1 00:00:00,719 --> 00:00:04,000 Exciting things are happening at Becker's Healthcare. Stay 2 00:00:04,000 --> 00:00:06,240 ahead of industry trends with the new Becker's 3 00:00:06,240 --> 00:00:08,419 CFO plus Revenue Cycle podcast, 4 00:00:08,880 --> 00:00:11,119 your go to source for insights from top 5 00:00:11,119 --> 00:00:14,154 healthcare finance leaders. Tune in wherever you get 6 00:00:14,154 --> 00:00:15,054 your podcasts. 7 00:00:15,914 --> 00:00:18,475 And don't miss the tenth annual health IT 8 00:00:18,475 --> 00:00:22,154 plus digital health plus RCM conference, happening September 9 00:00:22,154 --> 00:00:24,474 30 to 10/03/2025 10 00:00:24,474 --> 00:00:25,214 in Chicago. 11 00:00:25,755 --> 00:00:29,140 Join thousands of executives, engage with industry leaders, 12 00:00:29,279 --> 00:00:31,619 and explore the future of health care innovation. 13 00:00:32,320 --> 00:00:36,719 Learn more about our upcoming events at beckershospitalreview.com. 14 00:00:36,719 --> 00:00:37,539 See you there. 15 00:00:38,239 --> 00:00:40,424 This is Laura Dierda with the Becker's Health 16 00:00:40,505 --> 00:00:42,424 Care podcast. I'm thrilled today to be joined 17 00:00:42,424 --> 00:00:45,545 by doctor Rebecca Mashouris, chief medical information officer 18 00:00:45,545 --> 00:00:47,945 and vice president at Mass General Brigham. Doctor 19 00:00:47,945 --> 00:00:49,225 Mashouris, it's a pleasure to have you on 20 00:00:49,225 --> 00:00:50,284 the podcast today. 21 00:00:50,905 --> 00:00:53,079 Thanks very much for having me. Now I'm 22 00:00:53,079 --> 00:00:55,159 looking forward to our conversation and really digging 23 00:00:55,159 --> 00:00:56,600 deep into some of the cool things that 24 00:00:56,600 --> 00:00:58,280 you're doing at Mass General Brigham as well 25 00:00:58,280 --> 00:01:00,199 as how you're thinking about the future. But 26 00:01:00,199 --> 00:01:01,799 before we dive into my questions, can you 27 00:01:01,799 --> 00:01:03,159 tell us a little bit more about Mass 28 00:01:03,159 --> 00:01:05,019 General Brigham and what makes it unique? 29 00:01:06,155 --> 00:01:06,655 Sure. 30 00:01:07,114 --> 00:01:09,375 Mass General Brigham is a, 31 00:01:09,915 --> 00:01:10,655 health system, 32 00:01:11,355 --> 00:01:14,395 primarily located in Massachusetts, but also up in 33 00:01:14,395 --> 00:01:16,655 New Hampshire and a little bit into Maine. 34 00:01:17,114 --> 00:01:19,789 And we are made up of two anchor 35 00:01:19,789 --> 00:01:22,750 academic medical centers, the Mass General Hospital and 36 00:01:22,750 --> 00:01:23,969 Brigham Women's Hospital, 37 00:01:24,349 --> 00:01:27,170 and then a number of community hospitals and 38 00:01:27,310 --> 00:01:30,689 a very large number of ambulatory care sites. 39 00:01:31,355 --> 00:01:32,475 We are, 40 00:01:32,954 --> 00:01:35,135 the largest employer in Massachusetts, 41 00:01:35,835 --> 00:01:37,055 health care or otherwise, 42 00:01:38,075 --> 00:01:38,555 and, 43 00:01:38,954 --> 00:01:41,135 you know, really aim to serve the communities 44 00:01:41,275 --> 00:01:42,575 in which we exist. 