1 00:00:05,235 --> 00:00:07,285 - This is the Becker's Healthcare Podcast, created 2 00:00:07,285 --> 00:00:08,445 by the team of Becker's Healthcare, 3 00:00:08,605 --> 00:00:10,325 a multimedia company devoted to the people 4 00:00:10,345 --> 00:00:11,645 who power us healthcare. 5 00:00:11,915 --> 00:00:14,765 Four new 15 minute episodes are released daily containing 6 00:00:14,925 --> 00:00:16,685 industry news analysis and thought leadership. 7 00:00:16,685 --> 00:00:19,605 From powerful healthcare decision makers Support our show 8 00:00:19,705 --> 00:00:22,525 by leaving it a five star rating and review on Apple Podcast 9 00:00:22,745 --> 00:00:24,005 or other platforms you use. 10 00:00:24,155 --> 00:00:26,005 It's a chance to tell us what you like about the show 11 00:00:26,025 --> 00:00:27,245 and act on your feedback. 12 00:00:27,245 --> 00:00:29,205 Thanks for listening. Now here's the episode. 13 00:00:30,635 --> 00:00:33,165 - This is Laura Dedo with the Becker's Healthcare Podcast. 14 00:00:33,705 --> 00:00:34,965 I'm thrilled today to be joined by Dr. 15 00:00:34,965 --> 00:00:37,965 Neil Tala, medical director of structural heart disease 16 00:00:38,185 --> 00:00:41,325 and intervention for the VA Eastern Colorado Healthcare 17 00:00:41,325 --> 00:00:44,085 System, as well as Assistant professor at the University 18 00:00:44,085 --> 00:00:46,685 of Colorado School of Medicine, and Co-founder 19 00:00:46,745 --> 00:00:48,405 and chief Medical Officer for High Labs. 20 00:00:48,825 --> 00:00:51,205 Dr. Al, it's a pleasure to have you on the podcast today. 21 00:00:52,815 --> 00:00:54,635 - Thanks for having me. Excited to chat today. 22 00:00:55,735 --> 00:00:57,435 - Now, I'm looking forward to our discussion. 23 00:00:57,515 --> 00:01:00,155 I know you've got a lot of really neat things going on, um, 24 00:01:00,155 --> 00:01:02,715 right now, but you know, as we, 25 00:01:02,715 --> 00:01:04,475 before we dive into the deeper discussion, 26 00:01:04,575 --> 00:01:06,115 can you tell us a little bit more about yourself 27 00:01:06,115 --> 00:01:09,035 and your background and how High Labs came about? 28 00:01:10,895 --> 00:01:13,985 - Sure. Happy to. Um, so I founded High Labs. 29 00:01:14,105 --> 00:01:16,945 I co-founded High Labs when I was doing my MD MBA, um, 30 00:01:16,965 --> 00:01:19,945 at Yale, uh, at this point, you know, almost 10 years ago. 31 00:01:20,285 --> 00:01:22,785 And, and there, uh, initially we founded the company is 32 00:01:22,785 --> 00:01:24,345 almost like a, a pop health company. 33 00:01:24,805 --> 00:01:26,745 Um, looking, you know, myself 34 00:01:26,745 --> 00:01:29,265 and my co-founder, who was an executive MBA student at Yale 35 00:01:29,265 --> 00:01:31,745 at the time, um, who was a tech technologist. 36 00:01:31,775 --> 00:01:33,465 He'd worked in a number of tech companies and startups. 37 00:01:33,885 --> 00:01:37,185 Uh, and so we sort of wanted to combine his AI background 38 00:01:37,185 --> 00:01:38,505 with my healthcare background 39 00:01:38,925 --> 00:01:41,545 and really tried to, um, you know, figure out if we could, 40 00:01:41,545 --> 00:01:42,705 you know, predict who's gonna get sick 41 00:01:42,705 --> 00:01:44,225 and make them better before they get sick. 42 00:01:44,725 --> 00:01:47,785 And, uh, we realized pretty quickly that, um, a lot 43 00:01:47,785 --> 00:01:50,025 of the data going into these models was total garbage. 