1 00:00:00,080 --> 00:00:02,080 Hello, everyone. This is Jacob Emerson with the 2 00:00:02,080 --> 00:00:05,120 Becker's Digital Health and Health IT podcast. Thrilled 3 00:00:05,120 --> 00:00:07,620 today to be joined by doctor Mark Townsend, 4 00:00:07,679 --> 00:00:10,339 who is the chief clinical digital ventures officer 5 00:00:10,400 --> 00:00:13,214 at Bon Secours Mercy Health. Doctor Townshend, thank 6 00:00:13,214 --> 00:00:14,815 you so much for taking the time to 7 00:00:14,815 --> 00:00:16,595 sit down with me on the podcast today. 8 00:00:17,295 --> 00:00:19,375 Sure thing, Jacob. I think, thanks for having 9 00:00:19,375 --> 00:00:21,614 me. Yeah. Absolutely. And before we dive into 10 00:00:21,614 --> 00:00:23,295 everything we wanna talk with you about, can 11 00:00:23,295 --> 00:00:24,574 you tell us a little bit more about 12 00:00:24,574 --> 00:00:27,100 yourself, your background in health care, and what 13 00:00:27,100 --> 00:00:28,940 it is that you're doing today at Bon 14 00:00:28,940 --> 00:00:29,440 Secours? 15 00:00:30,539 --> 00:00:33,340 Yeah. Sure. So I'll break that into three, 16 00:00:33,340 --> 00:00:35,200 I guess. So beginning, with myself, 17 00:00:36,060 --> 00:00:36,560 serving 18 00:00:37,100 --> 00:00:39,439 as chief clinical digital ventures officer, 19 00:00:40,015 --> 00:00:40,515 is, 20 00:00:40,895 --> 00:00:43,215 the job of a lifetime. It's a super 21 00:00:43,215 --> 00:00:44,975 cool job, and I'll describe that in a 22 00:00:44,975 --> 00:00:45,954 little bit of detail. 23 00:00:47,375 --> 00:00:48,575 I I think it would make sense to 24 00:00:48,575 --> 00:00:49,875 begin with my background. 25 00:00:50,734 --> 00:00:52,575 And, so my background, 26 00:00:53,590 --> 00:00:55,210 began as a physician, obviously, 27 00:00:55,750 --> 00:00:58,409 intent on on practicing internal medicine pediatrics, 28 00:00:59,350 --> 00:01:01,369 which allowed me to go into adult congenital 29 00:01:01,429 --> 00:01:03,689 cardiology and pediatric cardiology. So, 30 00:01:04,390 --> 00:01:05,689 four boards later, 31 00:01:06,364 --> 00:01:08,364 I I had the honor and privilege of 32 00:01:08,364 --> 00:01:10,284 of being part of a specialty that was 33 00:01:10,284 --> 00:01:12,704 essentially up and coming adult congenital cardiology, 34 00:01:13,805 --> 00:01:15,245 which put me on the front end of 35 00:01:15,245 --> 00:01:19,245 program growth, and development and, actually, technology growth 36 00:01:19,245 --> 00:01:20,465 and development as well, 37 00:01:20,939 --> 00:01:23,099 which which really led to my current role. 38 00:01:23,099 --> 00:01:23,599 So, 39 00:01:24,140 --> 00:01:25,819 as you mentioned, I serve at Bon Secours 40 00:01:25,819 --> 00:01:28,459 Mercy Health, and, we are the fifth largest 41 00:01:28,459 --> 00:01:30,479 Catholic health care system in the country. 42 00:01:31,019 --> 00:01:33,280 50 facilities, 10 regional markets. 43 00:01:33,935 --> 00:01:35,855 And we have a company called Accrete, which 44 00:01:35,855 --> 00:01:37,555 is our digital ventures arm. 45 00:01:38,094 --> 00:01:38,734 And so, 46 00:01:39,215 --> 00:01:42,674 my job as an operator is representing essentially 47 00:01:43,375 --> 00:01:46,255 the the core of of BSMH and knowing 48 00:01:46,255 --> 00:01:48,115 the operators and the pain points 49 00:01:48,469 --> 00:01:50,229 of our boots on the ground people, if 50 00:01:50,229 --> 00:01:51,450 you will, in the health system. 51 00:01:52,229 --> 00:01:53,530 They then come forward, 52 00:01:53,909 --> 00:01:54,650 with technology, 53 00:01:55,269 --> 00:01:58,229 you know, requests specific to individual pain points 54 00:01:58,229 --> 00:01:59,129 or use cases. 