1 00:00:00,000 --> 00:00:03,400 When I have questions about what's going on in healthcare, 2 00:00:04,080 --> 00:00:06,620 especially with regards to IT and things like that, 3 00:00:06,640 --> 00:00:07,100 I think of you. 4 00:00:07,520 --> 00:00:11,500 I think maybe we spoke a couple years ago now. 5 00:00:11,580 --> 00:00:15,340 I lose track of time, what with life and everything. 6 00:00:15,340 --> 00:00:18,520 But here, this episode, we have J.T. Perry, 7 00:00:19,020 --> 00:00:21,420 who works here with me at Tanzu. 8 00:00:21,940 --> 00:00:24,260 You were on the Tanzu Value Advisor team. 9 00:00:24,580 --> 00:00:27,160 You work just kind of general itinerant. 10 00:00:27,960 --> 00:00:33,040 Remember that, you're the subject matter errant 11 00:00:33,040 --> 00:00:35,660 when it comes to healthcare and things like that. 12 00:00:35,740 --> 00:00:39,080 Talking with our customers and people we talk with about 13 00:00:39,080 --> 00:00:42,440 kind of applying our whole Tanzu way of thinking about 14 00:00:42,440 --> 00:00:44,420 getting better at software, data management, 15 00:00:44,560 --> 00:00:46,480 all that stuff in the healthcare world. 16 00:00:46,700 --> 00:00:48,320 So thanks for being on again. 17 00:00:48,820 --> 00:00:51,100 Pleasure to always speak with you, sir. 18 00:00:51,880 --> 00:00:55,620 And yeah, I'll let my ego take that introduction. 19 00:00:56,620 --> 00:00:59,520 It's been here about three years. 20 00:01:00,160 --> 00:01:04,520 And before that, involved in several companies. 21 00:01:05,460 --> 00:01:07,600 Primera Blue Cross here in the United States 22 00:01:07,600 --> 00:01:09,640 is probably the best known one. 23 00:01:10,300 --> 00:01:10,760 Yeah, yeah. 24 00:01:10,820 --> 00:01:12,160 So it's always good to talk with someone 25 00:01:12,160 --> 00:01:15,360 who's not just a pure thought leader like myself, 26 00:01:15,440 --> 00:01:17,300 who's huffing the good ideas as they go. 27 00:01:17,400 --> 00:01:19,200 You've actually worked on these things. 28 00:01:19,700 --> 00:01:22,340 You've huffed bad ideas in addition to the good ones. 29 00:01:22,340 --> 00:01:22,480 Absolutely. 30 00:01:22,920 --> 00:01:24,940 Well, so the first thing I want to talk about, 31 00:01:25,020 --> 00:01:27,480 like I thought it would be, let's call it update on, 32 00:01:28,020 --> 00:01:29,440 you'll have to tell me when we get to it, 33 00:01:29,440 --> 00:01:32,240 what people call this, but digital healthcare in general, right? 34 00:01:32,260 --> 00:01:33,840 There's always a lot going on. 35 00:01:34,260 --> 00:01:36,160 But what I've been really interested in recently, 36 00:01:36,320 --> 00:01:39,520 just like everyone, of course, I'm interested in generative AI. 37 00:01:39,780 --> 00:01:41,160 And I'm just going to say AI. 38 00:01:41,460 --> 00:01:43,480 No need to get all persnickety. 39 00:01:43,740 --> 00:01:45,340 You can if you want, but that's fine. 40 00:01:46,380 --> 00:01:54,220 But I've really been trying to figure out like what it means in enterprises beyond, 41 00:01:54,620 --> 00:01:57,240 well, not even beyond, but just like I use it all the time. 42 00:01:57,380 --> 00:01:57,960 I don't know about you. 43 00:01:58,040 --> 00:02:03,820 Like I have, I'm probably spending too much money on chat GPT and Gemini and other things, 44 00:02:03,820 --> 00:02:04,740 but whatever. 45 00:02:05,120 --> 00:02:07,500 So I use it all the time for all sorts of things. 46 00:02:07,500 --> 00:02:14,420 And I've almost reached like, I feel like the limit of things I can do with it in my life. 47 00:02:14,500 --> 00:02:18,540 Like there's a lot of like work things I can't do with it because I'm a good corporate citizen 48 00:02:18,540 --> 00:02:21,040 and I don't like cut and paste all the work stuff in there. 49 00:02:21,120 --> 00:02:26,540 So like I only use it for my personal stuff and my podcasts and like public things, 50 00:02:26,540 --> 00:02:27,920 if that makes sense. 51 00:02:27,980 --> 00:02:32,640 So I don't know if it's good for summarizing meetings or like all this kind of stuff. 52 00:02:32,640 --> 00:02:37,720 But what I'm, what I'm mostly, what I'm also really interested in is if we look at various 53 00:02:37,720 --> 00:02:45,180 industries, like what people are thinking about doing with it and like the potential for all 54 00:02:45,180 --> 00:02:46,380 the AI stuff to help out. 55 00:02:46,500 --> 00:02:52,520 So that's the first like, like question is like, what are, what are people, well, let's, 56 00:02:52,580 --> 00:02:55,640 let's do first, like, what are they actually doing with AI right now? 57 00:02:55,720 --> 00:02:59,220 Like, and then we'll get to the more speculative things, like what it might be used to, but what 58 00:02:59,220 --> 00:03:01,120 are some applications that people are doing? 59 00:03:01,780 --> 00:03:08,460 So in a lot of ways, I think, uh, uh, in some of this could to be own it my own bias, cause 60 00:03:08,460 --> 00:03:14,280 that's where I pay the most attention, but it feels like healthcare has had kind of a head 61 00:03:14,280 --> 00:03:20,820 start or a significant amount of, of interest with the, the, the career kind of just explosion 62 00:03:20,820 --> 00:03:25,600 of, of activity and investment, uh, and even production in the AI space. 63 00:03:25,600 --> 00:03:32,280 And the biggest one that we are seeing like things already happen is in the radiological 64 00:03:32,280 --> 00:03:32,280 space. 65 00:03:32,280 --> 00:03:36,600 And by that, I mean, all the diagnostic stuff, right? 66 00:03:36,600 --> 00:03:42,640 Um, x-rays, cat scans, MRIs, nuclear, all of those kinds of things. 67 00:03:42,980 --> 00:03:47,080 Um, and a lot of that is because it was happening. 68 00:03:47,340 --> 00:03:54,160 The, the interest, uh, uh, and the experimentation was happening before kind of chat GPT and open 69 00:03:54,160 --> 00:03:56,640 AI and all of that, that took out, right? 70 00:03:56,640 --> 00:03:59,180 I think we would have called it machine learning, right? 71 00:03:59,180 --> 00:04:00,460 Before we decided, right, right. 72 00:04:00,840 --> 00:04:07,820 Two major providers in this space, both Siemens and, and GE healthcare, um, and without having 73 00:04:07,820 --> 00:04:12,040 direct knowledge of their stuff, just even what they've talked about publicly and, and, and, 74 00:04:12,040 --> 00:04:14,840 you know, kind of dinner barroom type conversations. 75 00:04:14,840 --> 00:04:19,900 We know that they have been using it for a period of time to help with diagnostic. 76 00:04:20,080 --> 00:04:25,740 And I'll give an example, um, an MRI, you know, you go, you're, they put some body part 77 00:04:25,740 --> 00:04:29,520 in the tube, take, you know, thousands of pictures and put them together. 78 00:04:29,840 --> 00:04:32,980 And it is important to realize those are a thousand pictures, right? 79 00:04:33,280 --> 00:04:35,020 Well, that radiologist. 80 00:04:35,020 --> 00:04:39,320 So the doctor that looks at those and tries to kind of come up with a, so what about that 81 00:04:39,320 --> 00:04:42,840 has to look at each one of those pictures, right? 82 00:04:42,840 --> 00:04:47,120 You could think of it as a slide show now, there's all sorts of tools that can scroll 83 00:04:47,120 --> 00:04:51,940 their, their, their mouse or whatever, and, and make it look like a movie and all of that. 84 00:04:52,200 --> 00:04:54,160 But, but human nature happens, right? 85 00:04:54,180 --> 00:05:00,400 So if I was going in for, uh, an MRI to say, look at my left lung, because I, you know, somebody 86 00:05:00,400 --> 00:05:01,960 thought something was going on in there. 87 00:05:02,260 --> 00:05:05,320 You go on and say, I'm going to get an MRI. 88 00:05:06,060 --> 00:05:09,520 Uh, they tell the radiologist, Hey, we're looking at something in the left lung. 89 00:05:09,940 --> 00:05:14,580 Well, meanwhile, you know, the MRI was of your whole body of your whole chest, right? 90 00:05:15,040 --> 00:05:18,160 As a doctor, as I'm scrolling through that and I'm looking at your left lung, I might 91 00:05:18,160 --> 00:05:21,820 not see that other spot over on your right side, right? 92 00:05:21,880 --> 00:05:24,660 Cause, cause I'm looking at how many of these a day. 93 00:05:24,840 --> 00:05:25,900 And I just need to crunch through those. 94 00:05:26,100 --> 00:05:29,320 So that's where we're starting to see a lot of these tools happen, right? 95 00:05:29,320 --> 00:05:32,800 Is, is using AI to go, Hey doc, go look at that. 96 00:05:33,400 --> 00:05:38,080 So if I were to generalize that, cause I think you're making me think there is a generalization 97 00:05:38,080 --> 00:05:42,200 of let's call it enterprise AI, which is we'll use tech, tech terms. 98 00:05:42,200 --> 00:05:47,560 We'll use like a, some, some tech terms, which is you can scale a human's effort. 99 00:05:47,740 --> 00:05:52,320 And, and in that instance, like I think that example is great because well, great. 100 00:05:52,380 --> 00:05:58,100 It's interesting because you're constrained by the expert's time by the human's time. 101 00:05:58,200 --> 00:06:03,440 And so one of the behaviors that they have to do by necessity is focus on just one thing. 102 00:06:03,960 --> 00:06:08,420 So in a sense, like if they're looking at your left lung, there's throwing out all this 103 00:06:08,420 --> 00:06:11,940 data, all this opportunity to also look at other things. 104 00:06:12,700 --> 00:06:17,240 So it actually makes probably, I mean, tell me if I'm wrong, but it definitely makes economic 105 00:06:17,240 --> 00:06:22,580 sense and probably even diagnostic sense because then you can, I don't know the ethics of radiology, 106 00:06:22,740 --> 00:06:26,360 but you can look at more people to help more people instead of focusing all of your time 107 00:06:26,360 --> 00:06:26,540 there. 108 00:06:26,540 --> 00:06:32,180 So the scaling is sort of like, well, what if we had like, instead of just one entity, 109 00:06:32,380 --> 00:06:36,580 one human looking at it, now we can kind of simulate like five people looking at this 110 00:06:36,580 --> 00:06:38,660 one thing to catch other stuff that's going on. 111 00:06:39,080 --> 00:06:40,680 Yeah, it is scale. 112 00:06:40,780 --> 00:06:45,420 And I would say it's both sides of the ethical box, so to speak too, right? 113 00:06:45,680 --> 00:06:47,500 That are really positive one, right? 114 00:06:47,520 --> 00:06:53,820 Let's, let's use these computers to check everything and be that backstop, right? 115 00:06:53,820 --> 00:06:56,620 And say, oh no, hey, look, here's something here. 116 00:06:56,620 --> 00:07:01,640 Maybe it's smaller than frankly, a human eye would, would pick up, right? 117 00:07:01,820 --> 00:07:10,880 It's a, but then there is the other side where in general, and again, not to speak for anybody 118 00:07:10,880 --> 00:07:15,220 that's actually doing this, they feel like it's a great assistive technology right now. 119 00:07:15,260 --> 00:07:18,680 It's not something that should be used to diagnose by itself. 120 00:07:18,680 --> 00:07:20,540 Meaning it shouldn't be the doctor. 121 00:07:20,600 --> 00:07:20,760 Right. 122 00:07:20,920 --> 00:07:23,420 It should go, hey doc, I think he broke his arm. 123 00:07:23,420 --> 00:07:25,700 And the doctor should go, well, yes, you broke your arm, right? 