45 00:01:43,115 --> 00:01:44,655 I think in terms of uniqueness, 46 00:01:45,689 --> 00:01:46,750 we bring together 47 00:01:47,129 --> 00:01:48,430 the patient care, 48 00:01:48,890 --> 00:01:49,709 the research, 49 00:01:50,010 --> 00:01:52,969 and the training and education aspects of health 50 00:01:52,969 --> 00:01:55,609 care. And that's really important and and really 51 00:01:55,609 --> 00:01:57,450 critical to our mission of, 52 00:01:57,849 --> 00:02:00,009 serving the communities in which, 53 00:02:00,329 --> 00:02:00,829 we 54 00:02:01,174 --> 00:02:01,834 we sit. 55 00:02:02,775 --> 00:02:05,055 Absolutely. That's fantastic to hear. You know, what 56 00:02:05,055 --> 00:02:05,915 a great opportunity 57 00:02:06,215 --> 00:02:08,534 to make a difference for a large patient 58 00:02:08,534 --> 00:02:10,854 population that you have there that are serving 59 00:02:10,854 --> 00:02:12,474 in the Boston area. Now, 60 00:02:12,854 --> 00:02:14,055 could you tell us a little bit about 61 00:02:14,055 --> 00:02:15,895 an accomplishment from the last year that you're 62 00:02:15,895 --> 00:02:16,794 most proud of? 63 00:02:17,790 --> 00:02:20,909 Sure. I think, I'm probably most proud of 64 00:02:20,909 --> 00:02:23,090 the way in which we have brought 65 00:02:23,710 --> 00:02:24,610 a new technology 66 00:02:25,310 --> 00:02:27,569 to bear against clinician burnout. 67 00:02:28,030 --> 00:02:30,290 That new technology is ambient documentation, 68 00:02:30,590 --> 00:02:32,450 the use of generative AI, 69 00:02:33,114 --> 00:02:35,854 to help draft a clinical note, 70 00:02:36,155 --> 00:02:37,995 from a recording of a of a visit 71 00:02:37,995 --> 00:02:39,694 between a patient and their, 72 00:02:40,235 --> 00:02:41,215 health care provider. 73 00:02:42,474 --> 00:02:45,455 And not just having brought that, to our 74 00:02:45,594 --> 00:02:46,334 our providers, 75 00:02:46,634 --> 00:02:48,300 but the way in which we did it 76 00:02:48,460 --> 00:02:49,919 in a very robust, 77 00:02:50,460 --> 00:02:52,719 methodical way in terms of 78 00:02:53,020 --> 00:02:53,520 understanding 79 00:02:53,819 --> 00:02:54,479 the technology, 80 00:02:54,780 --> 00:02:56,159 evaluating the technology, 81 00:02:56,939 --> 00:02:59,419 piloting the technology, and then bringing it to 82 00:02:59,419 --> 00:02:59,919 scale. 83 00:03:00,620 --> 00:03:02,145 And part of that intentionality 84 00:03:02,525 --> 00:03:04,444 was really around the fact that this is 85 00:03:04,444 --> 00:03:05,425 generative AI, 86 00:03:05,885 --> 00:03:08,044 and really has only been on the on 87 00:03:08,044 --> 00:03:10,284 kind of the in the market, for the 88 00:03:10,284 --> 00:03:12,605 last two plus years and and, you know, 89 00:03:12,605 --> 00:03:14,705 really in the market even less than that. 90 00:03:15,189 --> 00:03:17,829 And it's a technology that clearly has a 91 00:03:17,829 --> 00:03:20,549 role moving forward in health care, not just 92 00:03:20,549 --> 00:03:23,109 for ambient documentation, but for lots of different 93 00:03:23,109 --> 00:03:23,609 purposes. 94 00:03:24,389 --> 00:03:26,629 But it is a technology that we have 95 00:03:26,629 --> 00:03:28,629 not seen the likes of before in health 96 00:03:28,629 --> 00:03:29,530 care or otherwise 97 00:03:30,115 --> 00:03:31,495 in that it is 98 00:03:31,794 --> 00:03:34,134 built off of an immense amount of data, 99 00:03:34,835 --> 00:03:36,215 in that the algorithms 100 00:03:36,675 --> 00:03:37,175 are, 101 00:03:37,715 --> 00:03:39,875 you know, built by people but then are 102 00:03:39,875 --> 00:03:42,995 built to evolve over time as the data 103 00:03:42,995 --> 00:03:44,375 evolves, as the understanding 104 00:03:45,235 --> 00:03:47,469 evolves. Right? They're they're meant to learn. 