55 00:01:59,670 --> 00:02:00,950 And then what we've been able to do 56 00:02:00,950 --> 00:02:03,234 through Recruit is put together acceleration funding. So 57 00:02:03,234 --> 00:02:05,174 an operator comes forward with a problem, 58 00:02:05,875 --> 00:02:06,375 and, 59 00:02:06,754 --> 00:02:08,995 and they will create, for example, an offset 60 00:02:08,995 --> 00:02:10,914 in their budget to support a pilot. What 61 00:02:10,914 --> 00:02:11,895 we then do 62 00:02:12,354 --> 00:02:13,735 is then provide majority 63 00:02:14,034 --> 00:02:16,020 funding in real time, if you will, so 64 00:02:16,099 --> 00:02:17,620 without having to wait for a budget cycle 65 00:02:17,620 --> 00:02:18,439 to go by. 66 00:02:19,460 --> 00:02:21,379 And then we can accelerate some of these 67 00:02:21,379 --> 00:02:24,020 these relationships and technologies, and we can scale 68 00:02:24,020 --> 00:02:26,280 them throughout our organization. So, 69 00:02:26,980 --> 00:02:29,139 again, I'm blessed to be in this role. 70 00:02:29,139 --> 00:02:31,455 Super cool space to be in, and, 71 00:02:31,935 --> 00:02:34,575 and and, definitely sets us up for for 72 00:02:34,575 --> 00:02:35,875 a good conversation today. 73 00:02:36,495 --> 00:02:39,055 Yeah. Absolutely. Well, I appreciate that overview, Mark. 74 00:02:39,055 --> 00:02:40,655 And the first thing we wanted to talk 75 00:02:40,655 --> 00:02:42,335 with you about is something that we're hearing 76 00:02:42,335 --> 00:02:44,415 about from health systems all over the country 77 00:02:44,415 --> 00:02:46,610 all the time, and it's how they're increasingly 78 00:02:47,069 --> 00:02:47,569 using 79 00:02:47,870 --> 00:02:49,650 real world data. So, 80 00:02:50,270 --> 00:02:51,870 my my first question for you is what 81 00:02:51,870 --> 00:02:53,730 are some of the main challenges and opportunities 82 00:02:54,189 --> 00:02:57,169 for health systems in terms of integrating unstructured 83 00:02:57,389 --> 00:03:00,370 data into their clinical workflows and and ultimately 84 00:03:00,735 --> 00:03:01,394 improving care 85 00:03:01,694 --> 00:03:02,834 delivery at scale? 86 00:03:03,775 --> 00:03:05,235 Yeah. Sure. Great question. 87 00:03:05,775 --> 00:03:07,694 And you point out there the difference between 88 00:03:07,694 --> 00:03:10,014 structured and unstructured data, which is which is 89 00:03:10,014 --> 00:03:11,614 a key part of the conversation as well. 90 00:03:11,614 --> 00:03:12,114 So 91 00:03:12,655 --> 00:03:15,314 challenges and opportunities, quite frankly, can be synonymous 92 00:03:15,455 --> 00:03:15,955 here. 93 00:03:16,629 --> 00:03:18,729 Data acquisition is changing rapidly, 94 00:03:19,110 --> 00:03:21,270 changing as we speak, and we'll we can 95 00:03:21,270 --> 00:03:22,650 talk a little bit about that. 96 00:03:23,669 --> 00:03:26,150 And and so, you know, specific to our 97 00:03:26,150 --> 00:03:27,290 approach to data, 98 00:03:27,990 --> 00:03:29,514 the opportunity for us, and I like to 99 00:03:29,514 --> 00:03:31,115 say I'm standing on the shoulder of giants 100 00:03:31,115 --> 00:03:33,754 here who actually built this, but we built 101 00:03:33,754 --> 00:03:34,895 a an intentional 102 00:03:35,194 --> 00:03:36,895 approach to data aggregation, 103 00:03:37,675 --> 00:03:40,555 a data mart. And, and and so that 104 00:03:40,555 --> 00:03:41,375 data repository 105 00:03:41,675 --> 00:03:43,775 essentially empowers us then, for example, 106 00:03:44,270 --> 00:03:46,370 to then, formalize relationships, 107 00:03:47,150 --> 00:03:49,870 and with technologies that can can help us. 108 00:03:49,870 --> 00:03:52,110 And so one of the technologies I'd like 109 00:03:52,110 --> 00:03:53,490 to highlight today is Omni. 110 