44 00:01:50,565 --> 00:01:55,105 And so we shifted upstream, uh, to focus on, uh, using AI 45 00:01:55,125 --> 00:01:57,225 to improve data quality in healthcare. 46 00:01:57,645 --> 00:02:00,025 And that actually got, uh, a lot of, uh, traction. 47 00:02:00,645 --> 00:02:04,105 And so, um, right now High Lab sort of serves, you know, 48 00:02:04,105 --> 00:02:07,105 for the top 10 largest health plans as well as a number 49 00:02:07,105 --> 00:02:08,945 of other regional health plans and, uh, 50 00:02:08,945 --> 00:02:11,105 and small specialized insurers really working 51 00:02:11,125 --> 00:02:13,185 to improve data interoperability 52 00:02:13,185 --> 00:02:14,385 throughout the healthcare system. 53 00:02:14,925 --> 00:02:18,225 And, uh, improve the quality of data to then enable people 54 00:02:18,225 --> 00:02:20,065 to do cool things like predict who's gonna 55 00:02:20,065 --> 00:02:21,105 get sick and, and make them better. 56 00:02:21,485 --> 00:02:23,345 Um, so that's sort of my background and, 57 00:02:23,345 --> 00:02:25,305 and how I labs have started and how it's evolved. 58 00:02:27,775 --> 00:02:28,905 - Well, that's amazing to hear. 59 00:02:28,905 --> 00:02:30,985 And certainly, you know, having that mindset 60 00:02:31,205 --> 00:02:33,825 and understanding what data can do in medicine 61 00:02:33,845 --> 00:02:35,945 and healthcare, um, has been really important 62 00:02:36,045 --> 00:02:38,545 and it certainly, um, accelerated growth in, 63 00:02:38,545 --> 00:02:40,545 in many respects over the last year, um, 64 00:02:40,965 --> 00:02:42,905 and a lot of work to be done too in the future. 65 00:02:43,535 --> 00:02:46,185 What do you really see as being some of the, um, you know, 66 00:02:46,965 --> 00:02:48,465 big changes that you're excited about? 67 00:02:48,715 --> 00:02:50,345 Where do you see as technology today 68 00:02:50,445 --> 00:02:52,705 and then headed going in the, into the future? 69 00:02:54,725 --> 00:02:56,615 - Sure. I mean, I could focus a little bit on what I, 70 00:02:56,775 --> 00:02:58,695 I specialize and do research in as well as work 71 00:02:58,695 --> 00:03:00,650 with High Labs in an then top bigger picture about, 72 00:03:00,905 --> 00:03:02,125 you know, healthcare data quality. 73 00:03:02,465 --> 00:03:05,125 You know, one thing that, um, you know, we found out, um, 74 00:03:05,595 --> 00:03:09,085 last year from my academic hat, I did research on, you know, 75 00:03:09,085 --> 00:03:11,205 the quality of provider directory data, um, 76 00:03:11,305 --> 00:03:12,725 and across, you know, health plans. 77 00:03:13,145 --> 00:03:15,885 Uh, and that in the study was published in jama, uh, 78 00:03:15,915 --> 00:03:19,245 last March, which really showed that about, you know, 80% 79 00:03:19,665 --> 00:03:23,565 of all physicians, um, have some incorrect information 80 00:03:23,865 --> 00:03:25,285 and health plan provided directories. 81 00:03:25,665 --> 00:03:27,165 And that, um, it's a pretty 82 00:03:27,365 --> 00:03:28,405 profound, you know, a large number. 83 00:03:28,405 --> 00:03:29,805 It's consistent with a lot of other studies 84 00:03:30,425 --> 00:03:32,285 and, um, really goes to show the scope 85 00:03:32,285 --> 00:03:34,325 of this provider directory, um, 86 00:03:34,495 --> 00:03:36,325 inaccuracy problem in the us. 87 00:03:36,785 --> 00:03:39,485 Um, and it's funny 'cause I talk to, you know, um, you know, 88 00:03:39,485 --> 00:03:41,765 pretty much anyone I talk to about, you know, this study 89 00:03:41,865 --> 00:03:43,045 and, and sort of this research 90 00:03:43,045 --> 00:03:45,525 and really what High Labs does, um, you know, I'm like, oh, 91 00:03:45,525 --> 00:03:46,885 you know, provider directories are incorrect. 