105 00:03:48,409 --> 00:03:50,250 And that is very different than a lot 106 00:03:50,250 --> 00:03:52,569 of the static technology we've had in health 107 00:03:52,569 --> 00:03:53,389 care to date. 108 00:03:53,769 --> 00:03:56,669 And so we undertook an approach that was, 109 00:03:57,289 --> 00:03:59,930 modeled after actually clinical trials. So it's a, 110 00:03:59,930 --> 00:04:02,514 you know, zero to four clinical trials. We've 111 00:04:02,514 --> 00:04:05,074 taken a clinical trial informed approach to the 112 00:04:05,074 --> 00:04:07,574 evaluation and implementation of generative AI, 113 00:04:08,194 --> 00:04:10,455 really kind of first applying that at scale, 114 00:04:10,995 --> 00:04:12,215 to ambient documentation 115 00:04:12,675 --> 00:04:15,495 where we sought first to understand the technology, 116 00:04:16,230 --> 00:04:18,550 understand the safety of the technology, use a 117 00:04:18,550 --> 00:04:21,290 very small cohort of about 20 physicians, 118 00:04:22,230 --> 00:04:23,689 to evaluate the safety 119 00:04:24,470 --> 00:04:26,569 and workflows with ambient documentation, 120 00:04:27,509 --> 00:04:29,430 having seen that it was safe, right, no 121 00:04:29,430 --> 00:04:30,410 rampant hallucination, 122 00:04:31,355 --> 00:04:33,855 wasn't making things up, from the visit, 123 00:04:34,394 --> 00:04:36,714 and and seeing sort of the enthusiasm of 124 00:04:36,714 --> 00:04:38,555 those first twenty providers in terms of the 125 00:04:38,555 --> 00:04:40,394 workflow and the impact it was having on 126 00:04:40,394 --> 00:04:43,675 them, very quickly moved to a, what became 127 00:04:43,675 --> 00:04:46,550 a fairly large pilot or initial implementation, if 128 00:04:46,550 --> 00:04:48,629 you will, because it ended up being about 129 00:04:48,629 --> 00:04:52,470 800 physicians and advanced practice providers across our 130 00:04:52,470 --> 00:04:55,769 system, all care settings, all sites, all specialties, 131 00:04:56,629 --> 00:04:58,949 to really try and understand the impact that 132 00:04:58,949 --> 00:05:01,264 the technology was having on our providers. 133 00:05:01,964 --> 00:05:04,384 You could very quickly see that this technology 134 00:05:04,604 --> 00:05:08,204 was transforming the way in which we interact 135 00:05:08,204 --> 00:05:09,024 with our patients 136 00:05:09,564 --> 00:05:11,264 that ends up being a note. 137 00:05:11,805 --> 00:05:13,725 But it it was about more than just 138 00:05:13,725 --> 00:05:15,745 writing the note. It was actually a technology 139 00:05:16,310 --> 00:05:18,410 that was putting us back in the room 140 00:05:18,629 --> 00:05:20,949 with our patients and that's really unlike any 141 00:05:20,949 --> 00:05:22,870 other technology in healthcare. Right? A lot of 142 00:05:22,870 --> 00:05:25,830 times technology will put up barriers between people 143 00:05:25,830 --> 00:05:27,770 rather than take those barriers down. 144 00:05:28,310 --> 00:05:29,370 And so we, 145 00:05:29,910 --> 00:05:32,009 evaluated about 20 different measures 146 00:05:32,604 --> 00:05:36,044 of impact of ambient documentation and from that, 147 00:05:36,524 --> 00:05:38,604 particularly because of the impact it was having 148 00:05:38,604 --> 00:05:41,805 on clinician burnout with a 20% absolute reduction 149 00:05:41,805 --> 00:05:43,024 in clinician burnout, 150 00:05:43,564 --> 00:05:45,104 have now moved to scaling 151 00:05:45,404 --> 00:05:47,344 the technology across our system. 152 00:05:48,290 --> 00:05:50,129 There is nothing else in health care that 153 00:05:50,129 --> 00:05:51,350 has been able to 154 00:05:51,730 --> 00:05:53,990 reduce burnout to that extent. 