00:03:53,790 --> 00:03:54,930 And so OmniHealth, 111 00:03:56,189 --> 00:03:57,169 is an ecosystem 112 00:03:58,105 --> 00:03:59,944 and, again, that they partner through a group 113 00:03:59,944 --> 00:04:02,844 for for BSMA for Bontecore Mercy Health. So 114 00:04:02,985 --> 00:04:03,485 OmniHealth, 115 00:04:04,265 --> 00:04:05,645 you know, itself, 116 00:04:06,105 --> 00:04:08,605 pulls in 1,000,000,000 patient encounters, 117 00:04:09,064 --> 00:04:11,724 three and a half billion unstructured clinical notes, 118 00:04:12,219 --> 00:04:14,539 and and really is helping us with our 119 00:04:14,539 --> 00:04:15,840 approach to unstructured 120 00:04:16,220 --> 00:04:19,180 documentation. So the difference, of course, being structured 121 00:04:19,180 --> 00:04:19,680 documentation 122 00:04:20,620 --> 00:04:24,225 turns clinicians into data entry technicians. Right? And 123 00:04:24,225 --> 00:04:26,305 we have to enter data into specific, 124 00:04:26,705 --> 00:04:28,564 specific fields or discrete fields, 125 00:04:29,105 --> 00:04:31,824 unstructured data. And and and this is kind 126 00:04:31,824 --> 00:04:33,425 of an interesting time to be having that 127 00:04:33,425 --> 00:04:36,084 conversation because ambient documentation is growing. 128 00:04:36,705 --> 00:04:38,324 And and I think anecdotally, 129 00:04:40,240 --> 00:04:42,479 we're seeing that the length of the the 130 00:04:42,479 --> 00:04:43,459 clinical documents 131 00:04:43,759 --> 00:04:44,259 increasing. 132 00:04:45,040 --> 00:04:46,660 We're capturing more problems, 133 00:04:47,040 --> 00:04:50,560 and, arguably, we're capturing more unstructured data. And, 134 00:04:50,560 --> 00:04:51,540 you know, it's been 135 00:04:54,014 --> 00:04:55,535 a a a a hot topic in in 136 00:04:55,535 --> 00:04:58,095 health care to to really implement these technologies 137 00:04:58,095 --> 00:04:59,235 with ambient dictation. 138 00:05:00,014 --> 00:05:02,574 So so back to then, you know, the 139 00:05:02,574 --> 00:05:04,574 the making sense, if you will, of all 140 00:05:04,574 --> 00:05:05,474 of this data. 141 00:05:06,779 --> 00:05:08,860 You know, it it is important to have 142 00:05:08,860 --> 00:05:10,620 a data strategy as a health system. But 143 00:05:10,620 --> 00:05:11,120 then, 144 00:05:11,660 --> 00:05:13,600 the tools to essentially help us, 145 00:05:14,139 --> 00:05:16,220 parse that data, to read that data, to 146 00:05:16,220 --> 00:05:17,899 mine it, to look for a needle in 147 00:05:17,899 --> 00:05:19,040 a haystack essentially 148 00:05:19,685 --> 00:05:20,004 are, 149 00:05:20,485 --> 00:05:22,645 you know, really the the value propositions that 150 00:05:22,645 --> 00:05:24,725 empower relationships like the one we have with 151 00:05:24,725 --> 00:05:25,225 Omni. 152 00:05:26,245 --> 00:05:28,165 Certainly. And let's talk a little bit about 153 00:05:28,165 --> 00:05:30,665 your your most recent partnership with OmniHealth. 154 00:05:31,125 --> 00:05:33,605 You're you're partnered with them to unlock valuable 155 00:05:33,605 --> 00:05:34,740 insights from unstructured 156 00:05:35,060 --> 00:05:37,779 clinical notes. You just mentioned the rise of 157 00:05:37,779 --> 00:05:40,259 ambient listening all at health systems all over 158 00:05:40,259 --> 00:05:40,839 the country, 159 00:05:41,459 --> 00:05:43,779 Mark. So can you expand a little bit 160 00:05:43,779 --> 00:05:46,099 upon that in terms of what what you 161 00:05:46,099 --> 00:05:47,720 see as some of the biggest opportunities 162 00:05:48,214 --> 00:05:50,634 in leveraging this type of data for improving 163 00:05:50,694 --> 00:05:53,254 the care to your patients and then clinical 164 00:05:53,254 --> 00:05:55,835 decision making within the broader health system? 