124 00:07:26,000 --> 00:07:28,520 That's maybe too obvious of one, but, but something like that. 125 00:07:28,760 --> 00:07:35,380 We do know that there are countries that don't have enough radiologists out there that are 126 00:07:35,380 --> 00:07:37,880 kind of basically going, eh, good enough. 127 00:07:38,100 --> 00:07:39,760 We'll just use this as diagnostic. 128 00:07:41,040 --> 00:07:41,900 Right, right, right. 129 00:07:41,900 --> 00:07:44,260 There is some ethical question, right? 130 00:07:44,340 --> 00:07:49,360 I mean, it's not, nobody's even tried to put it through like a FDA trial here in the United 131 00:07:49,360 --> 00:07:54,360 States, um, for, to be the sole diagnostic, uh, it goes through lots of trials to be this 132 00:07:54,360 --> 00:07:55,220 assistive technology. 133 00:07:55,220 --> 00:07:59,980 So, well, I mean, that, that brings up a side note I wanted to ask later, but we'll, we'll, 134 00:07:59,980 --> 00:08:06,680 we'll side note here, which is, uh, like what, like I would imagine, I mean, I mean, this 135 00:08:06,680 --> 00:08:11,760 gets in a weird squishy space, but like, is there like medical innovation that happens in less 136 00:08:11,760 --> 00:08:16,840 regulated regions out of necessity that kind of comes back to more highly regulated? 137 00:08:16,960 --> 00:08:21,260 Cause, cause I could see kind of in this instance, you know, if you were in some remote place 138 00:08:21,260 --> 00:08:25,580 that didn't have a lot of radiologists, I mean, you could almost have like some rudimentary 139 00:08:25,580 --> 00:08:30,640 stuff and get like a Starlink satellite uplink, just like uploaded to somewhere. 140 00:08:31,100 --> 00:08:32,540 And I don't know what, right. 141 00:08:32,540 --> 00:08:37,940 But there's all this kind of weird stuff and, and, you know, remote health seems like fraught, 142 00:08:38,140 --> 00:08:41,660 except that you have to like back in COVID, as we talked about last time, like, 143 00:08:41,760 --> 00:08:47,960 it's just like all sorts of crazy stuff happens, but sometimes like necessity might like cause 144 00:08:47,960 --> 00:08:49,480 interesting innovation to happen. 145 00:08:50,120 --> 00:08:53,140 Well, I think that's, that's been true in healthcare all along, right? 146 00:08:53,300 --> 00:08:56,520 Nothing brings out innovation in healthcare, like a good crisis. 147 00:08:56,700 --> 00:09:00,200 Look at the MRNA technology around vaccines and all of that, right? 148 00:09:00,300 --> 00:09:01,380 It looked good on paper. 149 00:09:01,480 --> 00:09:02,780 People were really excited about it. 150 00:09:02,820 --> 00:09:03,560 And then COVID happened. 151 00:09:03,560 --> 00:09:05,620 And then now it's become the silver bullet, right? 152 00:09:06,520 --> 00:09:06,960 Yeah. 153 00:09:07,200 --> 00:09:07,600 Aspect. 154 00:09:08,340 --> 00:09:08,740 Yeah. 155 00:09:09,220 --> 00:09:14,100 We'll go too far down a tangent, but the obesity crisis and bringing in Ozepic and now us looking 156 00:09:14,100 --> 00:09:19,240 at the semi-glutide that makes Ozepic go and realizing all these other cool things that 157 00:09:19,240 --> 00:09:20,340 might do too, right? 158 00:09:20,480 --> 00:09:23,900 But again, let's fall down a big rabbit hole if we go too far there. 159 00:09:23,980 --> 00:09:29,720 But I would say in general, yes, in that I don't know that necessarily innovation in those 160 00:09:29,720 --> 00:09:35,020 areas is bringing it back, but it's, it gives the opportunity for a wide scale testing. 161 00:09:35,100 --> 00:09:38,200 And, and again, there's, there's an ethical issue about that, right? 162 00:09:38,240 --> 00:09:41,280 Do people even know they're being a test case, so to speak. 163 00:09:41,380 --> 00:09:46,460 So I don't think any of those companies would consciously honestly go, Hey, let's go give 164 00:09:46,460 --> 00:09:49,580 it to that rural area that doesn't have great healthcare and let's see what happens. 165 00:09:49,800 --> 00:09:51,280 It's more of a toll, right? 166 00:09:51,560 --> 00:09:55,160 Those areas, those governments go and grab this technology and use it. 167 00:09:55,640 --> 00:10:00,160 And, and we all look at, at kind of the outputs of that, but it is, it's a, it's an ethical 168 00:10:00,160 --> 00:10:04,160 mess and it's a, you know, you can have a lot of conversations over a lot of beverages 169 00:10:04,160 --> 00:10:09,100 on should we, or shouldn't we, um, you know, we learned a lot of good information out of 170 00:10:09,100 --> 00:10:10,020 this horrible scenario. 171 00:10:10,020 --> 00:10:10,960 Can we use it? 172 00:10:11,320 --> 00:10:12,140 Should we use it? 173 00:10:12,580 --> 00:10:16,420 Uh, uh, big philosophical books have been written in that space in the healthcare world. 174 00:10:16,720 --> 00:10:17,820 Well, back, back to the AI stuff. 175 00:10:17,820 --> 00:10:22,740 So yeah, I can see that that was, that was, that is one of the things that it wouldn't 176 00:10:22,740 --> 00:10:29,900 occur to me, uh, as an enterprise AI application, because it is basically images, analyzing images 177 00:10:29,900 --> 00:10:34,540 that, that brings up one sort of structural issue with, with AI stuff, which, you know, 178 00:10:34,540 --> 00:10:38,640 just to confirm with you, it's a lot of, at least my thinking, but probably our thinking 179 00:10:38,640 --> 00:10:44,740 of how to apply enterprise AI is limited by the fact that we consume it as a chat application 180 00:10:44,740 --> 00:10:45,660 essentially. 181 00:10:45,880 --> 00:10:45,980 Right. 182 00:10:46,020 --> 00:10:49,960 Whereas that's just one way of doing AI. 183 00:10:50,280 --> 00:10:54,020 And as, as, as I'm thinking more about this, and just as you brought up, it seems like there's 184 00:10:54,020 --> 00:10:58,700 a lot of applications beyond that, that are kind of hard to see, uh, essentially. 185 00:10:59,100 --> 00:11:04,340 Um, so anyways, I, I mean, that kind of opens up the aperture for like all sorts of other 186 00:11:04,340 --> 00:11:07,660 things, but what, like, like, what are some other things beyond radiology? 187 00:11:07,660 --> 00:11:14,200 A lot of drug research, a lot of solutions, a lot of kind of think about the, what if type 188 00:11:14,200 --> 00:11:18,660 type scenario of expert, you know, what if, you know, today I have chemical A and chemical 189 00:11:18,660 --> 00:11:20,220 B and it gives me Tylenol. 190 00:11:20,420 --> 00:11:25,620 Well, what if I took chemical B and chemical C, would it give me like something to cure cancer? 191 00:11:25,880 --> 00:11:26,100 Right. 192 00:11:26,180 --> 00:11:29,860 I, I, and, and I don't even mean to be flippant about that. 193 00:11:29,860 --> 00:11:35,240 Like the research around these diseases that we've never been able to like crack open and 194 00:11:35,340 --> 00:11:38,280 solve, um, ALS, right. 195 00:11:38,340 --> 00:11:41,820 Those, those big ones, Alzheimer's one super, super close to me. 196 00:11:41,880 --> 00:11:44,260 And what can AI help us? 197 00:11:44,640 --> 00:11:47,760 Uh, can it give us ideas, things we haven't thought of? 198 00:11:47,920 --> 00:11:53,500 I don't know that anybody realistically expects to go into chat GPT and go, here's a thousand 199 00:11:53,500 --> 00:11:54,120 case records. 200 00:11:54,120 --> 00:11:55,100 Help me solve this. 201 00:11:55,100 --> 00:11:55,460 Right. 202 00:11:55,880 --> 00:12:00,240 But could it down to just, you know, almost an idea generator. 203 00:12:00,560 --> 00:12:01,960 Hey, this might work. 204 00:12:02,040 --> 00:12:02,360 Yeah. 205 00:12:02,480 --> 00:12:08,600 Go look down with that area or the common, well, I'm going to bring it back to a geek example 206 00:12:08,600 --> 00:12:10,580 that I see in our enterprises today. 207 00:12:10,640 --> 00:12:10,780 Right. 208 00:12:11,140 --> 00:12:17,840 SRE is taking, uh, problem reports, outages, reports, test failures, uploading it into AI 209 00:12:17,840 --> 00:12:19,760 and seeing patterns they never saw before. 210 00:12:19,760 --> 00:12:20,060 Right. 211 00:12:20,380 --> 00:12:23,320 Oh, the core weakness of our system is this widget over here. 212 00:12:23,700 --> 00:12:25,440 We'll try to do that with the system. 213 00:12:25,440 --> 00:12:26,500 That is the human body. 214 00:12:26,640 --> 00:12:26,920 Right. 215 00:12:27,080 --> 00:12:33,180 Look across all these thousands of test diagnostics of, uh, I'll use Alzheimer's, you know, cat 216 00:12:33,180 --> 00:12:37,540 scans, even just reports from doctor interviews and stuff like that. 217 00:12:37,660 --> 00:12:44,280 Is there a pattern we're missing that that's the, the, the hope, the thing that like personally 218 00:12:44,280 --> 00:12:50,120 gets me excited, um, that, you know, can we make advancements where we're not, it's AI 219 00:12:50,120 --> 00:12:51,380 isn't going to cure all our ills. 220 00:12:51,640 --> 00:12:56,720 I'd be a little worried if it magically did, but, uh, you know, does it help us to start to 221 00:12:56,720 --> 00:12:58,200 make real advancements in some of those? 222 00:12:58,260 --> 00:13:01,840 That's, that's the cool, exciting stuff, frankly. 223 00:13:02,500 --> 00:13:04,940 Um, and, and the part of it that gets me excited. 224 00:13:05,360 --> 00:13:09,920 Yeah, like, like a lot of what you're talking about is, I don't really know the right word 225 00:13:09,920 --> 00:13:16,460 for it, but it's sort of like analysis analysis is too fancy of a word, but it's, it's almost 226 00:13:16,460 --> 00:13:18,540 like more of what I have in my head. 227 00:13:18,540 --> 00:13:22,720 It's like the, the, the person who brings a stack of papers home with them and just keeps 228 00:13:22,720 --> 00:13:27,440 looking through it, like kind of the, uh, the, the, the red string crazy board on, on 229 00:13:27,440 --> 00:13:29,260 their wall to try to look through something. 230 00:13:29,260 --> 00:13:34,940 And you know, that, I mean, tell, tell, tell me if, tell me if I'm wrong to limit it to 231 00:13:34,940 --> 00:13:39,920 this, but like in my experience and my very basic understanding of like generative AI stuff 232 00:13:39,920 --> 00:13:44,820 is that very rarely comes up with something new, right? 233 00:13:44,820 --> 00:13:49,380 Like there, there's not this like creativity so much to it as much as, as you're saying, 234 00:13:49,540 --> 00:13:52,600 pattern matching and like finding things that are there. 235 00:13:52,600 --> 00:13:56,140 And of course, you know, anticipating what the logical next thing would be. 236 00:13:56,240 --> 00:14:00,260 I mean, that's kind of the core of how it works, which for the scenarios you were describing, 237 00:14:00,260 --> 00:14:07,000 like if I were to, I guess, I guess a lot of diagnosing is pattern matching, right? 238 00:14:07,080 --> 00:14:08,060 See, seeing what's there. 239 00:14:08,060 --> 00:14:16,500 And then also a lot of just any gigantic process like healthcare is, is also paperwork, right? 240 00:14:16,560 --> 00:14:20,940 Like both, both paperwork as far as running in the positive way that you're obviously, but 241 00:14:20,940 --> 00:14:25,700 also like just the paperwork of just like looking through data, like you were talking about looking 242 00:14:25,700 --> 00:14:27,720 through all this, this knowledge. 243 00:14:28,640 --> 00:14:34,660 So much of healthcare is I had this treatment for this thing and we accidentally discovered 244 00:14:34,660 --> 00:14:36,100 it also helped this thing. 245 00:14:36,420 --> 00:14:36,860 Right. 