1 00:00:00,240 --> 00:00:02,580 Imagine this. You're in the heart of Chicago 2 00:00:02,639 --> 00:00:05,059 mingling with the brightest minds in health IT. 3 00:00:05,279 --> 00:00:07,825 You've arrived at the 9th annual Health IT 4 00:00:07,904 --> 00:00:10,484 plus Digital Health plus RCM conference 5 00:00:10,785 --> 00:00:13,344 taking place October 1st through 4th at the 6 00:00:13,344 --> 00:00:15,525 luxurious Hyatt Regency Chicago. 7 00:00:15,939 --> 00:00:18,739 Picture the excitement as you collect countless business 8 00:00:18,739 --> 00:00:22,339 cards, forging invaluable connections with over 25 100 9 00:00:22,339 --> 00:00:23,804 executive level attendees. 10 00:00:24,364 --> 00:00:26,605 Feel the buzz of ideas flowing as you 11 00:00:26,605 --> 00:00:29,405 engage in meaningful conversations about the future of 12 00:00:29,405 --> 00:00:30,385 healthcare technology. 13 00:00:30,925 --> 00:00:34,250 Envision yourself attending sessions led by over 415 14 00:00:35,109 --> 00:00:36,009 elite speakers 15 00:00:36,469 --> 00:00:39,049 gaining insights that could transform your organization. 16 00:00:39,509 --> 00:00:43,015 From digital transformation and telehealth to clinician burnout 17 00:00:43,015 --> 00:00:43,835 and cybersecurity, 18 00:00:44,614 --> 00:00:47,094 each topic is designed to spark new ideas 19 00:00:47,094 --> 00:00:49,894 and provide actionable takeaways, but it doesn't stop 20 00:00:49,894 --> 00:00:53,000 there. Imagine sitting in a packed auditorium listening 21 00:00:53,000 --> 00:00:55,239 to motivating keynotes from some of the biggest 22 00:00:55,239 --> 00:00:56,219 names in sports. 23 00:00:56,520 --> 00:00:58,939 Four time Super Bowl champion Rob Gronkowski, 24 00:00:59,719 --> 00:01:03,695 WNBA champion and author Lisa Leslie, and NFL 25 00:01:03,755 --> 00:01:05,694 legend and ESPN analyst 26 00:01:06,075 --> 00:01:08,635 Eli Manning will be there sharing their stories 27 00:01:08,635 --> 00:01:10,974 and inspiring you to reach new heights. 28 00:01:11,275 --> 00:01:13,759 All around you, the future of Health IT 29 00:01:13,819 --> 00:01:15,840 unfolds, and you're not just a spectator, 30 00:01:16,219 --> 00:01:17,759 you're an active participant. 31 00:01:18,299 --> 00:01:20,560 Don't wait. You can find the event website 32 00:01:20,619 --> 00:01:21,280 and get 33 00:01:21,685 --> 00:01:22,984 by visiting beckershospitalreview.com 34 00:01:25,125 --> 00:01:27,924 and clicking on the events page. That's the 35 00:01:27,924 --> 00:01:28,424 beckershospitalreview.com 36 00:01:30,245 --> 00:01:31,145 events page. 37 00:01:31,829 --> 00:01:34,549 Hello, everyone. This is Jacob Emerson with Becker's 38 00:01:34,549 --> 00:01:37,429 Digital Health and Health IT podcast. Thanks so 39 00:01:37,429 --> 00:01:39,614 much for tuning in to all of the 40 00:01:39,614 --> 00:01:41,795 media opportunities with Becker's Healthcare. 41 00:01:42,094 --> 00:01:44,094 Today, we're thrilled to be joined by a 42 00:01:44,094 --> 00:01:47,890 special guest. Doctor. Rohit Chandra is executive vice 43 00:01:47,890 --> 00:01:51,350 president and chief digital officer at Cleveland Clinic. 44 00:01:51,490 --> 00:01:53,490 Doctor Chandra, thank you so much for taking 45 00:01:53,490 --> 00:01:54,850 the time to be with us on the 46 00:01:54,850 --> 00:01:55,829 podcast today. 