165 00:05:56,535 --> 00:05:57,435 Yeah. Sure. 166 00:05:57,814 --> 00:05:59,654 You know, we have talked for, I feel 167 00:05:59,654 --> 00:06:01,654 like, a decade or more about, 168 00:06:02,689 --> 00:06:04,930 you know, the growing interest in genomics and 169 00:06:04,930 --> 00:06:06,469 precision medicine, etcetera. 170 00:06:07,409 --> 00:06:08,389 And and, 171 00:06:09,409 --> 00:06:11,329 again, you know, with with that kind of 172 00:06:11,329 --> 00:06:12,870 needle in the haystack construct, 173 00:06:14,050 --> 00:06:16,545 you know, the the difficulty that we've all 174 00:06:16,545 --> 00:06:18,545 encountered, I think, as health systems is how 175 00:06:18,545 --> 00:06:20,485 do you wrap care around an individual, 176 00:06:21,345 --> 00:06:23,504 and and how do you customize care to 177 00:06:23,504 --> 00:06:24,245 an individual? 178 00:06:25,185 --> 00:06:26,004 It's difficult 179 00:06:26,464 --> 00:06:27,764 when, you know, an individual, 180 00:06:28,625 --> 00:06:31,620 data set specific to a a given patient 181 00:06:32,240 --> 00:06:34,639 is part of a very large ecosystem. Right? 182 00:06:34,639 --> 00:06:36,720 And so, you know, being able to parse 183 00:06:36,720 --> 00:06:38,899 down and find a specific patient, 184 00:06:39,680 --> 00:06:40,660 with a specific 185 00:06:40,960 --> 00:06:42,020 clinical presentation, 186 00:06:42,879 --> 00:06:44,500 or with a very specific, 187 00:06:45,175 --> 00:06:47,175 you know, component in their their problem, 188 00:06:47,495 --> 00:06:50,134 statement or their their past medical history, that's 189 00:06:50,134 --> 00:06:52,134 that's an example of that in a very 190 00:06:52,134 --> 00:06:54,134 granular sense. If you could then start there 191 00:06:54,134 --> 00:06:56,855 and say, okay. Well, what we're addressing then 192 00:06:56,855 --> 00:06:58,235 essentially are care gaps, 193 00:06:59,019 --> 00:07:00,620 then you start to, you know, think a 194 00:07:00,620 --> 00:07:01,839 little bit more broadly. 195 00:07:02,620 --> 00:07:06,240 And, in in addressing care gaps, for example, 196 00:07:06,699 --> 00:07:07,599 we are all, 197 00:07:08,379 --> 00:07:11,339 pulling in social and behavioral determinants of health, 198 00:07:11,339 --> 00:07:12,719 right, as part of our screening 199 00:07:13,355 --> 00:07:15,115 of of all of our our patients and 200 00:07:15,115 --> 00:07:16,254 our clinical populations. 201 00:07:16,955 --> 00:07:18,014 As we do that, 202 00:07:18,475 --> 00:07:19,214 for example, 203 00:07:19,675 --> 00:07:21,194 we are picking up on, 204 00:07:21,675 --> 00:07:24,314 gaps in care, not only just gaps in 205 00:07:24,314 --> 00:07:27,295 clinical care, but, for example, gaps in resources. 206 00:07:28,230 --> 00:07:30,569 And so we've really doubled down on addressing 207 00:07:30,629 --> 00:07:31,129 resource, 208 00:07:31,670 --> 00:07:34,230 constraints as well for our patients, again, wrapping 209 00:07:34,230 --> 00:07:35,370 care around them. 210 00:07:35,750 --> 00:07:37,770 So, you know, real world examples, 211 00:07:38,470 --> 00:07:39,370 housing insecurity, 212 00:07:40,310 --> 00:07:40,810 BSMH, 213 00:07:42,194 --> 00:07:44,355 as a Catholic health system, has a housing 214 00:07:44,355 --> 00:07:46,915 initiative. And, in some of our markets, we 215 00:07:46,915 --> 00:07:48,214 can actually help with that. 216 00:07:48,995 --> 00:07:51,954 Transportation limitations, for example, are very real world 217 00:07:51,954 --> 00:07:52,454 examples. 