155 00:05:54,850 --> 00:05:55,750 I don't necessarily 156 00:05:56,129 --> 00:05:56,629 expect, 157 00:05:57,170 --> 00:06:01,545 everyone to to experience that degree of impact. 158 00:06:01,545 --> 00:06:04,264 But even if we were to impact burnout 159 00:06:04,264 --> 00:06:06,585 by 5%, that would still be a huge 160 00:06:06,585 --> 00:06:07,805 amount at scale. 161 00:06:08,745 --> 00:06:10,285 And so, you know, the technology 162 00:06:11,785 --> 00:06:14,910 really you know, it it takes away the 163 00:06:14,910 --> 00:06:15,410 documentation 164 00:06:16,029 --> 00:06:18,370 aspect of things while you're with the patient. 165 00:06:18,910 --> 00:06:22,110 And so providers report 80% of providers report 166 00:06:22,110 --> 00:06:23,410 that they're spending 167 00:06:23,790 --> 00:06:26,564 more time looking at their patient. They're spending 168 00:06:26,564 --> 00:06:28,644 more time paying attention to their patients in 169 00:06:28,644 --> 00:06:30,805 the course of a visit. And 60% of 170 00:06:30,805 --> 00:06:33,365 our providers reported that they were more likely 171 00:06:33,365 --> 00:06:35,545 to extend the length of their clinical career. 172 00:06:35,605 --> 00:06:37,605 And then, as I mentioned, 20% 173 00:06:37,605 --> 00:06:40,824 reduction in providers reporting burnout symptoms. 174 00:06:41,699 --> 00:06:44,339 And so, you know, again, really kind of 175 00:06:44,339 --> 00:06:44,839 impressive 176 00:06:45,220 --> 00:06:47,300 impact of the technology, but I think that 177 00:06:47,300 --> 00:06:49,639 the way that our team went about evaluating 178 00:06:49,779 --> 00:06:52,600 and implementing and bringing the technology to scale 179 00:06:52,980 --> 00:06:53,800 is actually, 180 00:06:54,259 --> 00:06:56,564 the the most important thing to me. And 181 00:06:56,564 --> 00:06:58,665 the fact that we've been able to impact 182 00:06:58,725 --> 00:06:59,545 so many, 183 00:07:00,085 --> 00:07:02,324 providers' lives and, quite honestly, patients' lives as 184 00:07:02,324 --> 00:07:04,564 well. Patients notice that their doctors are no 185 00:07:04,564 --> 00:07:06,965 longer flapping away at the keyboard during their 186 00:07:06,965 --> 00:07:07,465 visit. 187 00:07:08,004 --> 00:07:10,105 And when I hear stories from my providers, 188 00:07:10,165 --> 00:07:12,370 like, I can go to my child's gymnastics 189 00:07:12,509 --> 00:07:14,750 meet on Saturday morning instead of writing my 190 00:07:14,750 --> 00:07:17,230 notes, or I can sit down to dinner 191 00:07:17,230 --> 00:07:19,389 with my family now instead of finishing my 192 00:07:19,389 --> 00:07:22,290 notes, that is huge, huge impact. 193 00:07:23,645 --> 00:07:25,245 Well, that's amazing to hear. You know? And, 194 00:07:25,245 --> 00:07:27,964 certainly, it's so inspiring to have that personal 195 00:07:27,964 --> 00:07:29,964 touch to it, not only from how it 196 00:07:29,964 --> 00:07:32,764 impacts patients and can really improve the patient 197 00:07:32,764 --> 00:07:35,439 care and experience, but then for the clinicians 198 00:07:35,500 --> 00:07:37,660 as well and the physicians who very much 199 00:07:37,660 --> 00:07:40,300 need to have that ability to, be there 200 00:07:40,300 --> 00:07:41,199 for their families 201 00:07:41,500 --> 00:07:43,339 and to fill their buckets in that space 202 00:07:43,339 --> 00:07:44,000 as well. 203 00:07:44,379 --> 00:07:46,540 Just knowing you have that kind of support 204 00:07:46,540 --> 00:07:47,919 from technology to, 205 00:07:48,625 --> 00:07:50,704 you know, do that, I can imagine, is 206 00:07:50,704 --> 00:07:51,444 really gratifying. 