246 00:14:37,300 --> 00:14:42,400 And not to get into like uncomfortable or giggly of a subject, but, but right. 247 00:14:42,480 --> 00:14:48,180 The whole, uh, Viagra erectile dysfunction was a blood pressure medicine that helped this. 248 00:14:48,180 --> 00:14:50,720 And I just bring it up because it's kind of the one everybody knows. 249 00:14:50,860 --> 00:14:51,160 Oh, right. 250 00:14:51,160 --> 00:14:54,480 And then you also, I think, I think you brought it up earlier, but you have the, uh, that 251 00:14:54,480 --> 00:14:57,100 diabetes thing that helps out with, with obesity, right? 252 00:14:57,300 --> 00:14:59,320 That seems like an accidental discovery. 253 00:14:59,860 --> 00:15:00,080 Yeah. 254 00:15:00,380 --> 00:15:00,560 Yeah. 255 00:15:00,680 --> 00:15:03,400 I mean, again, we could be a whole nother topic. 256 00:15:03,400 --> 00:15:09,320 We're now discovering it may help with, uh, addictions, whether alcohol or, or, or, uh, opioid, 257 00:15:09,320 --> 00:15:15,440 um, just a crazy number of studies going on that are adjacent for, yeah, this drug that 258 00:15:15,440 --> 00:15:18,600 started off as, Hey, maybe we could help folks with type two diabetes. 259 00:15:19,300 --> 00:15:23,700 Uh, and so get, give it, given that kind of analysis and diagnostic stuff. 260 00:15:23,700 --> 00:15:29,680 I mean, it seems like there's a lot that you would apply, uh, like enterprise AI to and 261 00:15:29,680 --> 00:15:29,980 healthcare. 262 00:15:29,980 --> 00:15:35,320 And so like what, so then this gets to more of like what our business in the Tanzu world 263 00:15:35,320 --> 00:15:40,100 is, but like, what are, so, so when, when you either think to yourself or you're talking 264 00:15:40,100 --> 00:15:45,080 with people who are doing this, what is like the plan, right? 265 00:15:45,080 --> 00:15:50,580 Like they need the data, they need like the governance, they need some kind of like what 266 00:15:50,580 --> 00:15:54,340 I would call a product management cycle, like just discovering what to do and experimenting 267 00:15:54,340 --> 00:15:54,760 with it. 268 00:15:54,760 --> 00:16:00,320 But like, what's kind of like the toolbox that people need to just like start doing stuff 269 00:16:00,320 --> 00:16:01,220 with it. 270 00:16:01,640 --> 00:16:05,200 The folks that are doing the, the, the research stuff, right. 271 00:16:05,240 --> 00:16:09,260 And we, we should at some point talk about the healthcare industry and how it's, it's 272 00:16:09,260 --> 00:16:14,400 helping in similar ways to just like every other industry, to be honest, but, but, uh, the 273 00:16:14,400 --> 00:16:19,080 healthcare world, these folks are really, really good today. 274 00:16:19,080 --> 00:16:25,020 And already, how do I take an idea, take or take my hypothesis, go through various trials, 275 00:16:25,180 --> 00:16:28,060 try things in a very safe, ethical, regulated way. 276 00:16:28,260 --> 00:16:29,800 That structure is there. 277 00:16:29,960 --> 00:16:36,400 AI is not going to, uh, nor should it frankly go around or change that process. 278 00:16:36,940 --> 00:16:41,900 Um, you know, yeah, yeah, I, I guess, I guess of there's, there's other industries, but 279 00:16:41,900 --> 00:16:46,840 of, of, of very few other industries, healthcare is very scientific in what it does. 280 00:16:47,340 --> 00:16:53,420 Um, for us, for, for just frankly, our day jobs in the Tanzu world, um, there's a lot 281 00:16:53,420 --> 00:16:56,220 of geeky stuff that becomes really interesting. 282 00:16:56,220 --> 00:17:01,620 And to use that radiological example, um, I can't upload an MRI to the cloud, right. 283 00:17:01,620 --> 00:17:06,160 Despite all our advantage advances today, hospitals don't have enough bandwidth, right. 284 00:17:06,220 --> 00:17:06,360 Right. 285 00:17:06,360 --> 00:17:08,520 I mean, MRIs are just gigabytes and gigabytes. 286 00:17:08,520 --> 00:17:15,000 So how do I do this dramatic AI helping in kind of a, uh, to use another buzzword of the 287 00:17:15,000 --> 00:17:16,940 day edge scenario, right. 288 00:17:16,940 --> 00:17:19,840 And how do I have a thin little office in a hospital that does that? 289 00:17:20,320 --> 00:17:24,120 Um, there's a lot of innovation happening in the healthcare space there. 290 00:17:24,740 --> 00:17:30,120 Um, that, that, you know, how do I, if I find this great idea, how do I make it practical 291 00:17:30,120 --> 00:17:37,160 to, uh, the hospital in, you know, a very rural area around the world, um, that may have 292 00:17:37,160 --> 00:17:38,660 Starlink may not have Starlink. 293 00:17:38,660 --> 00:17:39,360 How do I do it? 294 00:17:39,420 --> 00:17:43,300 So there's a, I don't think that's quite what you were asking, but I would say there's a 295 00:17:43,300 --> 00:17:48,640 ton of innovation and things as an industry we need to solve to bring some of this healthcare 296 00:17:48,640 --> 00:17:53,540 stuff to life, as opposed to the research stuff, which is, Hey, give me all your GPUs 297 00:17:53,540 --> 00:17:56,080 and I'm just going to crunch this to see how to solve Alzheimer's. 298 00:17:56,380 --> 00:17:59,240 That bit's already there because they've been using a lot of that for jump. 299 00:17:59,280 --> 00:17:59,560 Yeah. 300 00:17:59,680 --> 00:18:00,220 Stuff like that. 301 00:18:00,780 --> 00:18:01,080 Yeah. 302 00:18:01,160 --> 00:18:06,380 I think, I think that's, that's, that's, that's a, that's a good, helpful answer because let, 303 00:18:06,380 --> 00:18:10,580 you know, as the first two things you said, one there, I mean, the first two things are 304 00:18:10,580 --> 00:18:16,180 in, in healthcare, people are very good at experimenting and, and just, I guess, to make a pun, generative 305 00:18:16,180 --> 00:18:20,700 experimenting, where you're trying things out to find out what works and you understand how a 306 00:18:20,700 --> 00:18:25,680 scientific experiment works. And then, and then two, they're also very good at regulation 307 00:18:25,680 --> 00:18:29,920 and governance. Now, obviously there's, there's, you know, that creates some friction and things 308 00:18:29,920 --> 00:18:34,280 like that, but in general, they're probably kind of like banks, one of the better industries 309 00:18:34,280 --> 00:18:39,580 when it comes to dealing with regulation and making it not shut everything down, which, which, 310 00:18:39,740 --> 00:18:43,180 which, which, which would, which would seem like it'd be great for like AI stuff, because 311 00:18:43,180 --> 00:18:47,140 then you don't have to worry about that stuff so much. You just kind of fit it in to, 312 00:18:47,260 --> 00:18:48,760 to, to those processes. 313 00:18:49,240 --> 00:18:54,620 And I think super importantly, medicine and healthcare research has an ethical framework 314 00:18:54,620 --> 00:18:59,500 too, where I think, you know, when we talk about some of the AI stuff that's going out 315 00:18:59,500 --> 00:19:04,020 there, whether it's the, the Hollywood, right. Making videos, making music, all of that, like 316 00:19:04,020 --> 00:19:09,540 what's the ethical impact of doing X, Y, or Z the healthcare world. There is a whole structure 317 00:19:09,540 --> 00:19:13,060 around that and process. And I'm not going to pretend that the healthcare world always gets 318 00:19:13,060 --> 00:19:18,060 that right. Like, like it's not perfect, but the structure is there and it gets it right 319 00:19:18,060 --> 00:19:22,760 much, much more than it gets it wrong. When it comes to, should we try this? Should we 320 00:19:22,760 --> 00:19:23,440 experiment? 321 00:19:23,440 --> 00:19:28,560 All of that's already there. I actually believe that'll be an accelerant for AI in the healthcare 322 00:19:28,560 --> 00:19:33,740 space because it'll be the word where I think a lot of other industries are going to go forward, 323 00:19:33,980 --> 00:19:39,820 hit a wall, have to take steps back, whether court mandated or, or whatever, uh, and then 324 00:19:39,820 --> 00:19:40,560 kind of go forward. 325 00:19:40,820 --> 00:19:45,480 Yeah. I mean, I guess, so, so that's, that's what we're talking about next is the, the, the, 326 00:19:45,480 --> 00:19:50,680 the risk or the, or the doom and gloom things, the FUD, right. And, you know, one of the main 327 00:19:50,680 --> 00:19:56,980 things people don't like about AI, which certainly, you know, if, if, if it were to affect me, I would 328 00:19:56,980 --> 00:20:02,180 not like it is instead of augmenting people being co-pilots as Microsoft would say, just replacing 329 00:20:02,180 --> 00:20:11,660 them. But it, it seems to me that in healthcare, there is such a lack of supply of skills that that would 330 00:20:11,660 --> 00:20:17,360 be an impossible scenario that, that basically like, we're always looking for, for new, you know, more 331 00:20:17,360 --> 00:20:23,180 nurses and doctors and radiologists and everyone's skilled at it that like any help you can give is like 332 00:20:23,180 --> 00:20:28,320 needed and people would be very excited to, uh, have, have, uh, augmentation there. 333 00:20:28,940 --> 00:20:35,780 I think that's true. Right. I, I, I, the, the doom and gloom around AI and healthcare is somebody using 334 00:20:35,780 --> 00:20:42,780 it before it's ready. Right. We can, I can write hours about the FDA regulatory process and how long 335 00:20:42,780 --> 00:20:48,320 it takes, but there is a tremendous amount of safety behind it. Right. Conspiracy theorists 336 00:20:48,320 --> 00:20:54,120 decide it's a damn good system. It really is as far as making sure treatments are safe before they're 337 00:20:54,120 --> 00:21:00,420 widely used. Um, and, and, and then how about, how about like, like privacy and leaking concerns and 338 00:21:00,420 --> 00:21:06,120 things like that? Like what, what, um, I, I mean, you know, to, to, to, as I like to do, ask a question 339 00:21:06,120 --> 00:21:11,320 and, and talk some more about it. Like I was thinking recently about privacy and leaking things. And it 340 00:21:11,320 --> 00:21:18,140 seems like if, if you don't let what you have leak back into the basic model, then you're fine. 341 00:21:18,140 --> 00:21:23,320 Like, I don't really know how this stuff works. Uh, technically as far as like writing data back 342 00:21:23,320 --> 00:21:28,580 that other people then use, but it seems like you can just not allow things to happen. I'm not quite 343 00:21:28,580 --> 00:21:34,180 sure, but like when it comes to like, like healthcare data, what are people thinking about there? 344 00:21:34,180 --> 00:21:42,780 My, my personal risk around it, isn't that this concept of AIs and, and using right GPT-4 as a 345 00:21:42,780 --> 00:21:48,460 base model and augmenting it with my local medical records. I totally contrived that scenario. Um, 346 00:21:48,560 --> 00:21:54,460 I think as an industry, we've figured out, right. Using RAG, using these various other approaches, 347 00:21:54,460 --> 00:22:00,700 how do I add my local data into the mix to, you know, to the base model, but yet keep them separate. 348 00:22:00,700 --> 00:22:06,280 I think I don't want to say it's solved. We're on the large part. What, what I worry about from a 349 00:22:06,280 --> 00:22:13,560 privacy perspective is in the urgency to, to use all of this is we're just uploading, right? Again, 350 00:22:13,560 --> 00:22:17,700 even if I'm going to use this adjacent database, right. Using VAG and vector, RAG and vector and 351 00:22:17,700 --> 00:22:23,620 all these other things. Um, I, I still probably am going to upload it somewhere. And then my fear is 352 00:22:23,620 --> 00:22:29,700 just frankly, somebody leaving the S3 bucket exposed, right? Just standard data security. 