218 00:07:53,074 --> 00:07:53,759 And, capturing that in an 219 00:07:54,720 --> 00:07:55,220 unstructured 220 00:07:55,520 --> 00:07:57,759 format, you know, just in a conversation with 221 00:07:57,759 --> 00:07:58,420 a patient, 222 00:07:59,279 --> 00:08:01,360 allows us then to close those gaps. So 223 00:08:01,360 --> 00:08:02,259 we have partners, 224 00:08:02,879 --> 00:08:05,520 in that space as well that can really, 225 00:08:05,759 --> 00:08:08,319 service our patients and and close those those 226 00:08:08,319 --> 00:08:09,220 need gaps. 227 00:08:09,894 --> 00:08:11,254 And then taking all of, 228 00:08:11,735 --> 00:08:12,634 those individual 229 00:08:12,935 --> 00:08:14,394 resource constraints essentially 230 00:08:15,175 --> 00:08:17,254 and then pulling them into an after visit 231 00:08:17,254 --> 00:08:20,474 summary. You know, they're very real, use cases, 232 00:08:20,774 --> 00:08:23,014 and technologies that we're partnering with there as 233 00:08:23,014 --> 00:08:23,514 well. 234 00:08:24,100 --> 00:08:25,639 And so all of that now, 235 00:08:26,339 --> 00:08:28,100 you know, brings us full circle to the 236 00:08:28,100 --> 00:08:30,740 to the construct of, big data. Right? And 237 00:08:30,740 --> 00:08:31,639 so big data 238 00:08:32,179 --> 00:08:34,440 then allows us to look, 239 00:08:34,820 --> 00:08:37,559 at a population level for insights, and population 240 00:08:37,779 --> 00:08:38,600 based insights, 241 00:08:39,625 --> 00:08:41,544 you know, are really kind of exciting. And 242 00:08:41,544 --> 00:08:42,024 and, 243 00:08:42,504 --> 00:08:44,365 you know, whether or not we're talking about, 244 00:08:45,865 --> 00:08:47,884 you know, congestive heart failure 245 00:08:48,264 --> 00:08:50,584 as a KPI or, you know, whether or 246 00:08:50,584 --> 00:08:51,245 not, again, 247 00:08:51,620 --> 00:08:54,440 we're we're looking to match a specific patient, 248 00:08:55,220 --> 00:08:56,679 with a specific tumor 249 00:08:57,299 --> 00:08:59,299 with a clinical trial. Right? So all of 250 00:08:59,299 --> 00:09:01,480 those lend themselves to technology, 251 00:09:02,419 --> 00:09:04,919 again, empowered by data as an asset. 252 00:09:06,264 --> 00:09:08,184 Certainly. And just listening to you talk about 253 00:09:08,184 --> 00:09:09,865 this market, it seems like there's so much 254 00:09:09,865 --> 00:09:12,585 potential for this data. So I think the 255 00:09:12,585 --> 00:09:14,745 the natural follow-up question is, how do you 256 00:09:14,745 --> 00:09:18,024 foresee the role of health systems evolving in 257 00:09:18,024 --> 00:09:20,559 terms of using this type of data to 258 00:09:20,559 --> 00:09:23,440 drive innovation and to improve the outcomes for 259 00:09:23,440 --> 00:09:24,179 their patients? 260 00:09:25,600 --> 00:09:28,100 Yeah. Well, key term evolving. 261 00:09:29,120 --> 00:09:32,179 So it is evolving. It's evolving very quickly. 262 00:09:32,320 --> 00:09:33,460 And and I think 263 00:09:34,105 --> 00:09:36,264 you know, the expectations of our patients and 264 00:09:36,264 --> 00:09:38,745 the populations that we serve are evolving as 265 00:09:38,745 --> 00:09:41,085 well. Right? So, you know, I referenced essentially 266 00:09:41,144 --> 00:09:42,445 how they receive care, 267 00:09:43,225 --> 00:09:44,684 you know, how we match, 268 00:09:45,304 --> 00:09:48,184 you know, specific constraints in care delivery to 269 00:09:48,184 --> 00:09:49,404 to specific needs. 270 00:09:50,320 --> 00:09:50,980 The evolution, 271 00:09:51,919 --> 00:09:55,539 of of essentially using data to to drive 272 00:09:55,600 --> 00:09:56,100 innovation. 