207 00:07:51,985 --> 00:07:53,745 I'm curious. I I know a lot of 208 00:07:53,745 --> 00:07:56,064 health systems are going down this path of 209 00:07:56,064 --> 00:07:58,164 trying to use this type of technology, 210 00:07:59,024 --> 00:08:00,704 and and figure out, you know, who the 211 00:08:00,704 --> 00:08:03,360 best partners are for them and what the 212 00:08:03,360 --> 00:08:06,180 best investments are for their organization. 213 00:08:07,360 --> 00:08:09,439 What types of considerations did you make when 214 00:08:09,439 --> 00:08:12,720 you were evaluating different opportunities, different partners, and 215 00:08:12,720 --> 00:08:15,975 technologies, and that before making a decision on 216 00:08:15,975 --> 00:08:17,975 how to move forward with, what you're currently 217 00:08:17,975 --> 00:08:18,795 using today? 218 00:08:19,735 --> 00:08:22,375 So, you know, first things first was, could 219 00:08:22,375 --> 00:08:23,814 it write a good note? Right? Could it 220 00:08:23,814 --> 00:08:25,495 draft a good note? And so we looked 221 00:08:25,495 --> 00:08:27,995 at the actual functionality of the technology 222 00:08:28,455 --> 00:08:30,795 at the current at that current time. Right? 223 00:08:31,319 --> 00:08:33,080 And that that had a lot to do 224 00:08:33,080 --> 00:08:33,899 with the, 225 00:08:34,440 --> 00:08:37,080 robustness and sophistication of the algorithms that were 226 00:08:37,159 --> 00:08:39,159 you know, the the large language models that 227 00:08:39,159 --> 00:08:41,339 were underlying that. And, 228 00:08:41,960 --> 00:08:43,480 so that was that was step one. Step 229 00:08:43,480 --> 00:08:45,259 two was really paying attention to the workflow, 230 00:08:45,865 --> 00:08:47,085 and looking for 231 00:08:47,465 --> 00:08:47,965 opportunities 232 00:08:48,504 --> 00:08:51,785 with, applications that would integrate into our existing 233 00:08:51,785 --> 00:08:54,184 workflows, and that meant the ability to integrate 234 00:08:54,184 --> 00:08:55,725 with our electronic health plan. 235 00:08:56,504 --> 00:08:58,665 And then, you know, I think last but 236 00:08:58,665 --> 00:09:00,980 certainly not least was really trying to get 237 00:09:00,980 --> 00:09:03,700 an understanding of where that vendor was going 238 00:09:03,700 --> 00:09:06,019 in the future because I don't think the 239 00:09:06,019 --> 00:09:08,360 the end of this is writing or drafting 240 00:09:08,419 --> 00:09:10,340 a a clinical note. Right? There are so 241 00:09:10,340 --> 00:09:12,759 many things you can do on top of 242 00:09:12,820 --> 00:09:15,139 that large language model and that recording of 243 00:09:15,139 --> 00:09:15,799 a visit. 244 00:09:16,154 --> 00:09:16,815 Everything from, 245 00:09:17,834 --> 00:09:19,674 helping me place an order. Right? If I 246 00:09:19,674 --> 00:09:21,914 mentioned during the visit that we're going to 247 00:09:21,914 --> 00:09:24,554 start you on metformin a thousand milligrams once 248 00:09:24,554 --> 00:09:26,554 a day, can it queue up that order 249 00:09:26,554 --> 00:09:27,294 for me, 250 00:09:28,315 --> 00:09:30,735 to helping me with my billing and coding 251 00:09:31,559 --> 00:09:33,720 to some point in the future, maybe even 252 00:09:33,720 --> 00:09:35,100 helping me with diagnostic, 253 00:09:36,039 --> 00:09:38,620 and and clinical reasoning and decision making. 