353 00:22:29,700 --> 00:22:36,720 I think that's, that's probably the bigger risk because we're all in a hurry. Right. And because 354 00:22:36,720 --> 00:22:43,660 I'll use Alzheimer's, right? Look, if somebody could like forget all the regulation and solve this for 355 00:22:43,660 --> 00:22:48,620 my mother tomorrow, I'd be really appreciative. And I might even be okay. If you leak some of her data 356 00:22:48,620 --> 00:22:55,600 in the process, I hate to be that flippant, but the around healthcare, a risk and why I'm really glad we 357 00:22:55,600 --> 00:23:02,620 have these ethical structures, we do desperation comes into play, right? Very, very much so. And so, 358 00:23:02,620 --> 00:23:09,340 uh, but anyway, I'm rambling. I think the issue I worry more about is folks now doing more with 359 00:23:09,340 --> 00:23:17,260 big data or these large data sets, uploading them, you know, or frankly, new AI providers. I, I think 360 00:23:17,260 --> 00:23:22,080 open AI would be great at this. So, so excuse me for using them as an example, but, but, you know, 361 00:23:22,320 --> 00:23:29,020 I upload my medical record database next to open AI and open AI just somehow leaves a back door open, 362 00:23:29,120 --> 00:23:33,960 right? I, again, I think open AI is really good. I shouldn't even use a brand, but, but I think just 363 00:23:33,960 --> 00:23:39,480 general data security to your point, um, folks playing in areas that they aren't used to, 364 00:23:39,480 --> 00:23:46,920 right? Um, uh, around this is the bigger issue. I don't think there's a leaking, you know, I could 365 00:23:46,920 --> 00:23:52,940 see a startup in the AI space that, you know, Hey, I can review medical records. I can review your, 366 00:23:52,940 --> 00:23:59,220 your Epic data as a startup, right? Uh, you know, give me access to your Epic data and I can find these 367 00:23:59,220 --> 00:24:04,300 patterns to, you know, doctor help you treat these patients better, which, which gets into a financial 368 00:24:04,300 --> 00:24:09,240 piece, right? And we can talk about revenue management in a minute for providers, um, you 369 00:24:09,240 --> 00:24:14,160 know, in them just being new to the space and leaking data. But again, that has nothing. I don't think 370 00:24:14,160 --> 00:24:20,040 there's anything inherently an AI that's going to leak data, not anything. Yeah. I know of, or I'm aware 371 00:24:20,040 --> 00:24:26,100 of today, but just suddenly this many more people playing with data and uploading probably large data 372 00:24:26,100 --> 00:24:30,200 sets, you know, just good old, to your point earlier, a good old fashioned data security is 373 00:24:30,200 --> 00:24:35,620 probably the big risk. Right. Right. It's the, uh, in a good way, it's the same problems we've had 374 00:24:35,620 --> 00:24:42,100 forever. Like, like eventually the files have to be copied somewhere and be unencrypted for you to use 375 00:24:42,100 --> 00:24:46,500 them. And so you have to make sure that, that the wrong people are, or, you know, the wrong bit, 376 00:24:46,600 --> 00:24:50,620 the wrong thing doesn't happen to your unencrypted data. Right. But you gotta, it's, it's, uh, but 377 00:24:50,620 --> 00:24:56,360 there's, but there's not something inherent in the generative AI thing that requires you to leak 378 00:24:56,360 --> 00:25:01,220 information or it makes that a lot easier. Like it's, it's easy enough to, uh, to protect things. 379 00:25:01,400 --> 00:25:05,300 Well, well you brought up like revenue management, right? And I think, I mean, there's obviously, 380 00:25:05,900 --> 00:25:10,400 well, not obviously, but many more scenarios when it comes to the, uh, well, you have to tell me the 381 00:25:10,400 --> 00:25:16,040 terms, but the, uh, the content, the actual practice of healthcare. Uh, but then there's the, 382 00:25:16,040 --> 00:25:22,800 the, uh, running all of this, the backend, if you will. Uh, and that seems like there's probably 383 00:25:22,800 --> 00:25:28,380 also a lot, uh, of things you could, you could apply generative AI to, uh, in that area. So what, 384 00:25:28,380 --> 00:25:32,500 what are, what are, you know, what are people doing there now? And what are they thinking about? 385 00:25:32,600 --> 00:25:37,040 I think the big key here is it's we're now, you know, so, so we just got done talking about, 386 00:25:37,100 --> 00:25:41,720 I think what's unique about AI and healthcare. And now the other place that we're seeing it like 387 00:25:41,720 --> 00:25:48,040 in production and making a difference now is like everybody else, right? That those business process 388 00:25:48,480 --> 00:25:56,140 type stuff, you know, revenue management, uh, almost seems like a euphemism, but it's, it's the 389 00:25:56,140 --> 00:26:01,740 backend of, of providers. And when I say provider, I mean, doctor, hospital, clinic lab, the, the people 390 00:26:01,740 --> 00:26:06,900 doing the healthcare stuff, the back office of that is generally referred to as revenue. That's the 391 00:26:06,900 --> 00:26:10,100 dealing with the insurance companies. And that's the part I would, I would, you know, I'll talk about 392 00:26:10,100 --> 00:26:16,280 here in a second is, is the biggest example. And this is a U S uh, uh, thing for, for those of, 393 00:26:16,280 --> 00:26:20,760 of you in the international markets, uh, feel free to turn this off for the next five minutes and laugh 394 00:26:20,760 --> 00:26:25,940 at the U S healthcare system. But this is the prior authorization space, right? Where as a provider, 395 00:26:25,940 --> 00:26:31,680 I have to ask the insurance company for almost anything that isn't an emergent. Can I, right? 396 00:26:31,680 --> 00:26:38,980 Can I please let J do this back surgery on JT? Can I please do this MRI on JT? Right. That, that kind 397 00:26:38,980 --> 00:26:45,220 of stuff. Um, you know, we always hear the, the horrible story of prior authorization conceptually, 398 00:26:45,220 --> 00:26:51,420 it's there for fraud protection. Um, it is also there, uh, conceptually to make sure you get the 399 00:26:51,420 --> 00:26:56,700 care you need and don't get the care you don't need. Right. Uh, uh, right. There are downsides to 400 00:26:56,700 --> 00:27:02,560 endless cat scans that there really are right. Right. Right. Radiation is a problem. Um, so, 401 00:27:02,660 --> 00:27:10,000 you know, it's things like that, but that process is incredibly, uh, manual today. And I will 402 00:27:10,000 --> 00:27:16,300 right. Highly skilled. Right. So as a provider, if I'm going to ask the insurance company for, 403 00:27:16,300 --> 00:27:22,300 uh, uh, permission to do a back surgery, I have to know all these codes called ICD codes and let's not 404 00:27:22,300 --> 00:27:27,060 go into that, but just know I've got to get the code, right. I've got to get the text, right. 405 00:27:27,060 --> 00:27:33,020 Kind of the justification, the evidence, why I want to do this. Um, right. And a doctor, 406 00:27:33,020 --> 00:27:38,140 a certified doctor has to be the one to do this. Right. So I can't, I can't be a doctor and put 407 00:27:38,140 --> 00:27:44,020 into the, the, my medical record system. I think JT needs a back surgery and then have, right. Right. 408 00:27:44,100 --> 00:27:48,780 In a legal system, we've got the concept of the lawyer and the paralegal. We, we don't really 409 00:27:48,780 --> 00:27:54,460 have that for, for prior authorization. Right. So there's entire management companies that have 410 00:27:54,460 --> 00:27:59,540 doctors that never see patients. So when you talk about capacity, right, we've got a huge number of 411 00:27:59,540 --> 00:28:03,600 doctors, both working on the insurance company side and the back office revenue management side 412 00:28:03,600 --> 00:28:09,340 that are doing this. So a lot of prior authorizations get denied because they use the 413 00:28:09,340 --> 00:28:16,580 wrong code. They forgot to attach the, the, the, the, the test result or, or they didn't frankly use 414 00:28:16,580 --> 00:28:22,900 the magic words in the, in the, in the justification, right. For, for all of that. So the generative AI, 415 00:28:23,040 --> 00:28:29,040 the AI you are talking about is really coming to bear there and, and helping clean that up. Um, 416 00:28:29,140 --> 00:28:34,120 R1 RCM. Yay. What an acronym. It's actually the name of a company that, that does a lot of this 417 00:28:34,120 --> 00:28:40,200 revenue management stuff. They've really brought AI to bear in this and they've, they've been able to, 418 00:28:40,200 --> 00:28:45,500 uh, greatly reduce the rejection rates that are due to just paperwork, which is 90% of the 419 00:28:45,500 --> 00:28:49,960 rejections by the way. And I think that number is actually, right. We always hear about the horror 420 00:28:49,960 --> 00:28:54,600 they won't let my grandma have the cancer medicine she needs. That's a different story for a different 421 00:28:54,600 --> 00:29:02,200 day, but most prior authorizations, time and, and delay and rejections are, are paperwork errors, 422 00:29:02,200 --> 00:29:09,000 right? This is where just making a tremendous difference on accuracy, speed, and reducing the 423 00:29:09,000 --> 00:29:14,340 number of doctors that are doing this. So I, this is a little bit of a thought experiment, but maybe those 424 00:29:14,340 --> 00:29:19,280 doctors can get back in the caregiving game, right? Like it's really hard to get a physical, 425 00:29:19,360 --> 00:29:23,440 an appointment to get a physical here in the United States. You know, it would be good if some of those 426 00:29:23,440 --> 00:29:26,820 doctors went back and did that. I don't know if they want to, anyway, a lot of, a lot of stories, 427 00:29:26,940 --> 00:29:32,580 but that is a space where, right. We're already seeing a material change using generative AI, 428 00:29:32,800 --> 00:29:38,600 creating the, the, the letter, so to speak, it's all electronic, but, but letter, uh, getting the 429 00:29:38,600 --> 00:29:44,600 right codes and getting that prior authorization. Tremendous, like real material advantages there 430 00:29:44,600 --> 00:29:50,660 already seen now. Yeah. It's like a, another example of, uh, having the co-pilot, having the 431 00:29:50,660 --> 00:29:55,720 augmentation of what you're doing. And I, I was, I was watching a, uh, to, to try to be a little 432 00:29:55,720 --> 00:30:00,180 smart on this topic. I watched a few videos and there was one from some UC Davis people talking 433 00:30:00,180 --> 00:30:04,560 about this topic. And I forget the person's name, unfortunately, but they brought us a great 434 00:30:04,560 --> 00:30:10,120 sort of, uh, I love these kinds of like scenes where they're like, you know, anytime you go see 435 00:30:10,120 --> 00:30:14,840 a doctor, there's always a keyboard involved and they're like typing things and like mousing around. 