92 00:03:47,275 --> 00:03:49,445 Like, oh yeah, here's an example of where I had, you know, 93 00:03:49,445 --> 00:03:50,645 took three hours to find an appointment 94 00:03:50,645 --> 00:03:52,085 to get a physical therapist or something. 95 00:03:52,185 --> 00:03:53,885 So people in the US just assume 96 00:03:53,885 --> 00:03:55,565 that this information at Health Plan product 97 00:03:55,565 --> 00:03:57,245 directories is not gonna be accurate. 98 00:03:57,265 --> 00:03:59,885 And I think that's, um, you know, an industrywide problem, 99 00:04:00,185 --> 00:04:01,605 um, or a healthcare system-wide 100 00:04:01,605 --> 00:04:02,765 problem that really needs solving. 101 00:04:03,385 --> 00:04:06,325 Um, so moving forward, you know, that we, 102 00:04:06,325 --> 00:04:08,525 we demonstrate the scope of that problem, you know, um, 103 00:04:08,865 --> 00:04:10,765 do this research and have another paper that's sort 104 00:04:10,765 --> 00:04:11,805 of hopefully coming out in the next month 105 00:04:11,805 --> 00:04:15,645 or two, characterizing a little further, um, last year, um, 106 00:04:15,965 --> 00:04:19,325 I think, uh, this actually is informing some new legislation 107 00:04:19,325 --> 00:04:22,445 coming through, uh, hopefully, uh, in the next few months. 108 00:04:22,625 --> 00:04:26,005 So, um, the Senate Finance Committee is looking into, um, 109 00:04:26,505 --> 00:04:29,085 you know, the, uh, using, you know, legislation 110 00:04:29,085 --> 00:04:30,445 to tackle ghost networks. 111 00:04:30,445 --> 00:04:32,845 And ghost networks are essentially, uh, you know, 112 00:04:33,085 --> 00:04:35,445 providers listed in health plan directory, uh, 113 00:04:35,585 --> 00:04:36,645 health plan directories 114 00:04:36,645 --> 00:04:38,605 that aren't really practicing if locations are listed at. 115 00:04:38,605 --> 00:04:40,725 And this is a big problem in mental health in particular 116 00:04:40,735 --> 00:04:43,165 where, you know, a a patient who's, you know, 117 00:04:43,165 --> 00:04:45,845 having enough issues as is accessing a be behavioral health 118 00:04:45,845 --> 00:04:47,725 or mental health provider looks at, uh, 119 00:04:47,965 --> 00:04:49,965 provider up in a provider directory calls them up or, 120 00:04:50,125 --> 00:04:52,525 or even goes to place in, uh, a physician's office. 121 00:04:52,585 --> 00:04:54,125 And really they're not practicing there anymore. 122 00:04:54,465 --> 00:04:56,885 And so that's actually caused a lot of, you know, friction 123 00:04:56,905 --> 00:04:58,605 and really decreased access, uh, 124 00:04:58,665 --> 00:05:00,805 or led to sort of the maintenance 125 00:05:00,805 --> 00:05:02,605 of decreased access to care for a lot of people. 126 00:05:03,065 --> 00:05:05,325 And so the Senate Finance Committee passed, um, you know, 127 00:05:05,405 --> 00:05:07,685 I think the, the Real Pro Health Providers Act is a, 128 00:05:07,685 --> 00:05:10,045 is a legislation that passed unanimously actually 129 00:05:10,045 --> 00:05:12,925 by partisan support, um, I think last month. 