254 00:09:39,160 --> 00:09:41,080 You know, if if I'm seeing a patient 255 00:09:41,080 --> 00:09:43,080 for something and I go through kind of 256 00:09:43,080 --> 00:09:44,894 my list of things that I wanna ask 257 00:09:44,894 --> 00:09:46,495 them about to try and rule in or 258 00:09:46,495 --> 00:09:49,714 rule out possible etiologies or or treatment options, 259 00:09:50,654 --> 00:09:52,815 could that large language model prompt me if 260 00:09:52,815 --> 00:09:55,475 I forgot to ask something, right, or, 261 00:09:55,934 --> 00:09:58,014 alert me to something that I wasn't aware 262 00:09:58,014 --> 00:10:00,339 of in the chart that might change my 263 00:10:00,339 --> 00:10:02,039 thinking. We're not there yet, 264 00:10:02,419 --> 00:10:04,179 but we were looking for vendors who were 265 00:10:04,339 --> 00:10:06,419 who really kind of had that forethought and 266 00:10:06,419 --> 00:10:06,919 foresight, 267 00:10:07,539 --> 00:10:09,379 to to see what was coming and and 268 00:10:09,379 --> 00:10:10,919 be able to build towards that. 269 00:10:11,725 --> 00:10:13,644 That's amazing to hear. Thank you for going 270 00:10:13,644 --> 00:10:15,725 a little bit deeper in there. Now I'm 271 00:10:15,725 --> 00:10:17,965 curious. Looking ahead, what do you see as 272 00:10:17,965 --> 00:10:19,965 some of the biggest opportunities for growth over 273 00:10:19,965 --> 00:10:21,585 the next twelve months or so? 274 00:10:22,365 --> 00:10:24,764 Oh, gosh. There's huge opportunities here. You know, 275 00:10:24,764 --> 00:10:26,620 I think we have some of the biggest 276 00:10:26,620 --> 00:10:28,959 challenges right now in health care from, 277 00:10:29,419 --> 00:10:32,299 clinician burnout to the financial stress in health 278 00:10:32,299 --> 00:10:32,799 care, 279 00:10:33,259 --> 00:10:35,259 to just quality and safety of the care 280 00:10:35,259 --> 00:10:37,259 that we're delivering and included in that the 281 00:10:37,259 --> 00:10:38,879 equity of the care that we're delivering. 282 00:10:39,695 --> 00:10:41,535 And so, you know, I think we continue 283 00:10:41,535 --> 00:10:44,014 to see big opportunities in those spaces for 284 00:10:44,014 --> 00:10:47,315 the technologies we have, but also really excited 285 00:10:47,375 --> 00:10:49,475 about what we're seeing coming from, 286 00:10:50,014 --> 00:10:51,715 the generative AI space, 287 00:10:52,335 --> 00:10:53,235 and opportunities 288 00:10:54,000 --> 00:10:56,899 that that provides that is significant again, significantly 289 00:10:57,200 --> 00:10:57,700 different, 290 00:10:58,080 --> 00:11:00,340 from what we've had available in the past. 291 00:11:00,639 --> 00:11:02,720 Generative AI is clearly not going to be 292 00:11:02,720 --> 00:11:05,120 the right tool for every problem that we 293 00:11:05,120 --> 00:11:07,665 encounter in health care, But I think some 294 00:11:07,665 --> 00:11:09,125 of our sickier problems, 295 00:11:09,504 --> 00:11:11,445 right, it could have real impact, 296 00:11:12,225 --> 00:11:13,125 moving forward. 297 00:11:14,304 --> 00:11:16,304 That's great to hear. And, you know, really 298 00:11:16,304 --> 00:11:18,404 cool to have that type of vision in 299 00:11:18,465 --> 00:11:20,909 what's possible over the next few months and 300 00:11:20,909 --> 00:11:22,990 and see, you know, where things are headed. 