273 00:09:57,039 --> 00:09:57,539 Again, 274 00:09:58,320 --> 00:10:00,559 you know, it's it's worth doubling down on, 275 00:10:00,559 --> 00:10:01,220 for example, 276 00:10:01,679 --> 00:10:03,860 specific disease processes. So 277 00:10:04,315 --> 00:10:06,315 so the email I wrote right before jumping 278 00:10:06,315 --> 00:10:09,035 on this this call with you, was specific 279 00:10:09,035 --> 00:10:10,575 to a new structural cardiologist 280 00:10:10,955 --> 00:10:12,335 in our Kentucky market, 281 00:10:13,355 --> 00:10:14,795 which is awesome. And, 282 00:10:15,115 --> 00:10:16,875 as he as he joins, you know, his 283 00:10:16,875 --> 00:10:19,970 first question is, alright. So I want to 284 00:10:19,970 --> 00:10:21,590 essentially parse datasets 285 00:10:21,970 --> 00:10:23,590 specific to the application 286 00:10:24,610 --> 00:10:25,830 of clinical guidelines, 287 00:10:26,690 --> 00:10:28,450 and and I want to find patients who 288 00:10:28,450 --> 00:10:29,429 have care gaps. 289 00:10:29,970 --> 00:10:31,330 And and so I was able to write 290 00:10:31,330 --> 00:10:33,009 back. I was like, well, sure. We can 291 00:10:33,009 --> 00:10:35,574 do that. So beginning with aortic stenosis, for 292 00:10:35,574 --> 00:10:37,034 example, I mean, we can take, 293 00:10:38,214 --> 00:10:41,434 guidelines, and guidelines, for example, used to live, 294 00:10:41,735 --> 00:10:44,235 for example, in the brains of my partners, 295 00:10:44,375 --> 00:10:46,294 my cells, and, you know, the the, 296 00:10:47,429 --> 00:10:49,509 the resources that we use to to get 297 00:10:49,509 --> 00:10:51,690 our patient care, you can take those guidelines 298 00:10:51,750 --> 00:10:53,910 now as an algorithm essentially, and you can 299 00:10:53,910 --> 00:10:56,309 use them to curate a list of patients 300 00:10:56,309 --> 00:10:57,610 who have a care gap, 301 00:10:58,309 --> 00:11:01,174 around a very specific disease state. State. Again, 302 00:11:01,174 --> 00:11:03,415 my background in cardiology. So using this example, 303 00:11:03,415 --> 00:11:05,894 aortic stenosis, you know, thickening of the aortic 304 00:11:05,894 --> 00:11:08,535 valve, give me a list of patients who 305 00:11:08,535 --> 00:11:10,634 meet the clinical criteria for intervention 306 00:11:11,174 --> 00:11:13,399 but who have not received appropriate care. Right? 307 00:11:13,399 --> 00:11:15,080 So we can do that, and then we 308 00:11:15,080 --> 00:11:16,379 can essentially then, 309 00:11:17,000 --> 00:11:18,840 turn that over to a navigator or a 310 00:11:18,840 --> 00:11:21,559 new structural cardiologist in Paducah, Kentucky and say, 311 00:11:21,559 --> 00:11:23,480 here you go. These are patients that would, 312 00:11:24,200 --> 00:11:26,360 very, very much appreciate a call from you 313 00:11:26,360 --> 00:11:29,454 all. And and so that that is it's 314 00:11:29,454 --> 00:11:32,274 it's, evolving very quickly, but it's exciting. 315 00:11:33,615 --> 00:11:34,115 And 316 00:11:34,495 --> 00:11:35,475 and quite frankly, 317 00:11:36,334 --> 00:11:38,654 you know, sometimes a little bit overwhelming. Right? 318 00:11:38,975 --> 00:11:40,735 You know, the the technology is coming at 319 00:11:40,735 --> 00:11:41,774 us pretty quickly. So, 320 00:11:42,659 --> 00:11:44,259 you know, I'd like to say that that 321 00:11:44,259 --> 00:11:45,399 we're keeping pace, 322 00:11:46,019 --> 00:11:47,700 as a health system, and, 323 00:11:48,179 --> 00:11:50,659 I don't think we're, like, bleeding edge, but 324 00:11:50,659 --> 00:11:53,059 but but we we aspire to absolutely be 325 00:11:53,059 --> 00:11:55,240 leading edge. Right? I mean, the early adopters. 