47 00:01:56,355 --> 00:01:57,335 Oh, thank you. 48 00:01:58,275 --> 00:02:00,435 So, Rohit, there's a lot we wanna talk 49 00:02:00,435 --> 00:02:02,435 with you about today. But to get us 50 00:02:02,435 --> 00:02:04,969 started more broadly, there's obviously a lot going 51 00:02:04,969 --> 00:02:07,709 on right now all across the healthcare tech 52 00:02:08,090 --> 00:02:09,769 world. So so talk to us a little 53 00:02:09,769 --> 00:02:11,930 bit about from your perspective, the the trends 54 00:02:11,930 --> 00:02:14,145 that you're watching most closely right now that 55 00:02:14,284 --> 00:02:17,245 you think isn't receiving enough attention either in 56 00:02:17,245 --> 00:02:19,665 the media or among your industry colleagues. 57 00:02:20,205 --> 00:02:21,885 What what's top of mind for you right 58 00:02:21,885 --> 00:02:22,990 now and within your role? 59 00:02:25,230 --> 00:02:27,870 I think there's a few different areas that 60 00:02:27,870 --> 00:02:29,810 we're trying hard to pay attention to. 61 00:02:30,514 --> 00:02:32,375 Stepping back. I think the 62 00:02:32,914 --> 00:02:33,414 developments 63 00:02:33,875 --> 00:02:34,775 in AI, 64 00:02:35,314 --> 00:02:37,474 especially over the last couple of years have 65 00:02:37,474 --> 00:02:37,974 been 66 00:02:38,610 --> 00:02:40,930 mind blowing. And I think the potential is 67 00:02:40,930 --> 00:02:41,430 remarkable. 68 00:02:42,689 --> 00:02:44,209 I think a few trends that I think 69 00:02:44,209 --> 00:02:45,969 we're trying hard to keep an eye on 70 00:02:45,969 --> 00:02:46,469 is 71 00:02:47,044 --> 00:02:49,705 this traditional machine learning, which is well understood 72 00:02:50,564 --> 00:02:52,405 and or at least much better understood in 73 00:02:52,405 --> 00:02:54,884 the industry and making sure that we're leveraging 74 00:02:54,884 --> 00:02:56,745 that where it makes sense is important. 75 00:02:57,349 --> 00:02:59,769 And separate that a little bit from generative 76 00:02:59,909 --> 00:03:01,509 AI, which has shown up in the flavor 77 00:03:01,509 --> 00:03:02,489 of chat GPT 78 00:03:03,110 --> 00:03:04,569 that is has 79 00:03:05,224 --> 00:03:07,965 remarkably different potential to help streamline healthcare. 80 00:03:08,824 --> 00:03:10,504 So I think keeping an eye on both 81 00:03:10,504 --> 00:03:12,905 capabilities that are emerging from technology, I think 82 00:03:12,905 --> 00:03:13,645 is important. 83 00:03:14,719 --> 00:03:16,879 The second part I would say is that 84 00:03:16,879 --> 00:03:17,780 within healthcare, 85 00:03:18,879 --> 00:03:21,379 we all get excited by the, 86 00:03:21,865 --> 00:03:24,905 I will say the shiny objects that have 87 00:03:24,905 --> 00:03:27,085 to do with frontline clinical care. 88 00:03:27,865 --> 00:03:29,639 But given that AI 89 00:03:30,180 --> 00:03:32,659 is an imperfect technology that we're still trying 90 00:03:32,659 --> 00:03:34,580 to learn how to deploy and use in 91 00:03:34,580 --> 00:03:36,280 a responsible and safe way, 92 00:03:36,705 --> 00:03:38,485 it's important to not just 93 00:03:39,025 --> 00:03:41,185 look at clinical use cases, but to also 94 00:03:41,185 --> 00:03:41,844 look at 95 00:03:42,305 --> 00:03:44,784 nonclinical use cases where we can learn how 96 00:03:44,784 --> 00:03:46,159 to use it, apply 97 00:03:46,539 --> 00:03:48,620 it in a much safer setting where there 98 00:03:48,620 --> 00:03:50,719 is minimal risk to patient harm. 99 00:03:52,139 --> 00:03:52,639 The 100 00:03:53,115 --> 00:03:54,715 third area that we're trying to keep an 101 00:03:54,715 --> 00:03:55,854 eye on is 102 00:03:56,235 --> 00:03:58,875 just technology is an enabler. It's important to 103 00:03:58,875 --> 00:04:00,875 think in terms of the healthcare problem so 104 00:04:00,875 --> 00:04:03,219 that it is our responsibility to connect the 105 00:04:03,219 --> 00:04:05,620 dots from technology to the change that we 106 00:04:05,620 --> 00:04:07,319 wish to drive within healthcare. 