301 00:11:22,990 --> 00:11:24,750 Now I know we've talked a lot about 302 00:11:24,750 --> 00:11:27,470 the cool things and opportunities, especially with some 303 00:11:27,470 --> 00:11:30,269 of the AI and other technologies, but I'm 304 00:11:30,269 --> 00:11:31,710 curious as well. What are some of the 305 00:11:31,710 --> 00:11:33,330 big challenges that you're anticipating? 306 00:11:35,485 --> 00:11:36,924 So, you know, I think when it comes 307 00:11:36,924 --> 00:11:39,404 to the technology, one of the biggest challenges 308 00:11:39,404 --> 00:11:41,264 we're facing right now is, 309 00:11:41,804 --> 00:11:44,445 how do we evaluate these technologies? Right? Do 310 00:11:44,445 --> 00:11:46,205 we continue to use, and I think we 311 00:11:46,205 --> 00:11:46,705 should, 312 00:11:47,164 --> 00:11:50,580 our clinical trials informed approach and, how robust, 313 00:11:50,580 --> 00:11:52,820 you know, should the robustness of that change 314 00:11:52,820 --> 00:11:55,080 based off of the risk of the application? 315 00:11:55,700 --> 00:11:57,539 Should it change based off of the risk 316 00:11:57,539 --> 00:11:58,679 of the use case? 317 00:11:59,220 --> 00:12:00,899 And then as part of that, we have 318 00:12:00,899 --> 00:12:02,980 phase four, right, the last phase is actually 319 00:12:02,980 --> 00:12:04,360 the monitoring phase. 320 00:12:04,674 --> 00:12:07,095 And I think that is the biggest opportunity, 321 00:12:08,595 --> 00:12:11,315 in the technology space actually is to ensure 322 00:12:11,315 --> 00:12:12,615 that we have tools 323 00:12:12,995 --> 00:12:15,894 that allow us to do the sufficient monitoring 324 00:12:16,035 --> 00:12:18,215 of these applications moving forward. 325 00:12:18,539 --> 00:12:20,940 Again, in somewhat of a risk based approach 326 00:12:20,940 --> 00:12:21,440 where, 327 00:12:21,899 --> 00:12:24,299 you know, some of the applications, some of 328 00:12:24,299 --> 00:12:26,299 the use cases may be fairly low risk. 329 00:12:26,299 --> 00:12:28,539 Think about a chatbot that is giving you 330 00:12:28,539 --> 00:12:30,720 directions to get from one place to another. 331 00:12:31,054 --> 00:12:32,654 Fairly low risk when it comes to health 332 00:12:32,654 --> 00:12:33,154 care, 333 00:12:34,014 --> 00:12:34,514 versus, 334 00:12:34,894 --> 00:12:36,574 some of the tools I was describing that 335 00:12:36,574 --> 00:12:38,495 don't quite exist yet, right, but may very 336 00:12:38,495 --> 00:12:39,934 well in the in the kind of near 337 00:12:39,934 --> 00:12:42,334 term future that are helping us with clinical 338 00:12:42,334 --> 00:12:45,089 decision making. Those are really high risk when 339 00:12:45,089 --> 00:12:46,709 it comes to to outcomes, 340 00:12:47,089 --> 00:12:49,569 and and quality of care. And so we 341 00:12:49,569 --> 00:12:52,129 need monitoring tools that are aligned with that 342 00:12:52,129 --> 00:12:54,149 risk evaluation, that risk assessment 343 00:12:55,169 --> 00:12:57,809 that allow us to monitor these technologies moving 344 00:12:57,809 --> 00:12:59,995 forward. And in some spaces, actually in quite 345 00:12:59,995 --> 00:13:02,154 a few spaces, the technology actually doesn't exist 346 00:13:02,154 --> 00:13:04,955 yet, to do that monitoring. And so really, 347 00:13:05,274 --> 00:13:07,514 looking forward to to seeing the evolution of 348 00:13:07,514 --> 00:13:09,774 the applications themselves and also, 349 00:13:10,154 --> 00:13:12,315 in our in the the applications that are 350 00:13:12,315 --> 00:13:13,855 going to monitor those applications, 