326 00:11:56,514 --> 00:11:58,274 Absolutely. And that's a great example that you 327 00:11:58,274 --> 00:12:00,115 shared with us, so I really appreciate that, 328 00:12:00,115 --> 00:12:02,355 Mark. And to follow-up on on your last 329 00:12:02,355 --> 00:12:04,835 point there, exciting, but but certainly can be 330 00:12:04,835 --> 00:12:08,615 overwhelming. So, you know, what's your advice for 331 00:12:08,809 --> 00:12:11,289 the other health system leaders listening in right 332 00:12:11,289 --> 00:12:13,529 now in terms of how they can better 333 00:12:13,529 --> 00:12:16,750 build a data driven culture like Bon Secours 334 00:12:17,049 --> 00:12:20,009 within their organizations, particularly as they also work 335 00:12:20,009 --> 00:12:23,470 to integrate diverse data sources from from EHRs, 336 00:12:23,884 --> 00:12:25,044 from clinical notes, 337 00:12:25,485 --> 00:12:27,424 and from patient reported outcomes? 338 00:12:28,605 --> 00:12:31,164 Yeah. Great question and a challenging one as 339 00:12:31,164 --> 00:12:32,384 well. I think the challenge 340 00:12:32,764 --> 00:12:33,964 for us as, 341 00:12:34,365 --> 00:12:35,904 operators, health care administrators, 342 00:12:36,205 --> 00:12:37,105 physician leaders 343 00:12:37,919 --> 00:12:39,839 is is to be able to challenge our 344 00:12:39,839 --> 00:12:42,399 health systems to really view data as an 345 00:12:42,399 --> 00:12:44,100 asset. Right? And so, 346 00:12:45,120 --> 00:12:46,980 I've I've had a hand throughout my career 347 00:12:47,679 --> 00:12:51,065 in data aggregation and and fortunately, was, you 348 00:12:51,065 --> 00:12:53,144 know, known as a data geek. And, hence, 349 00:12:53,144 --> 00:12:55,065 the people would come and as they were 350 00:12:55,065 --> 00:12:57,464 building, you know, our our first enterprise data 351 00:12:57,464 --> 00:12:59,464 warehouse, for example, in a prior health system, 352 00:12:59,464 --> 00:13:01,304 they would come to me and say, so 353 00:13:01,304 --> 00:13:03,263 give us examples of use cases. That's where 354 00:13:03,263 --> 00:13:03,870 there's a disconnect. 355 00:13:04,329 --> 00:13:05,709 And, again, you can pull together 356 00:13:06,329 --> 00:13:08,889 brilliant data teams, you know, who can aggregate 357 00:13:08,889 --> 00:13:10,029 data. There's infrastructure 358 00:13:10,569 --> 00:13:12,110 out there readily accessible, 359 00:13:13,289 --> 00:13:16,514 and and that has absolutely accelerated. But but 360 00:13:16,514 --> 00:13:17,975 it does need to be championed, 361 00:13:18,995 --> 00:13:21,154 you know, by by someone who who can 362 00:13:21,154 --> 00:13:23,254 actually understand the use cases. Right? 363 00:13:23,955 --> 00:13:26,514 So so finding out whether or not you 364 00:13:26,514 --> 00:13:28,434 have a data strategy, for example, whether or 365 00:13:28,434 --> 00:13:30,690 not you're aggregating your data, whether or not 366 00:13:30,690 --> 00:13:32,850 you've created a data mart, I think, you 367 00:13:32,850 --> 00:13:34,290 know, would be a good place to start 368 00:13:34,290 --> 00:13:35,670 and, you know, particularly 369 00:13:36,129 --> 00:13:38,370 smaller health systems, large health systems, of course, 370 00:13:38,370 --> 00:13:40,290 I think, you know, have invested in this 371 00:13:40,290 --> 00:13:40,790 strategy. 372 00:13:41,809 --> 00:13:42,769 But but then, 373 00:13:43,250 --> 00:13:44,950 coming forward with use cases 374 00:13:45,825 --> 00:13:48,004 and connecting with the clinicians who, 375 00:13:48,465 --> 00:13:48,965 again, 376 00:13:49,425 --> 00:13:52,225 you know, absolutely can tell you what they 377 00:13:52,225 --> 00:13:54,004 are seeing in terms of the technology. 378 00:13:54,785 --> 00:13:55,764 Younger physicians, 379 00:13:56,304 --> 00:13:59,105 older physicians are keeping pace. They they are 380 00:13:59,105 --> 00:14:00,245 constantly approached 381 00:14:01,339 --> 00:14:03,740 by our vendors in in the digital world, 382 00:14:03,740 --> 00:14:05,659 and, they can often point to, you know, 383 00:14:05,659 --> 00:14:07,120 very specific use cases. 