436 00:30:14,960 --> 00:30:19,120 So there's, he didn't quite put it like this, but there's, there's almost like there's a keyboard 437 00:30:19,120 --> 00:30:25,700 between the patient and the doctor, like time-wise and sometimes physically. Right. And like that 438 00:30:25,700 --> 00:30:32,320 seems to use a layman's term, like a bunch of nonsense that doesn't need to exist. And it also 439 00:30:32,320 --> 00:30:40,360 seems like, so, so it seems like a good example where you've got, uh, I don't know the terminology, 440 00:30:40,360 --> 00:30:46,840 but I think like training and reinforcement on both sides of the transaction where the, the, 441 00:30:46,840 --> 00:30:52,360 the generative AI can like try to use the right words. And then on the other end, you can verify 442 00:30:52,360 --> 00:30:56,360 that like, these are the right words and you can do some kind of like random checking in and to make 443 00:30:56,360 --> 00:31:01,020 sure that like, what would you call it? Not the right, right words, but they, it didn't make up that 444 00:31:01,020 --> 00:31:04,720 these are the right words because it wanted to try to do this, even though it was unjustified. 445 00:31:04,720 --> 00:31:09,980 And if you allow this kind of like going back and forth, like it was, you're, you were making me 446 00:31:09,980 --> 00:31:15,180 think of this scene from, uh, you know, I don't know if you were a big Mad Max fan, but it was a TV 447 00:31:15,180 --> 00:31:20,060 show and, and, and they, they, uh, of course it's the dystopia. So I'm going to ruin the coolness 448 00:31:20,060 --> 00:31:24,760 of what we're talking about, but there's a scene where someone has to go to court and the two lawyers 449 00:31:24,760 --> 00:31:29,980 just put like a little to their own discs into a computer and, and then the disc, like, you know, 450 00:31:30,180 --> 00:31:35,680 argue the case on either side, but a positive version of that kind of, it's almost a way of 451 00:31:35,680 --> 00:31:41,060 reducing like, Oh, this was a bunch of nonsense we were doing. Uh, and because I would assume, 452 00:31:41,060 --> 00:31:46,480 because a lot of it has to do with trust is unfortunately an overloaded word, but there's 453 00:31:46,480 --> 00:31:52,380 not a way to establish trust between the insurance for a provider and the doctor where they would just 454 00:31:52,460 --> 00:31:56,400 trust the doctor would say like, yeah, they need a back surgery. And the insurance provider would be 455 00:31:56,400 --> 00:32:01,500 like, great. You, you actually, so I'm going to say, right. And it's funny to use that Mad Max 456 00:32:01,500 --> 00:32:06,400 analogy of the two discs kind of arguing against each other. So the payer side, so the insurance, 457 00:32:06,480 --> 00:32:09,800 we talked about the revenue management side, the insurance company side of this prior authorization 458 00:32:09,800 --> 00:32:15,200 is also using AI to try to automate many more of these decisions, right? A lot of the 459 00:32:15,200 --> 00:32:20,820 were automated before that. And when you talk about trust, I would say, yeah, it was, 460 00:32:20,820 --> 00:32:26,000 it was automated trust, right? This doctor repeatedly has asked for this surgery. Every 461 00:32:26,000 --> 00:32:31,340 time we've approved it, we've never detected any indications of fraud, right? There isn't 462 00:32:31,340 --> 00:32:36,700 this guy that's required, right? You know, you start to pre AI, right? All of this will go 463 00:32:36,700 --> 00:32:42,180 back to pattern matching, right? Well, just the same way that, you know, banks did credit card 464 00:32:42,180 --> 00:32:49,020 fraud for the, for, for years, right? Um, is this one person constantly like requested like 90% of 465 00:32:49,020 --> 00:32:53,380 the back surgeries we see, uh, we might want to ask some questions, right? Right. Is there, 466 00:32:53,460 --> 00:32:59,140 is there another back specialist that boy only asked for surgery for one out of a hundred of his 467 00:32:59,140 --> 00:33:04,640 patients? The rest of the times we always see him walk the protocol, what we talk about, right? Hey, 468 00:33:04,640 --> 00:33:08,440 let's, let's try the muscle relaxer first. Let's, let's take an x-ray. Oh, let's, 469 00:33:08,560 --> 00:33:12,400 let's try physical therapy, right? If somebody always follows that. And generally, by the way, 470 00:33:12,400 --> 00:33:16,260 it gets good outcomes. Cause as an insurance company, I know whether or not you got healed 471 00:33:16,260 --> 00:33:21,940 or not. Um, then you know what? I am just going to automatically. Yes. There's just a human's never 472 00:33:21,940 --> 00:33:26,760 even going to see it on the insurance side. The ones that humans see are the odd cases are the ones 473 00:33:26,760 --> 00:33:33,340 that get kicked out of like, wait a minute. Right. Um, or, you know, part surgeon requests to do a 474 00:33:33,340 --> 00:33:37,160 back surgery. You know what? We should probably ask some questions, right? Right. About this, 475 00:33:37,220 --> 00:33:43,320 that kind of thing. But seeing AI and be able to do that at a much bigger scale. Now, this is a place 476 00:33:43,320 --> 00:33:49,400 where it's not in production yet that I know of. I should put that big caveat in there, but talking 477 00:33:49,400 --> 00:33:54,220 to friends back in the insurance world, and especially in the blues world, seeing a lot of 478 00:33:54,220 --> 00:34:00,900 experimentation where they're, uh, uh, having the, the AI system make the, the, the first decisions on 479 00:34:00,900 --> 00:34:05,120 this and then having humans look at them and go, Hey, when I made the same decision, that that's kind 480 00:34:05,120 --> 00:34:09,660 of where we're at right now. Um, I don't know whether to call it experimentation or training, 481 00:34:09,660 --> 00:34:15,020 uh, you know, the model and things like that, but it is setting up this case, right? I do know of a 482 00:34:15,020 --> 00:34:21,220 case of where somebody is using this on the insurance side as an experimentation and they're getting a lot 483 00:34:21,220 --> 00:34:26,760 of R1 or CM requests. So in my head, it is that exact scenario you talked about, right? These two AI 484 00:34:26,760 --> 00:34:31,560 bots fighting over and deciding whether or not you should get the back surgery. I, and that could 485 00:34:31,560 --> 00:34:36,620 be creepy. That can feel very uncomfortable. Like, wait a minute, why are the computers making these? 486 00:34:36,780 --> 00:34:41,620 Yeah. Well, right. That's, but, well, I think, I think in the, in that scenario, what you would want 487 00:34:41,620 --> 00:34:46,820 instead of like the max headroom thing is, you know, to make it, to stack it, to be absurd, you would 488 00:34:46,820 --> 00:34:53,060 almost want a third AI or, or a third disc you put in there that was like, all right, now you were 489 00:34:53,060 --> 00:34:56,660 going to standardize how this is done. So you don't have to do that in the first place. Right. It's 490 00:34:56,660 --> 00:35:01,060 just like, we're going to come up with a better scheme because, you know, uh, I, I, you know, 491 00:35:01,060 --> 00:35:06,100 as, as a U S person, like you would kind of know this intuitively, but maybe other people is like 492 00:35:06,100 --> 00:35:10,980 the problem you just described assumes a one-to-one relationship between provider and a health 493 00:35:10,980 --> 00:35:16,900 insurance company, but we have a uncountable to uncountable relationship. I guess you could count 494 00:35:16,900 --> 00:35:21,180 the number of insurance providers in the, in the States, but like, there's a lot of different 495 00:35:21,180 --> 00:35:26,460 things. So not only, I mean, that's where things get crazy is there just so many combinations 496 00:35:26,460 --> 00:35:32,300 of, of that single discussion that happens that you do need, you do need some augmentation to 497 00:35:32,300 --> 00:35:37,960 deal with all of that, that those con is a permutation or a combination. I was bad at math, 498 00:35:37,960 --> 00:35:44,120 but all of those various, uh, mix. Yeah. I mean, look, sign me up to burn the current U S healthcare 499 00:35:44,120 --> 00:35:50,440 system down and, and, and come up with a new one. Um, but we, we've got to make do with the one we 500 00:35:50,440 --> 00:35:59,180 have. Um, and yeah, a lot of those problems that you just talked about, right. Are so many tech firms 501 00:35:59,180 --> 00:36:04,460 or, or large retail firms have thought they could just crack this healthcare nut and they haven't. 502 00:36:04,460 --> 00:36:10,520 It's been a train wreck lately of, of, of, you know, folks try new things in the healthcare world 503 00:36:10,520 --> 00:36:14,240 from a business model perspective and absolutely nothing stuck the stickier. 504 00:36:15,320 --> 00:36:21,740 So, so as a transition to like general digital healthcare stuff, you know, as, as we're talking 505 00:36:21,740 --> 00:36:27,500 through this, one of the things I was wondering is like, who, who does this innovation in healthcare? 506 00:36:27,500 --> 00:36:33,420 Like, I don't, I mean, maybe I'm totally wrong, but I don't, I don't think I wouldn't imagine that 507 00:36:33,420 --> 00:36:39,620 an even kind of a medium, large size hospital system. They have a lot of programmers who are like 508 00:36:39,620 --> 00:36:44,760 writing applications and doing this. And so like a lot of, in my mind, a lot of what we talked about 509 00:36:44,760 --> 00:36:50,600 is just like, here's, here's like, I mean, generic, and here's a general purpose, generative AI engine. 510 00:36:50,840 --> 00:36:55,140 And now we need to make a bunch of, we need to write a bunch of applications. And that might be like, 511 00:36:55,580 --> 00:37:01,180 you know, not the most beautiful AI, beautiful UIs, but like some kind of application development 512 00:37:01,180 --> 00:37:05,020 you're doing. But where is that? Where's that done in the healthcare system? 513 00:37:05,020 --> 00:37:11,400 So most of the healthcare back office stuff is, is outsourced these days, right? Or vendor sourced 514 00:37:11,400 --> 00:37:16,720 outsourced as a weird connotation to me. Um, right, right, right. Like, like this revenue management 515 00:37:16,720 --> 00:37:20,860 thing that I was just talking about that providers and hospitals do, they generally contract with 516 00:37:20,860 --> 00:37:26,660 firms to do that. Now there are exceptions, the very large, uh, uh, healthcare systems in the world 517 00:37:26,660 --> 00:37:32,960 are in the U S right. Uh, I'm thinking of the HCA is, which kind of covers the whole Southeast United 518 00:37:32,960 --> 00:37:37,740 States, uh, with, with hospitals, excuse me, with hospitals and clinics. They're big enough. They have 519 00:37:37,740 --> 00:37:42,400 a lot of their own developers. Uh, like, frankly, they're doing a lot of development for stuff that 520 00:37:42,400 --> 00:37:46,900 we talk about, right. Backstage development and things like that, even. Um, so they're actually 521 00:37:46,900 --> 00:37:53,480 themselves doing, uh, uh, you know, a lot of this, but most, right. When you think of hospitals, 522 00:37:53,480 --> 00:37:58,420 back offices, these business processes, there's vendors that are, that are doing this innovation 523 00:37:58,420 --> 00:38:03,460 that are, are right. And they're doing it for their own selfish needs of, Hey, if you're still 524 00:38:03,460 --> 00:38:07,040 going to pay me a dollar per prior authorization, it's not that, by the way, I just make that up. 525 00:38:07,040 --> 00:38:13,840 Um, you know, and I can do that without having an adopter in the mix. I can make a lot of money. 