130 00:05:13,185 --> 00:05:15,205 And that hopefully should be up for discussion of the house, 131 00:05:15,665 --> 00:05:17,125 um, in, in January or so. 132 00:05:17,125 --> 00:05:19,085 That's something that, um, I I'm definitely watching, 133 00:05:19,345 --> 00:05:20,565 at least from my research angle, 134 00:05:20,565 --> 00:05:23,365 from provider directory standpoint, um, as to, you know, 135 00:05:23,365 --> 00:05:25,965 how the, uh, legislation sort of actually goes through 136 00:05:26,665 --> 00:05:29,125 and what impact it has on, on he the healthcare industry. 137 00:05:31,585 --> 00:05:34,285 - That's fascinating to hear about. Yeah, absolutely. 138 00:05:35,965 --> 00:05:37,625 - And, and one more thing I'll, I'll add to that. 139 00:05:37,685 --> 00:05:40,425 So just a little more on the provider directory piece, uh, 140 00:05:40,455 --> 00:05:43,665 from a high lab hat, um, you know, they, one of the things 141 00:05:43,665 --> 00:05:46,705 that High Labs does is actually uses technology 142 00:05:46,705 --> 00:05:48,345 to queen provider directories. 143 00:05:48,805 --> 00:05:52,025 And this again was, you know, born from the idea that, 144 00:05:52,025 --> 00:05:54,145 you know, what's, you improve data quality in healthcare, 145 00:05:54,525 --> 00:05:56,185 what's the first interaction somebody has 146 00:05:56,185 --> 00:05:57,845 with the healthcare system finding a doctor? 147 00:05:58,185 --> 00:05:59,885 And that is often a challenge for a lot of people, 148 00:05:59,945 --> 00:06:02,965 and particularly those, um, you know, with, um, you know, 149 00:06:02,965 --> 00:06:05,205 who are most affected, you know, most comorbidities, 150 00:06:05,385 --> 00:06:08,005 the ones with the, you know, um, you know, 151 00:06:08,425 --> 00:06:10,325 we socioeconomic disparities, ones 152 00:06:10,325 --> 00:06:11,845 with the least health literacy, those people are affected 153 00:06:11,845 --> 00:06:13,405 the most by this access to care problem, 154 00:06:13,635 --> 00:06:15,445 even getting in the door to see a provider. 155 00:06:16,025 --> 00:06:18,365 And so, um, you know, high Labs focused on improving 156 00:06:18,605 --> 00:06:19,765 provider directories for that reason. 157 00:06:20,145 --> 00:06:21,285 And that actually has gotten a lot 158 00:06:21,285 --> 00:06:23,005 of traction in really using technology 159 00:06:23,005 --> 00:06:25,045 and AI to clean provider directories. 160 00:06:25,665 --> 00:06:27,645 Um, and so that I think is, 161 00:06:27,645 --> 00:06:29,325 is a solution a lot of people are employing now. 162 00:06:29,665 --> 00:06:30,925 Um, and again, I think this 163 00:06:30,925 --> 00:06:32,365 legislation will sort of buttress that. 164 00:06:32,625 --> 00:06:34,445 Um, 'cause I do think, you know, the root 165 00:06:34,445 --> 00:06:37,045 of the problem is not just like, uh, you know, 166 00:06:37,225 --> 00:06:38,645 health plans are doing a bad job 167 00:06:38,645 --> 00:06:39,725 or providers doing a bad job. 168 00:06:39,725 --> 00:06:41,725 It's fundamentally an information disconnect problem. 169 00:06:42,345 --> 00:06:45,325 And, um, that I think, um, can only be solved by technology 170 00:06:45,825 --> 00:06:46,925 and happy to chat about that 171 00:06:46,925 --> 00:06:48,805 and other solutions, as you know, again, I think 172 00:06:48,825 --> 00:06:50,965 that's gonna be an important thing coming into play this 173 00:06:50,965 --> 00:06:54,405 year as if legislation sort of, um, goes through, um, 174 00:06:54,585 --> 00:06:56,085 at least sort of the one, one of the big things I 175 00:06:56,085 --> 00:06:57,165 think I'm excited about this year. 176 00:06:58,345 --> 00:07:01,315 - Yeah, yeah, definitely. That really seems to be a, 177 00:07:01,435 --> 00:07:02,595 a huge shift and, 178 00:07:02,615 --> 00:07:05,075 and could be make a big difference for the research 179 00:07:05,095 --> 00:07:06,755 and the data and, and how that's used. 180 00:07:07,135 --> 00:07:08,635 Now. I'm wondering too, um, 181 00:07:08,635 --> 00:07:11,115 when you look at artificial intelligence, especially, 182 00:07:11,415 --> 00:07:13,795 you know, it's important to employ the 183 00:07:13,795 --> 00:07:15,515 responsible AI as a physician. 