384 00:14:07,899 --> 00:14:10,059 So I think then back to VSMH and 385 00:14:10,059 --> 00:14:12,620 our strategy, we recognized that we needed to 386 00:14:12,620 --> 00:14:15,039 be able to engage in real time 387 00:14:15,375 --> 00:14:18,014 And building that infrastructure that, you know, again, 388 00:14:18,014 --> 00:14:20,174 wasn't going to take a a a great 389 00:14:20,174 --> 00:14:22,095 idea and turn it into an eighteen month 390 00:14:22,095 --> 00:14:22,595 process 391 00:14:23,134 --> 00:14:25,134 was was really the challenge that we needed 392 00:14:25,134 --> 00:14:27,154 to address. And so being able to 393 00:14:27,500 --> 00:14:28,559 use a great acceleration, 394 00:14:29,339 --> 00:14:31,360 for example, and, you know, build a infrastructure 395 00:14:31,659 --> 00:14:32,879 that allows us to 396 00:14:33,419 --> 00:14:35,820 to, you know, to to finance and fund 397 00:14:35,820 --> 00:14:38,299 some of these programs in real time, that 398 00:14:38,299 --> 00:14:40,779 has been key for us. And, it really 399 00:14:40,779 --> 00:14:43,120 does engage your teams as well. So 400 00:14:43,475 --> 00:14:45,715 all lessons that we have learned are learning. 401 00:14:45,715 --> 00:14:47,955 We've got a lot to learn. We certainly 402 00:14:47,955 --> 00:14:50,274 haven't got it all figured out. I don't 403 00:14:50,274 --> 00:14:51,634 think anybody has. But, 404 00:14:52,514 --> 00:14:55,575 but it, again, takes, teams, and we're fortunate 405 00:14:55,634 --> 00:14:58,035 to to have teams that that really support 406 00:14:58,035 --> 00:14:58,695 this work. 407 00:14:59,940 --> 00:15:02,339 Fantastic. Well, Mark, what else are we missing? 408 00:15:02,339 --> 00:15:05,139 Any final thoughts you wanna offer our listeners 409 00:15:05,139 --> 00:15:05,639 today? 410 00:15:06,659 --> 00:15:08,419 You know, I often use this as the 411 00:15:08,419 --> 00:15:10,820 rallying cry, which is we're at the point 412 00:15:10,820 --> 00:15:11,320 where 413 00:15:11,865 --> 00:15:14,845 clinicians have reached the tipping point. Right? Cognitive 414 00:15:15,144 --> 00:15:18,184 assistance is required. The amount of information and 415 00:15:18,184 --> 00:15:20,445 data coming at us in health care 416 00:15:20,904 --> 00:15:23,625 is really beyond the human capacity, I think, 417 00:15:23,625 --> 00:15:25,085 to keep up. And so 418 00:15:25,509 --> 00:15:26,009 so, 419 00:15:26,470 --> 00:15:27,610 we we are thankfully 420 00:15:27,990 --> 00:15:29,909 living in an era where, you know, we 421 00:15:29,909 --> 00:15:31,129 we can empower, 422 00:15:31,750 --> 00:15:32,490 our teams, 423 00:15:33,269 --> 00:15:34,870 not only with data, but then with the 424 00:15:34,870 --> 00:15:37,429 tools, in many cases, AI driven tools that 425 00:15:37,429 --> 00:15:40,544 that allow us to to effectively overcome the 426 00:15:40,544 --> 00:15:43,345 the cognitive limitations. So this is my rallying 427 00:15:43,345 --> 00:15:45,845 cry. We've got to work smarter, not harder. 428 00:15:46,304 --> 00:15:48,245 And, I'll leave you with that, Jacob. 429 00:15:48,865 --> 00:15:51,184 Wonderful. Well, doctor Townsend, thank you so much 430 00:15:51,184 --> 00:15:52,544 for taking the time to sit down with 431 00:15:52,544 --> 00:15:54,559 us and for sharing your insights with our 432 00:15:54,559 --> 00:15:56,259 audience. We really appreciate it. 433 00:15:56,720 --> 00:15:59,440 My pleasure. Thanks for having me. Yeah. And 434 00:15:59,440 --> 00:16:01,360 if you'd like to listen to more podcasts 435 00:16:01,360 --> 00:16:05,299 from Becker's HealthCare, you can visit beckershospitalreview.com.