526 00:38:14,340 --> 00:38:20,120 Um, that, that kind of thing. So who like, like in the, at least in the States, like who are those 527 00:38:20,120 --> 00:38:25,140 vendors? Like I know Epic is one, but that's it. That's, that's my novice understanding, 528 00:38:25,140 --> 00:38:29,680 but who like kind of what's the set of those vendors? So there's the EMR providers that, 529 00:38:29,680 --> 00:38:33,840 that, you know, we, we, you talk to in the hallways of conferences and stuff like that. 530 00:38:33,840 --> 00:38:39,740 And they're trying to experiment with it. Epic, Cerner, Meditech, those kinds of people. Um, 531 00:38:40,180 --> 00:38:44,720 R1 RCM is, is a revenue management. I hate to keep using that. Uh, good friends, 532 00:38:44,780 --> 00:38:49,280 that of architecture there, um, is why I know them so well. Um, you know, things like that, 533 00:38:49,400 --> 00:38:53,060 the insurance companies, they're doing this themselves. They, they, they have traditionally 534 00:38:53,060 --> 00:38:57,740 done a lot of software development and experimentation. Right. That would make sense. 535 00:38:58,040 --> 00:39:03,020 Yeah. But then we're also seeing like the big SIs, right. The cognizance, the Accenture's, 536 00:39:03,360 --> 00:39:08,000 Deloitte's, all of those folks are doing a lot of this and trying a lot of this and then, 537 00:39:08,000 --> 00:39:12,160 uh, bringing it back to their customers. Um, that's where we're seeing a lot of this 538 00:39:12,160 --> 00:39:18,220 happen right now. And, and then like, uh, again, in the scope of, of the, the, of the U S like, 539 00:39:18,580 --> 00:39:24,700 do do things like the VA and, uh, I always get Medicare and Medicaid mixed up unless I think about 540 00:39:24,700 --> 00:39:30,400 the little poem about them or whatever, but like, like, if you look at those big, I, I wouldn't call 541 00:39:30,400 --> 00:39:34,860 them like shadow healthcare, but like people don't always think about those as huge monolithic 542 00:39:34,860 --> 00:39:39,340 healthcare institutions. So they have like their own, yeah. I mean, innovation staff, their, 543 00:39:39,340 --> 00:39:42,900 their own development staff, or do they also just work with these other providers? 544 00:39:43,220 --> 00:39:49,020 The VA works a lot with providers. Now, I think that what I don't know, and actually it's your, 545 00:39:49,020 --> 00:39:52,760 your question just made me want to go ask questions and research over the next couple of weeks. Right. 546 00:39:52,960 --> 00:39:58,640 There's been in the U S the whole move to, uh, a lot of divisional development by the business, 547 00:39:58,760 --> 00:40:03,580 by the government itself. Right. Whether it's stuff we do at VMware with some of the military branches, 548 00:40:03,580 --> 00:40:09,300 um, you know, in Tanzu world, right? Like software factories, right? Software factories and things 549 00:40:09,300 --> 00:40:14,480 like that, but also just in other parts of the, of the government as well. Um, I don't know if 550 00:40:14,480 --> 00:40:21,220 they're making an impact on, on the VA, but the VA generally big exception here has always been very 551 00:40:21,220 --> 00:40:26,920 vendor sourced. Um, yeah. Cerner being the one that that's, you know, having a little bit of a bump in 552 00:40:26,920 --> 00:40:32,060 the road, trying to roll out a new EMR there. As a matter of fact, a little bit is a, a big bump in 553 00:40:32,060 --> 00:40:38,240 the road, trying to roll that out. Um, but, uh, so I don't see a lot of government activity. 554 00:40:38,920 --> 00:40:44,900 Medicare, right. Is a more of a set of regulations than it is an insurance company. Uh, uh, news 555 00:40:44,900 --> 00:40:50,420 to medic, I guess outsource to various insurance companies under the guise of Medicare advantage. 556 00:40:50,700 --> 00:40:56,200 So, yeah, yeah, I guess, I guess the, the Medicare and Medicaid, you're, I could imagine there are a lot 557 00:40:56,200 --> 00:41:02,020 more of the, uh, revenue management is, as you say, just sort of like managing that back end of stuff. 558 00:41:02,060 --> 00:41:07,600 There are as much regulatory frameworks as they are actually at. And so most of that work, 559 00:41:07,680 --> 00:41:11,280 the thing I just talked about, right. The revenue management and the insurance company, 560 00:41:11,280 --> 00:41:15,360 that's the same for Medicare and Medicaid or commercial that you get through your employer, 561 00:41:15,360 --> 00:41:20,640 or maybe even individual, individual insurance, you buy yourself that struck those structures and 562 00:41:20,640 --> 00:41:25,560 how that money flows is all the same. There's just a regulatory framework and some price controls 563 00:41:25,560 --> 00:41:30,740 around it for Medicare and Medicaid. Frankly. Okay. Yeah. I'll have to, uh, have to, it'd be 564 00:41:30,740 --> 00:41:35,680 interesting to see like what in the UK, like what the NHS does and how they're structured. And I think, 565 00:41:35,780 --> 00:41:39,300 you know, I think where I live in the Netherlands, I'll have to ask about that too, because the 566 00:41:39,300 --> 00:41:45,180 Netherlands has this weird, well, I I'm sure the Dutch love it, but you know, if compared to the system, 567 00:41:45,180 --> 00:41:49,660 we just went over this kind of weird in a good way system of, I don't know, there's like five or 568 00:41:49,660 --> 00:41:54,360 six insurance providers. And as far as I can tell, they're so regular, they're basically the same, 569 00:41:54,600 --> 00:41:58,780 uh, but whatever, you know, they've got that interesting hybrid system, but anyhow. So, 570 00:41:58,880 --> 00:42:03,380 so the other thing that I, you know, I was curious to ask about is just like, in addition to AI, 571 00:42:03,380 --> 00:42:10,580 like other, whether it's like, well, let's, let's call this business innovation made possible by 572 00:42:10,580 --> 00:42:15,440 digital stuff. Like, like that's, that's interesting to go over. And, and the thing that brought this up 573 00:42:15,440 --> 00:42:19,840 is I, you know, I was just looking through my, um, whatever, whatever, I guess you, is it called 574 00:42:19,840 --> 00:42:26,800 ham? Whatever it is between email you like and spam, like promotions that you have. And I got one, 575 00:42:26,800 --> 00:42:32,460 of course, well, of course I got one from the Amazon prime going over like their prime healthcare. 576 00:42:32,880 --> 00:42:38,540 And I was looking through it and it seemed like, I don't know, it seemed interesting, but then, 577 00:42:38,600 --> 00:42:42,600 but then also I know that like, you know, we even talked about this a couple of years ago, 578 00:42:42,600 --> 00:42:46,400 there, there have been some experiments like at Walmart and other places to have, 579 00:42:46,500 --> 00:42:51,120 and I think CVS had some of these to have sort of like, you'll have to tell me the term for this, 580 00:42:51,180 --> 00:42:57,080 but like mid tier, you know, the middle of healthcare stuff done more affordably. So like, 581 00:42:57,560 --> 00:43:02,700 well, first of all, what do you call that whole area? And then like, what's going on in that area? 582 00:43:02,700 --> 00:43:06,500 Like, is it working out? Like, what are the, what's up with it at the moment? 583 00:43:06,500 --> 00:43:13,080 You know, six questions in one there. Um, the, the, uh, key answer to what you're looking for is 584 00:43:13,080 --> 00:43:19,280 dock in the box. Um, and that's where the, you, right, right, right. Carve out a corner of your 585 00:43:19,280 --> 00:43:25,300 Walmart, frankly, not frankly, but, but carve out a corner of your Walmart and set up a general 586 00:43:25,300 --> 00:43:31,120 practice clinic. So it's something more than urgent care. Although some of those were set up as urgent 587 00:43:31,120 --> 00:43:38,760 care too. Like, you know, I, I, I cut my hand or, or, uh, hit my head. Right. Right. Some of those 588 00:43:38,760 --> 00:43:45,640 kinds of things. Um, but also to be more general practice. So the one thing we know in the United 589 00:43:45,640 --> 00:43:51,380 States is there is not, and you and I briefly touched on this earlier capacity, right. Especially 590 00:43:51,380 --> 00:43:58,480 in the rural areas to just get folks in, to get a physical, to, uh, you know, checkup, or even when 591 00:43:58,480 --> 00:44:04,760 they don't feel good to actually go see a doctor. Um, because if I can see you early on and treat 592 00:44:04,760 --> 00:44:09,620 you, I can keep you from getting worse. Right. So, so it's actually, even if I'm only going to look 593 00:44:09,620 --> 00:44:14,240 at it from a system dollars perspective, I would rather much rather you get into the doctor more 594 00:44:14,240 --> 00:44:21,020 often, frankly, like that's okay. Um, I'm going to, I'm going to watch how much of the big fancy 595 00:44:21,020 --> 00:44:25,800 procedures that are really expensive you get, but I, I really want you to go to the doctor fairly 596 00:44:25,800 --> 00:44:32,260 often. And that's not very possible. Like it's even in a very metro area that I am right. 597 00:44:32,260 --> 00:44:38,560 Getting an appointment to get a physical is very hard. So the idea that, uh, um, the Walmarts 598 00:44:38,560 --> 00:44:43,380 in the world, the Walgreens that already have footprints in these rural areas, uh, in a large 599 00:44:43,380 --> 00:44:48,380 number, if they could stand up these clinics, the concept was, boy, we could increase capacity. 600 00:44:48,600 --> 00:44:53,420 But then if I'm Walmart, if I get big enough, I can then negotiate with insurance companies 601 00:44:53,420 --> 00:44:59,540 like I do with the maker of paper towels, right? Like Walmart has a huge advantage of being able 602 00:44:59,540 --> 00:45:04,540 to go to their suppliers and go, no, you're going to, I'm only going to pay 50 cents. I'm 603 00:45:04,540 --> 00:45:08,200 not going to pay a dollar and you're going to like it. Right. Because I'm so big and I'm 604 00:45:08,200 --> 00:45:11,880 so key to the market and things like that. Kind of inverting that to the insurance company 605 00:45:11,880 --> 00:45:16,700 and going, no, no insurance company. I'm so vital to you being able to sell your insurance because 606 00:45:16,700 --> 00:45:21,580 I'm so much a percentage of your doctors. You're going to pay me a dollar instead of 50 cents. Like 607 00:45:21,580 --> 00:45:28,940 that was the quick example of how this would work. And it just, and I don't know why I, I, I mean, 608 00:45:28,940 --> 00:45:35,220 I can be snarky, right. You know, they just couldn't get in there. So, but Walmart just in the last 609 00:45:35,220 --> 00:45:41,600 month announced they're shutting down this, this entire concept and that's huge, right? And it's 610 00:45:41,600 --> 00:45:47,200 having some secondary for some big vendors, by the way, to think EMR providers, cloud providers and 611 00:45:47,200 --> 00:45:54,540 stuff like that. Um, but, but that Walmart's doing this Walgreens tried the same thing. Uh, they bought 612 00:45:54,540 --> 00:45:59,680 village MD, which was these, these doctors that would be in shopping centers and we're going to bring 613 00:45:59,680 --> 00:46:05,200 them into wall there, their Walgreens clinics. They've had to walk that back just at, it hasn't 614 00:46:05,200 --> 00:46:12,680 been financially, uh, uh, really