184 00:07:15,685 --> 00:07:16,995 Could you talk a little bit about that 185 00:07:17,095 --> 00:07:18,435 and what that really means 186 00:07:18,495 --> 00:07:20,235 and how you're viewing AI 187 00:07:20,435 --> 00:07:21,675 altogether in the healthcare space? 188 00:07:23,345 --> 00:07:24,645 - That's a great question. So I, 189 00:07:24,785 --> 00:07:26,205 and a couple of different perspectives on that. 190 00:07:26,265 --> 00:07:28,245 So first, you know, as a practicing clinician, 191 00:07:28,325 --> 00:07:31,005 I think we're still a ways away from AI 192 00:07:31,065 --> 00:07:32,245 to clinical decisions before. 193 00:07:32,325 --> 00:07:34,245 I think there was actually just a paper out a few days ago 194 00:07:34,265 --> 00:07:38,925 in, in jama, uh, on, um, you know, whether using an ai, uh, 195 00:07:39,025 --> 00:07:40,965 to sort of help someone read a chest X-ray 196 00:07:40,965 --> 00:07:42,445 or guide a clinical decision making, 197 00:07:42,785 --> 00:07:43,845 and it's helpful or not. 198 00:07:44,065 --> 00:07:45,405 And I think there's a lot of gaps still. 199 00:07:45,585 --> 00:07:47,685 Um, and even explainable AI where it shows you 200 00:07:47,685 --> 00:07:50,845 where exactly the chest x-ray, you know, is concerning 201 00:07:50,845 --> 00:07:53,965 to an AI algorithm, I think there's still a lot of room 202 00:07:53,965 --> 00:07:55,205 for improvement there in terms 203 00:07:55,205 --> 00:07:56,725 of guiding clinical decision support. 204 00:07:57,165 --> 00:07:59,245 I mean, that's a goal. I mean, that, that's like a, a goal 205 00:07:59,245 --> 00:08:00,405 that we really should get to. 206 00:08:00,405 --> 00:08:02,285 And I'm hopeful that the kinks can be worked out, 207 00:08:02,545 --> 00:08:03,645 but we're not quite there yet. 208 00:08:03,705 --> 00:08:05,005 Uh, despite all the excitement around it. 209 00:08:05,465 --> 00:08:07,805 Uh, where I do think AI is actually gonna be important in 210 00:08:07,965 --> 00:08:10,885 healthcare, um, is actually gonna be upstream of that, uh, 211 00:08:10,885 --> 00:08:14,005 addressing the boring problems in some sense, um, of data 212 00:08:14,145 --> 00:08:15,845 and, and how people, you know, um, 213 00:08:16,105 --> 00:08:17,165 you know, deal with operations. 214 00:08:17,245 --> 00:08:19,405 I think AI is amazing at, you know, uh, 215 00:08:19,405 --> 00:08:20,845 improving operational efficiency. 216 00:08:21,105 --> 00:08:22,645 And this is seen across industries, right? 217 00:08:22,645 --> 00:08:24,245 So whether it can, you know, automate 218 00:08:24,815 --> 00:08:26,165 email marketing campaigns 219 00:08:26,185 --> 00:08:29,325 or automate, you know, generation of, you know, logos 220 00:08:29,345 --> 00:08:32,045 or it could, um, automate some back office stuff 221 00:08:32,145 --> 00:08:35,085 and, um, you know, uh, take notes through meetings, a lot 222 00:08:35,085 --> 00:08:38,165 of the operational things that AI is certainly, um, geared, 223 00:08:38,165 --> 00:08:40,165 you know, to do perfectly that are sort of road tasks. 