, but how, how do I work? What are some resources? Obviously the aai med website. What are Some other ways I can get up to speed?

Anthony Chang:            41:12               Med website’s going to have 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 upwards. Um, we’re in the process of producing a short video series on hot topics. like what’s the difference between ai and learning, things like that. We’ll have that 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 as very entertaining with education. So in academic magazine it’s going to be free also to everybody on a bimonthly basis. We focus on a specific theme every other month. So this was all through ai met. This is all through us, we want to be 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 podcast here shortly and we’ll. All right, well all in yes, that’ll be great. Well thanks. Thanks for your time. I’m actually going to interview your friend here in a minute, but I’m going to do the close and then we’ll come back to this. So how can people follow you? Are you on social media or.

Anthony Chang:            42:48               I am. I’m just, that’s maturing right now, but I think the best source is still the website. That’s ai, dash med, med.io. It pretty much has everything to get started. Has the ebook, the magazine that you can go through? It has all the upcoming activities loaded 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. So you’ll be seeing a lot of us.

Bill Russell:                   43:17               Yeah, well I’m definitely gonna look you up down in dana point. I know when it came up last year somebody asked me to get on when I was out of town, so.

Anthony Chang:            43:26               and also I’m a finishing a book project with elsevier, so there’ll be a textbook that’s written for everybody because I don’t want this to be read by only clinicians or data scientists. So I’m trying to write it for everybody and with enough

Bill Russell:                   43:40               you’re running it for me. So it was a spiritual book. Is it like,

Anthony Chang:            43:43               oh, have you a picture next to me when I write from on? No, I think I’m a one. I’m really happy to see is there is 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 close out, you can follow me on twitter at the patient’s cio, my writing on the health lyrics website and health system cio.com. Don’t forget to follow the show at this weekend, this weekend. Hit and check out our website this week in health it that come, uh, I feel like this show. Take a few minutes and 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. Please come back every friday before

Speaker 4:                    44:38               news information.

 

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