good. It hasn't worked. Um, and so they're shutting that down. 615 00:46:13,100 --> 00:46:18,620 There's been a lot of these retail companies that I'm going to call Amazon a retail company for the 616 00:46:18,620 --> 00:46:24,340 purpose of my discussion here. Um, I know there are many things, right, but our retail companies 617 00:46:24,340 --> 00:46:29,500 trying to solve this problem, I think because a little bit out of arrogance, they thought they 618 00:46:29,500 --> 00:46:34,480 could do it. Nobody else would. Some of it's their scale. We're big enough. We can really do this. 619 00:46:35,180 --> 00:46:42,020 It just hasn't worked. We've seen over the last six months, especially so many of these things being 620 00:46:42,020 --> 00:46:48,160 walked back and, and shut down. And just, it's not very profitable. You could look at CVS, Walgreens, 621 00:46:48,160 --> 00:46:55,260 uh, uh, Amazon, uh, in the past with, with several iterations of this and go back to Amazon and Haven, 622 00:46:55,260 --> 00:47:00,800 right? That was their, uh, partnership with Berkshire Hathaway. And was it JP Morgan? Excuse me, 623 00:47:00,800 --> 00:47:06,820 JP Morgan? Yeah. Where they were going to solve insurance and that just didn't work either. It's 624 00:47:06,820 --> 00:47:12,060 such a regulatory mess. I mean, uh, you know, I, I joke that we have to burn it all down. You're, 625 00:47:12,140 --> 00:47:16,340 if you're going to make a material change to the U S healthcare system, it's probably political and 626 00:47:16,340 --> 00:47:21,000 regulatory. That's going to do it. And we don't have the environment for that to, to come back and 627 00:47:21,000 --> 00:47:26,540 talk about Amazon health, right? Which is what they just have been, been, uh, not spamming, but hamming, 628 00:47:26,540 --> 00:47:33,400 to use your term everybody about, uh, um, of late. That's kind of an iteration on Teladoc, something 629 00:47:33,400 --> 00:47:40,240 that existed before COVID, but really exploded during COVID and became normal during COVID. The idea 630 00:47:40,240 --> 00:47:46,900 that I could chat with a doctor on my phone, um, and get some basic medical or, or maybe do a video 631 00:47:46,900 --> 00:47:53,300 call, not, not different from what you and I are doing here with a doctor for many things. The doctor 632 00:47:53,300 --> 00:47:59,460 doesn't have to touch and feel, um, from, from that perspective, maybe it's cold and flu. In fact, 633 00:47:59,460 --> 00:48:06,200 if I'm the doctor, I don't want you in my office. I do not. Right. Sure. Spread it. Yeah. So I'm 634 00:48:06,200 --> 00:48:13,160 interested to see if this works. Um, and it works just by the meaning, can you get enough people to 635 00:48:13,160 --> 00:48:19,240 use it? Um, I will say, I assumed Amazon was going to require you to use their pharmacy and, and, 636 00:48:19,240 --> 00:48:24,840 and deliver their drugs and they aren't. That is very, very interesting. Um, I'm, I'm sure you can 637 00:48:24,840 --> 00:48:29,720 use Amazon pharmacy. Um, that has been something that's been pretty successful for Amazon by the 638 00:48:29,720 --> 00:48:34,540 way. And the healthcare is they bought pill pack five years ago now. Boy, don't quote me on that 639 00:48:34,540 --> 00:48:40,940 timeline. Um, and I've really done a good job of, you know, sending you your blood pressure medicine 640 00:48:40,940 --> 00:48:45,680 every month and all of that kind of stuff and making some good money at it. But, but trying to see if 641 00:48:45,680 --> 00:48:51,700 this, this, this Teladoc method works, uh, I hope it does. I will say I use a variety of it. I, 642 00:48:51,880 --> 00:48:56,060 a different vendor. Um, if you've ever heard, we talk about healthcare. I've, I've tried to go this 643 00:48:56,060 --> 00:48:59,800 whole conversation without mentioning zoom care, but I'm going to mention it once. Cause I'm a fan, 644 00:48:59,800 --> 00:49:06,160 not an investor. Um, that's a really kind of a hybrid of the two. Um, and, and so it works. And again, 645 00:49:06,160 --> 00:49:12,140 if we can get folks that don't have regular access to doctors to use that, it can be fairly 646 00:49:12,140 --> 00:49:17,400 inexpensive. The magic bullet for Amazon will be, can they get the insurance reimbursement? 647 00:49:18,180 --> 00:49:25,000 So will the insurance company pay Amazon for that visit at a rate, not the same as you going into a 648 00:49:25,000 --> 00:49:29,140 doctor's office, but enough to make money. That always seems to be the crux of this. 649 00:49:29,500 --> 00:49:34,560 Yeah. Well, you did a good job of responding with 10 topic ideas to my six questions. So, 650 00:49:34,640 --> 00:49:40,760 so that's, we, we, we, we matched up there. And I think like, like, uh, of, of many of the things 651 00:49:40,760 --> 00:49:47,320 there, what's, what's, what's interesting is to try to figure out, I'll, I'll say constraints, 652 00:49:47,320 --> 00:49:52,480 but the constraints to deal with there, uh, because, you know, the first one you brought up is, 653 00:49:52,480 --> 00:49:59,240 I mean, it's one of the, um, the old micro Porter five forces of strategy is the relationship between 654 00:49:59,240 --> 00:50:06,540 supplier and supplied to, right. And, and like, if you, if you as a company or an organization 655 00:50:06,540 --> 00:50:11,500 control your suppliers, then, you know, that's kind of, you, you know, these, as you say, 656 00:50:11,560 --> 00:50:17,680 dock in a box we're going for is that if we're a giant company, our suppliers, um, we can command 657 00:50:17,680 --> 00:50:24,200 a lower price from our suppliers. And, you know, the positive version is also, we can maybe drive 658 00:50:24,200 --> 00:50:30,060 some efficiency and standardization, right? Like the suppliers sort of kind of would like to sell 659 00:50:30,060 --> 00:50:35,000 to less people to have more of a guaranteed revenue stream. I don't know. I mean, that all depends 660 00:50:35,000 --> 00:50:40,500 right on, on, on, on, and again, that dynamic of control between the supplier, the seller and the 661 00:50:40,500 --> 00:50:45,680 buyer. But so there's, there's like that one constraint, but then as, as you bring up, it didn't 662 00:50:45,680 --> 00:50:51,360 work. It wasn't like a magic fantasy, which could mean all sorts of things, but you know, 663 00:50:51,360 --> 00:50:59,220 that, that is really interesting that you can't get the efficiencies of paper towel buying 664 00:50:59,220 --> 00:51:06,120 for cold diagnostics or something like that. Right. And so, and yes, then the other thing 665 00:51:06,120 --> 00:51:10,320 that you bring up that is interesting is, you know, you especially see this when you have kids 666 00:51:10,320 --> 00:51:14,820 and you learn this when you've had like a second or third kid is really, you just need to call 667 00:51:14,820 --> 00:51:20,520 and say like, my kid has 102 fever and they're like, call me back tomorrow. If they have 102 fever 668 00:51:20,520 --> 00:51:24,960 and it's having a third day, then actually bring them in. Right. Whereas, you know, I don't know 669 00:51:24,960 --> 00:51:30,380 about you, but we had your first kid just like, Oh my God, he seems, they seem sluggish. I'm going to 670 00:51:30,380 --> 00:51:36,800 the doctor. Right. Uh, and so there is kind of that screening that you would hope like in telemedicine 671 00:51:36,800 --> 00:51:41,880 and doc in a box stuff that you could just, I mean, especially in healthcare triage means something a 672 00:51:41,880 --> 00:51:47,900 little more dramatic, but you could kind of like filter out like, like things that are easier to 673 00:51:47,900 --> 00:51:53,640 solve that don't consume your constrained resource of the healthcare people. Yeah, for sure. And the idea 674 00:51:53,640 --> 00:51:59,140 that, look, I can prescribe you this and that's probably going to solve it like with, with a 90% 675 00:51:59,140 --> 00:52:03,380 confidence. And it doesn't have a downside of trying, right. That's always the other side of 676 00:52:03,380 --> 00:52:09,400 I'm just going to like, have you go take that medicine and try it. And, and that's probably 677 00:52:09,400 --> 00:52:13,840 going to solve you. And it saved you from driving and it saved you from standing in another crowd of 678 00:52:13,840 --> 00:52:19,020 people and giving it to other people. Right. But it also frankly gets people to come and talk about 679 00:52:19,020 --> 00:52:26,180 things that they may not want to. So the idea I can, I can say things to a chat bot that I might not 680 00:52:26,180 --> 00:52:31,360 want to say to you in a room. Right. And again, this is where we get into things like sexual health and 681 00:52:31,360 --> 00:52:35,440 all of that. And while they're, they're, they're tongue in cheek and people like to giggle about 682 00:52:35,440 --> 00:52:41,660 them, they're really important. Right. But there are other medical issues too, right. That, that people 683 00:52:41,660 --> 00:52:47,700 are just frankly embarrassed about their bodies or whatever. And if you can get them to open up in a 684 00:52:47,700 --> 00:52:53,740 remote sense, we do see people say things in this environment, like you and I are that they wouldn't 685 00:52:53,740 --> 00:52:59,680 necessarily say in person or being comfortable with, with a person. And as a doctor, that's a very 686 00:52:59,680 --> 00:53:04,740 positive thing. Right. You might say something that has, Hey, I know you're uncomfortable, but I really 687 00:53:04,740 --> 00:53:10,620 need you to get in here. And that's a viable outcome to that. But a lot of times, if I can get people to 688 00:53:10,620 --> 00:53:15,760 talk about them, I can get them happier and healthier, right. Mental health is just as important as 689 00:53:15,760 --> 00:53:22,560 physical health. Right. And so. Right. I'm hopeful, right. This is the case where I, I, well, I generally 690 00:53:22,560 --> 00:53:27,500 want them to, I don't want to spend Amazon and we more normalized. Yeah. You know, that's, that's an interesting 691 00:53:27,500 --> 00:53:32,020 one maybe that to, to, to, to bridge to other interesting things going on in healthcare is 692 00:53:32,020 --> 00:53:39,040 the, the, the mental health angle seems almost like, I mean, I'm, I'm, I'm, I'm well past the 693 00:53:39,040 --> 00:53:43,880 thing of, of doing the, uh, the, the anti-pattern of some outsider amateur trying to diagnose a 694 00:53:43,880 --> 00:53:50,380 complicated system. But that said, as a consumer of the complicated system, like, you know, uh, until 695 00:53:50,380 --> 00:53:55,380 COVID stuff, like the therapist I used to see, uh, back in Texas, like wouldn't see people online. 696 00:53:55,560 --> 00:54:01,780 And then they had to, and they realized it's totally fine or, or, you know, or, or at least 697 00:54:01,780 --> 00:54:07,500 it's better than a lot better than zero, uh, so to speak. And so I don't know if this is true, 698 00:54:07,500 --> 00:54:13,640 but it seems like the supply of therapists is maybe less constrained than the supply of doctors 699 00:54:13,640 --> 00:54:18,300 or, you know, I don't know, maybe it could be, maybe that's just like a supply and demand problem. 