224 00:08:40,545 --> 00:08:43,365 And I think that can really improve, um, you know, 225 00:08:43,365 --> 00:08:45,365 manual processing of a lot of things that happens, 226 00:08:45,525 --> 00:08:47,685 particularly in healthcare where everything is. 227 00:08:47,685 --> 00:08:50,645 So, you know, it just takes a long time for, for innovation 228 00:08:50,745 --> 00:08:52,965 to really, um, take hold in a lot of these business, 229 00:08:52,985 --> 00:08:54,565 you know, established business processes. 230 00:08:55,065 --> 00:08:56,485 So I think that's where their, you know, 231 00:08:56,625 --> 00:08:58,085 AI has a more direct impact. 232 00:08:58,425 --> 00:09:00,085 And I think that's really where we're, 233 00:09:00,085 --> 00:09:03,965 we'll see the first sort of, um, changes as a result of ai, 234 00:09:04,145 --> 00:09:05,485 and again, it's gonna be solving, solving 235 00:09:05,505 --> 00:09:06,565 the boring problems, right? 236 00:09:06,565 --> 00:09:07,725 Like, so how is, you know, 237 00:09:07,725 --> 00:09:09,685 how are we gonna do billing in, in healthcare? 238 00:09:09,685 --> 00:09:11,125 Like, how is that gonna be improved by ai? 239 00:09:11,125 --> 00:09:13,685 How is prior, prior authorization gonna, you know, uh, 240 00:09:13,745 --> 00:09:16,405 be more efficient or how are we gonna, you know, improve 241 00:09:16,685 --> 00:09:18,725 provided directories or improve, you know, transmission 242 00:09:18,725 --> 00:09:20,445 of clinical data from one hospital to another 243 00:09:20,785 --> 00:09:23,485 or improve, you know, um, you know, 244 00:09:24,005 --> 00:09:25,565 interpreting clinical data from 245 00:09:25,565 --> 00:09:26,805 another hospital or something like that. 246 00:09:27,185 --> 00:09:29,925 So I think all these sort of data problems, a lot 247 00:09:29,925 --> 00:09:32,405 of operational issues where a lot of manual works involved, 248 00:09:32,845 --> 00:09:34,885 I think that's certainly going to, uh, be right 249 00:09:34,905 --> 00:09:37,605 for transformation with AI as at least in the near term 250 00:09:38,185 --> 00:09:39,485 and the clinical decision report, 251 00:09:39,485 --> 00:09:41,685 that stuff we all dream about, uh, where, you know, 252 00:09:41,685 --> 00:09:43,885 it's like a, you know, AI driven, you know, 253 00:09:43,885 --> 00:09:45,005 physician or clinical judgment. 254 00:09:45,125 --> 00:09:47,765 I think that's still a ways away just based on at least the, 255 00:09:47,765 --> 00:09:49,645 the research I've seen and the step I've seen on clinical 256 00:09:49,965 --> 00:09:52,085 practice and is practicing as physician, right? 257 00:09:52,205 --> 00:09:54,685 I mean, there's only a certain, you know, I think we're, 258 00:09:55,335 --> 00:09:58,785 it's gonna be challenging, um, for, um, some of these, um, 259 00:09:59,085 --> 00:10:02,665 AI models to really, um, recreate, uh, the level of, um, 260 00:10:02,805 --> 00:10:04,585 you know, clinical interaction 261 00:10:04,895 --> 00:10:06,425 that a physician has with a patient. 262 00:10:06,725 --> 00:10:08,105 And there's multiple dimensions to it. 263 00:10:08,105 --> 00:10:09,345 It's not just what a patient says 264 00:10:09,445 --> 00:10:10,505 or, you know, it's a physician 265 00:10:10,505 --> 00:10:12,465 or types into a box in an AI engine. 