351 00:13:14,459 --> 00:13:15,740 so that we can get in start to 352 00:13:15,740 --> 00:13:17,740 get into some of these higher risk but 353 00:13:17,740 --> 00:13:20,000 maybe higher reward spaces as well. 354 00:13:20,939 --> 00:13:23,500 That's helpful for to understand. And, you know, 355 00:13:23,500 --> 00:13:24,620 it seems like a really, 356 00:13:25,339 --> 00:13:27,259 great way to look at things and make 357 00:13:27,259 --> 00:13:28,799 sure that, you know, you're 358 00:13:29,934 --> 00:13:32,495 seeing the challenges, but also developing solutions and 359 00:13:32,495 --> 00:13:35,134 able to overcome them moving forward. Before we 360 00:13:35,134 --> 00:13:37,214 wrap up here, I'm curious. What is the 361 00:13:37,214 --> 00:13:39,455 number one thing that you can do right 362 00:13:39,455 --> 00:13:41,294 now to set yourself up for success in 363 00:13:41,294 --> 00:13:41,955 the future? 364 00:13:43,460 --> 00:13:44,820 I think, you know, one thing we're doing 365 00:13:44,820 --> 00:13:47,800 right now is actually pulling together the multidisciplinary 366 00:13:49,059 --> 00:13:52,100 team that needs to move and drive this 367 00:13:52,100 --> 00:13:52,600 forward. 368 00:13:53,220 --> 00:13:54,740 And so, you know, a year and a 369 00:13:54,740 --> 00:13:56,304 half, two years ago, we set up an 370 00:13:56,304 --> 00:13:59,184 AI governance committee really to establish what our 371 00:13:59,184 --> 00:14:01,764 rules of the road were, establish a responsible 372 00:14:01,825 --> 00:14:02,804 use of AI, 373 00:14:03,504 --> 00:14:04,804 framework for the organization. 374 00:14:05,345 --> 00:14:06,960 And now it's about what, 375 00:14:07,519 --> 00:14:09,120 groups of people do we need to bring 376 00:14:09,120 --> 00:14:10,820 together in order to realize, 377 00:14:11,679 --> 00:14:14,160 and bring to life these technologies. And so 378 00:14:14,160 --> 00:14:16,160 that is much more about kind of the 379 00:14:16,160 --> 00:14:17,620 the technologist that 380 00:14:18,080 --> 00:14:21,139 with the data infrastructure, with data analytics, 381 00:14:22,514 --> 00:14:25,254 with the rest of the organization that brings 382 00:14:25,315 --> 00:14:26,294 the use cases, 383 00:14:26,835 --> 00:14:30,034 for these for these, technologies. And so bringing 384 00:14:30,034 --> 00:14:31,735 those people together into, 385 00:14:32,674 --> 00:14:33,575 a multidisciplinary 386 00:14:34,115 --> 00:14:36,514 team that actually will drive all of this 387 00:14:36,514 --> 00:14:38,470 forward, I I think, is the biggest thing 388 00:14:38,470 --> 00:14:40,870 we can do at this point, in addition 389 00:14:40,870 --> 00:14:43,829 to working you know, continuing to work with 390 00:14:43,829 --> 00:14:46,569 the vendors of these technologies to really understand 391 00:14:46,629 --> 00:14:48,789 how they work, to evaluate them within the 392 00:14:48,789 --> 00:14:50,730 context of our own health care system, 393 00:14:51,225 --> 00:14:52,985 and as I mentioned, you know, develop the 394 00:14:52,985 --> 00:14:54,924 tools to monitor them going forward. 395 00:14:56,105 --> 00:14:58,184 That's amazing to hear. Doctor. Macheras, thank you 396 00:14:58,184 --> 00:14:59,704 so much for joining us on the podcast 397 00:14:59,704 --> 00:15:01,784 today. This has been a really fun conversation, 398 00:15:01,784 --> 00:15:03,225 and I look forward to connecting with you 399 00:15:03,225 --> 00:15:05,964 again soon. Thanks very much for having me.