700 00:54:18,300 --> 00:54:22,800 Like not as many people see therapists as, as would be helpful. So there, there's a better 701 00:54:22,800 --> 00:54:28,700 equilibrium there, but it all, that, that, that seems like a good experimentation area that you 702 00:54:28,700 --> 00:54:35,880 could get a lot of learning about how to do, I guess it's remote stuff, but also just the 703 00:54:35,880 --> 00:54:40,020 efficiency of it, because I think it's also, I mean, the amount of money that I pay my therapist 704 00:54:40,020 --> 00:54:46,780 per session is, is relatively low to back surgery, right? So the, the, the, the backend part seems 705 00:54:46,780 --> 00:54:51,940 like it's a lot easier than, uh, than, than others. Yeah. I, I, I, you know, there were more 706 00:54:51,940 --> 00:54:59,100 therapists than, than, than medical doctors and I would strongly disagree. I would say, uh, one of 707 00:54:59,100 --> 00:55:05,440 the top five biggest problems we have right now in the U S anyway, I don't know, uh, how this plays in 708 00:55:05,440 --> 00:55:11,940 Europe or, or, or Asia or other places is folks that want mental health treatments and, and whether 709 00:55:11,940 --> 00:55:18,200 that's just a therapist, just a therapist or even something more right. Uh, versus the, the supply 710 00:55:18,200 --> 00:55:24,400 is really off. Like it is really hard to find a therapist, to find a therapist that your insurance 711 00:55:24,400 --> 00:55:32,040 covers. That's a very U S thing. But, um, and then get an appointment when somebody is in like 712 00:55:32,040 --> 00:55:36,840 distress, right? Because it's one thing to say, well, you know, what would really help me is some 713 00:55:36,840 --> 00:55:43,980 therapy. Okay. It's going to take me three weeks to get an appointment versus a teenager or, or somebody 714 00:55:43,980 --> 00:55:49,880 else who's been really some distressed environment, right? Having some very ideation type thoughts and 715 00:55:49,880 --> 00:55:54,980 stuff like that. And then it takes three weeks to get and find somebody. Um, and it's all right, 716 00:55:54,980 --> 00:56:01,460 right, right. You need like urgent care for therapy basically, which is, uh, yeah, yeah. Yeah. Okay. 717 00:56:01,460 --> 00:56:06,040 Okay. Now that's, that's, that's good to know. And I mean, I guess it gets back, you know, the, the, 718 00:56:06,040 --> 00:56:11,600 the question I was having about constraints is it seems like the number one constraint is just supply. 719 00:56:12,040 --> 00:56:18,180 Like there's just not enough people, uh, who can supply the healthcare that you, and so, you know, 720 00:56:18,180 --> 00:56:22,100 just to repeat it, it goes back to what we're saying. It's any way you can augment that supply, 721 00:56:22,100 --> 00:56:27,780 there's going to be huge advantages that you have. And then you also have the, the, I don't know if 722 00:56:27,780 --> 00:56:34,020 it's compounding, but you also have all of the, uh, the bottlenecks and friction of regulation in that 723 00:56:34,020 --> 00:56:39,000 very many to many complicated system we were talking about that just kind of piles things on top of it, 724 00:56:39,040 --> 00:56:43,760 but maybe to be a little more optimistic, like what, what are, what are other areas in healthcare that 725 00:56:43,760 --> 00:56:49,280 like, you know, if you look at like applying it or just like better software or whatever that, 726 00:56:49,280 --> 00:56:54,900 that you see people, uh, being successful with. Yeah. I'll come back to some of the stuff we 727 00:56:54,900 --> 00:56:58,860 already talked about that really does have me excited, right? The research side of this, 728 00:56:58,860 --> 00:57:06,980 that is starting to be really cool. Um, I think the training aspects of this getting doctors to be 729 00:57:06,980 --> 00:57:14,740 able to do more intricate, harder things and giving them experiments. Uh, uh, I don't know how super 730 00:57:14,740 --> 00:57:20,340 public it is, so I'm not going to use their name, but there is a vendor, uh, that does a lot of robotic 731 00:57:20,340 --> 00:57:26,080 type surgery kind of things like, like tools that can help a doctor that is kind of now like using 732 00:57:26,080 --> 00:57:33,500 technology, all the buzzwords, right? AI and all sorts of stuff, but to, to allow kind of simulations 733 00:57:33,500 --> 00:57:39,700 of that, like how can I, as a doctor don't necessarily get the touch and feel, but walk through that 734 00:57:39,700 --> 00:57:46,140 intricate cardiac surgery, removing a blockage in a very touchy space and things like that, and allow me to do 735 00:57:46,140 --> 00:57:52,040 it or allow complications to pop up in the middle of that. Right. Um, uh, VR. Oh, right. Right. That's 736 00:57:52,040 --> 00:57:58,020 the acronym. I couldn't pull out a second ago. Um, using some of that. There's a lot of cool things. 737 00:57:58,020 --> 00:58:04,440 I think, uh, personally, we're going to make some real advances in some really tough areas. We haven't 738 00:58:04,440 --> 00:58:10,220 been able to crack before or, or, or fast enough, whether it's some cancers out there, I'll use the 739 00:58:10,220 --> 00:58:16,440 one that I'm passionate about right now, Alzheimer's, right. ALS, some of those things, I really bet in 740 00:58:16,440 --> 00:58:22,140 the next, you know, four to five years, we see some real, but no, I don't want to say cures that that's 741 00:58:22,140 --> 00:58:28,160 but, but some treatments and being able to really make some steps forward where we haven't before and 742 00:58:28,160 --> 00:58:35,200 give people some real hope and, and positive change in their life environments. And then I'll come back 743 00:58:35,200 --> 00:58:40,360 to as epic, right. To talk about once. Well, somebody's being the type of drugs that is that's 744 00:58:40,360 --> 00:58:45,100 the diabetic drug that, that became everybody's using for weight loss. And now we're finding all 745 00:58:45,100 --> 00:58:51,660 these secondary and tertiary, uh, uh, benefits too. But if we can really start to solve some of the 746 00:58:51,660 --> 00:58:55,660 obesity issues in the U S and some of those are genetics, some of those are behavioral, 747 00:58:55,660 --> 00:59:01,140 all of that kind of stuff, we can really start to impact the aging curve. So all of that takes 748 00:59:01,140 --> 00:59:07,760 technology behind the scenes. Right. And, and, um, I'm super excited to, to kind of start to see some 749 00:59:07,760 --> 00:59:11,840 impacts there in the next four, you know, like I said, kind of five-year horizon, which is what I 750 00:59:11,840 --> 00:59:17,720 think about. Um, it could be really, really cool. Yeah. You know, there is, I don't know if this is 751 00:59:17,720 --> 00:59:22,800 necessarily the case, but at least as an industry, it seems like, you know, like going all the way back 752 00:59:22,800 --> 00:59:27,840 to the beginning, we were talking about how, when it comes to learning how to apply generative AI 753 00:59:27,840 --> 00:59:32,540 healthcare is pretty good, pretty well set up because they already have an experimental mindset 754 00:59:32,540 --> 00:59:38,920 in place. And then there's all, it's also already, you know, they know how to work within a governed 755 00:59:38,920 --> 00:59:44,580 framework and, and how to do things carefully like that, which, which, you know, little startups 756 00:59:44,580 --> 00:59:49,860 don't, you know, like, like, like all those things. Then, you know, there's to think about, 757 00:59:50,480 --> 00:59:57,180 so I was asking where the innovation happens and it can be a drawback. If you have a handful of people 758 00:59:57,180 --> 01:00:01,780 doing that innovation, if they're not innovating, if they're not changing, however, if they are working 759 01:00:01,780 --> 01:00:07,880 on new ways of doing things, there's a benefit because they, if you have, I'm just making the 760 01:00:07,880 --> 01:00:12,560 numbers up. You have five people supplying the software, quote unquote, everyone uses. That means 761 01:00:12,560 --> 01:00:16,840 once, once those five people figure it out, they can quickly get it out to everyone. You don't have 762 01:00:16,840 --> 01:00:21,240 to rely on independently doing that. So that has a huge advantage to it as well. And, and, you know, 763 01:00:21,280 --> 01:00:26,300 I think the other, the other advantage is just like, this is overstating it a little bit, but a lot of 764 01:00:26,300 --> 01:00:31,960 those tools are just ready to be used and people can just start experimenting with how to apply 765 01:00:31,960 --> 01:00:38,220 like generative AI or other things. They just sort of like have to will it. They have to decide to do it 766 01:00:38,220 --> 01:00:43,220 and start to deploy it, which, which I think is handy. And, you know, to, to be a little crass 767 01:00:43,220 --> 01:00:47,480 commercially, right? Like that is not some of the, some of the people you mentioned, I know they use 768 01:00:47,480 --> 01:00:53,160 the, uh, you know, our Tanzu, uh, application service or Tanzu platform. Uh, and so it's a good 769 01:00:53,160 --> 01:00:58,500 example again of that, that limited supplier thing where generative AI stuff and managing all your 770 01:00:58,500 --> 01:01:03,040 application workflows already built into that. You know, you can just start using it. And about this 771 01:01:03,040 --> 01:01:07,980 enterprise AI stuff who are using that in, in, in the Tanzu platform, other ones, it's just in there. 772 01:01:07,980 --> 01:01:14,100 So you can decide to start doing it. You don't have to, uh, you know, download one of the models and 773 01:01:14,100 --> 01:01:19,300 figure out how to do everything with it, which, which is handy. It's, it's unlike some other 774 01:01:19,300 --> 01:01:24,100 innovations in the past where you really do have to do it all from, uh, from scratch, uh, which, 775 01:01:24,220 --> 01:01:28,780 which is helpful. Well, as, as always, this has been great. Uh, you know, you, I, I know you've 776 01:01:28,780 --> 01:01:32,960 got a, uh, where do you put your stuff nowadays? Probably in LinkedIn, like, like, like everywhere 777 01:01:32,960 --> 01:01:36,960 else, where, where would you point people? I did. I still hang out on the X occasionally, 778 01:01:36,960 --> 01:01:42,840 um, or, or what are the, the site formerly known as Twitter, uh, uh, and blue sky as well, but 779 01:01:42,840 --> 01:01:49,820 circle JTP will find me on most social media. Uh, and then JT Perry under LinkedIn is, is where 780 01:01:49,820 --> 01:01:54,100 I'm doing a lot of stuff as well. Yeah. I, you know, I think for most, like most people, 781 01:01:54,100 --> 01:01:59,680 Twitter is now your ex social media location instead of your, your current one. Well, with 782 01:01:59,680 --> 01:02:04,240 that clever turn of phrase, uh, as always, this has been a Tom's we talk. If you want 783 01:02:04,240 --> 01:02:07,760 to find show notes, uh, you can either look at the video where you might've watched this, 784 01:02:07,760 --> 01:02:13,600 or you can go to Tom's talk.com and look this episode up. And, uh, you know, it has been 785 01:02:13,600 --> 01:02:17,220 a while. If you're listening, uh, in serial, it has been a while since we published something, 786 01:02:17,220 --> 01:02:21,320 but I've got several more things lined up and in the works. So, uh, there'll, there'll be 787 01:02:21,320 --> 01:02:25,580 more return to what we're doing here. And as always, thanks for listening. We'll see everyone 788 01:02:25,580 --> 01:02:26,940 next time. Bye-bye.