266 00:10:12,775 --> 00:10:15,105 It's, it's like, you know, what's the patient's, you know, 267 00:10:15,105 --> 00:10:16,465 what's their tone like when talking 268 00:10:16,465 --> 00:10:17,705 to you about something sensitive 269 00:10:17,705 --> 00:10:19,825 or that actually informs a lot of the clinical judgment, 270 00:10:19,955 --> 00:10:21,465 which I think is gonna be hard to replace 271 00:10:21,465 --> 00:10:22,985 with ai, at least in the near term. 272 00:10:24,385 --> 00:10:25,605 - That's helpful to know, you know, 273 00:10:25,605 --> 00:10:28,685 and definitely, um, important to keep in mind as we think 274 00:10:28,685 --> 00:10:31,165 through how AI can really affect 275 00:10:31,165 --> 00:10:32,645 healthcare and where it's headed. 276 00:10:32,745 --> 00:10:35,805 Um, I appreciate your analysis there. Um, Dr. 277 00:10:36,085 --> 00:10:38,205 Al, is there anything else you wanted to connect about, 278 00:10:38,205 --> 00:10:39,485 especially looking over to the next 279 00:10:39,505 --> 00:10:40,565 two to three years or so? 280 00:10:40,565 --> 00:10:42,565 What is exciting, um, and 281 00:10:42,565 --> 00:10:45,085 and what do you really see as, um, you know, medicine? 282 00:10:45,145 --> 00:10:46,925 How is it evolving in, in, uh, 283 00:10:47,205 --> 00:10:49,565 especially thinking about the data and, 284 00:10:49,565 --> 00:10:51,645 and information that clinicians as well 285 00:10:51,645 --> 00:10:53,845 as healthcare organizations will have in the next few years? 286 00:10:55,655 --> 00:10:57,345 - Yeah, I mean, one other thing is just, um, 287 00:10:57,345 --> 00:10:58,945 interoperability of clinical data. 288 00:10:59,125 --> 00:11:01,265 So right now, you know, there are regulations 289 00:11:01,265 --> 00:11:02,785 that are coming into place, especially the next year, 290 00:11:03,175 --> 00:11:07,105 that really, um, are encouraging the use of, um, you know, 291 00:11:07,545 --> 00:11:10,065 standards like fire, FHIR, fire, um, 292 00:11:10,205 --> 00:11:12,465 to transmit clinical data from one hospital to another. 293 00:11:12,465 --> 00:11:14,465 So let's say I have a patient who comes in 294 00:11:14,465 --> 00:11:15,945 with a heart attack in my hospital 295 00:11:16,525 --> 00:11:18,705 and, um, you know, they just had a procedure done 296 00:11:18,705 --> 00:11:20,025 another hospital a few weeks ago. 297 00:11:20,445 --> 00:11:22,825 The ability to transmit that information in some sense, 298 00:11:22,825 --> 00:11:24,945 it's required actually, uh, now by law. 299 00:11:25,105 --> 00:11:26,985 And the ability to interpret that information once it gets 300 00:11:26,985 --> 00:11:28,905 from one place to another is gonna be key. 301 00:11:29,165 --> 00:11:30,825 And so as more people start complying 302 00:11:30,825 --> 00:11:33,505 with these regulations, I'm excited for the potential to, 303 00:11:33,885 --> 00:11:37,265 you know, for this data interoperability to, uh, you know, 304 00:11:37,285 --> 00:11:39,105 reduce doing, redoing unnecessary tests 305 00:11:39,165 --> 00:11:41,905 or really improve, um, you know, clinical decision making 306 00:11:41,905 --> 00:11:44,185 of these more information about a holistic, you know, 307 00:11:44,265 --> 00:11:46,745 a patient's holistic medical record across different, um, 308 00:11:47,065 --> 00:11:48,665 entities, whether it's across the health plans, 309 00:11:48,725 --> 00:11:51,025 across physician groups, across hospitals. 310 00:11:51,205 --> 00:11:53,025 So I'm really excited about that, uh, especially 311 00:11:53,130 --> 00:11:54,650 as regulations kind of pushing this along. 312 00:11:55,025 --> 00:11:56,165 Um, I do worry, again, 313 00:11:56,165 --> 00:11:58,325 the data quality piece is a problem that needs to be solved, right? 314 00:11:58,325 --> 00:11:59,805 That's a boring problem that needs to be solved 315 00:11:59,805 --> 00:12:00,885 before the exciting part 316 00:12:00,885 --> 00:12:02,925 where we can actually know everything about a patient 317 00:12:02,925 --> 00:12:04,165 who's coming in with an acute problem. 318 00:12:04,665 --> 00:12:07,005 Um, and so the data quality piece is gonna be key. 319 00:12:07,005 --> 00:12:08,565 And again, that's where AI, again, can help. 320 00:12:09,165 --> 00:12:11,205 I think, uh, and High Labs has worked on this a fair bit 321 00:12:11,445 --> 00:12:12,605 with a couple large insurers. 322 00:12:12,825 --> 00:12:14,725 We see data from thousands of different providers 323 00:12:14,725 --> 00:12:16,045 and being able to standardize that 324 00:12:16,065 --> 00:12:19,205 and sort of, um, read clinical notes as a human can 325 00:12:19,205 --> 00:12:20,445 and then sort of summarize them. 326 00:12:20,825 --> 00:12:23,365 So there's AI ways to go around, around this and, 327 00:12:23,365 --> 00:12:25,005 and address this issue, uh, 328 00:12:25,005 --> 00:12:26,525 and actually, you know, realize the potential 329 00:12:26,525 --> 00:12:28,365 of interoperability, which people have been talking out now 330 00:12:28,365 --> 00:12:30,805 for, you know, uh, years if not a decade. 331 00:12:31,545 --> 00:12:33,445 So that's something I'm excited about with these regulations 332 00:12:33,745 --> 00:12:35,725 and the hope of AI to really, you know, 333 00:12:35,725 --> 00:12:37,005 make this data useful. 334 00:12:37,245 --> 00:12:40,245 I think the regulations that are sort of, uh, mandating some 335 00:12:40,245 --> 00:12:42,285 of these pipes in place between ENT entities, 336 00:12:42,285 --> 00:12:44,405 but then being able to make that data useful once it reaches 337 00:12:44,405 --> 00:12:46,325 and then location, um, I think that's 338 00:12:46,325 --> 00:12:47,445 where AI can certainly help. 339 00:12:47,585 --> 00:12:50,165 And I'm excited for that to also come to fruition, um, 340 00:12:50,165 --> 00:12:53,125 hopefully in the next year, uh, in practice at scale. 341 00:12:54,535 --> 00:12:56,295 - I love it. Dr. Butala, thank you so much 342 00:12:56,295 --> 00:12:57,775 for joining us on the podcast today. 343 00:12:57,965 --> 00:12:59,975 This has been a really fun and interesting conversation, 344 00:12:59,975 --> 00:13:01,775 and I look forward to connecting with you again soon. 345 00:13:02,405 --> 00:13:03,615 - Problem. Thank you for your time. 346 00:13:05,885 --> 00:13:08,615 - It's so important for leaders at the top of organizations 347 00:13:08,635 --> 00:13:11,135 to keep learning, stay sharp, grow their networks, 348 00:13:11,525 --> 00:13:14,015 help our audience better do this in a more simplified, 349 00:13:14,015 --> 00:13:15,695 personalized, and meaningful way. 350 00:13:16,255 --> 00:13:18,695 Becker's Healthcare has launched my BHC, 351 00:13:19,205 --> 00:13:21,175 it's your trusted Becker's healthcare experience 352 00:13:21,175 --> 00:13:23,215 and more with content, connections, events 353 00:13:23,235 --> 00:13:24,295 and learning opportunities. 354 00:13:25,045 --> 00:13:26,295 Join the community free 355 00:13:26,295 --> 00:13:31,055 of charge@www.my dot becker's hospital review.com, 356 00:13:31,195 --> 00:13:32,015 and we'll see you there.