107 00:04:09,344 --> 00:04:11,425 Absolutely. And I wanna follow-up with a really 108 00:04:11,425 --> 00:04:13,745 important point you just made about AI's use 109 00:04:13,745 --> 00:04:17,160 cases beyond just clinical care. As I understand 110 00:04:17,160 --> 00:04:19,319 it at Cleveland Clinic, you're using AI in 111 00:04:19,319 --> 00:04:21,259 a lot of different ways right now, including 112 00:04:21,399 --> 00:04:24,314 accelerating the pace of medical research and 113 00:04:24,615 --> 00:04:26,555 addressing staffing and organizational 114 00:04:26,935 --> 00:04:27,435 challenges. 115 00:04:27,975 --> 00:04:30,134 So Rohit, if you were going to look 116 00:04:30,134 --> 00:04:31,814 back over the last, let's say 6 to 117 00:04:31,814 --> 00:04:34,800 18 months, is there any initiative or accomplishment 118 00:04:34,860 --> 00:04:36,459 that that you're most proud of within this 119 00:04:36,459 --> 00:04:37,819 space? And and can you tell us a 120 00:04:37,819 --> 00:04:38,879 little bit about that? 121 00:04:40,235 --> 00:04:42,555 So we focus on a few different areas 122 00:04:42,555 --> 00:04:46,014 that actually span both clinical and nonclinical. 123 00:04:48,439 --> 00:04:50,439 I give a few a few examples from 124 00:04:50,439 --> 00:04:53,080 perhaps each of those. In clinical care, we've 125 00:04:53,080 --> 00:04:55,180 been working on an effort to do 126 00:04:56,254 --> 00:04:57,634 prediction of the onset 127 00:04:57,935 --> 00:04:58,595 of sepsis. 128 00:04:59,215 --> 00:05:01,074 Now, sepsis is a 129 00:05:02,014 --> 00:05:04,835 bloodstream infection that has a very high mortality 130 00:05:04,975 --> 00:05:05,475 rate, 131 00:05:05,775 --> 00:05:07,839 and and it kills nearly like a 1000 132 00:05:07,839 --> 00:05:10,579 people in the country almost every day. 133 00:05:11,120 --> 00:05:13,379 And with those numbers, it becomes 134 00:05:15,824 --> 00:05:17,985 quite imperative for us to bring everything we 135 00:05:17,985 --> 00:05:20,084 can to bear on treating sepsis. 136 00:05:20,785 --> 00:05:22,545 So that's an area where we're using machine 137 00:05:22,545 --> 00:05:24,589 learning and AI to predict the onset of 138 00:05:24,589 --> 00:05:25,089 sepsis, 139 00:05:25,870 --> 00:05:26,370 to 140 00:05:26,830 --> 00:05:29,730 integrate that with our clinical care protocols 141 00:05:30,430 --> 00:05:32,290 so that we can alert our caregivers 142 00:05:32,714 --> 00:05:35,115 to the onset of sepsis and drive early 143 00:05:35,115 --> 00:05:35,615 intervention. 144 00:05:36,154 --> 00:05:38,014 And that is the best known methodology 145 00:05:38,314 --> 00:05:40,149 to help take care of our patients better. 146 00:05:40,629 --> 00:05:42,229 So that's a clinical use case and I'm 147 00:05:42,229 --> 00:05:44,069 quite encouraged by the data that we have 148 00:05:44,069 --> 00:05:46,089 so far where if we can get roughly 149 00:05:46,629 --> 00:05:47,129 6 150 00:05:47,535 --> 00:05:51,055 times better positive predictive value of patients with 151 00:05:51,055 --> 00:05:52,115 the onset of sepsis 152 00:05:52,894 --> 00:05:54,975 with very few false alarms. And this is 153 00:05:54,975 --> 00:05:55,475 critical 154 00:05:55,970 --> 00:05:58,530 to our caregivers so that their resources can 155 00:05:58,530 --> 00:06:00,689 be deployed on the patients that need it 156 00:06:00,689 --> 00:06:01,350 the most. 157 00:06:02,210 --> 00:06:04,550 That's an example in clinical care. 158 00:06:05,884 --> 00:06:06,785 For caregiver, 159 00:06:07,964 --> 00:06:09,644 there's a second area which really has to 160 00:06:09,644 --> 00:06:10,544 do with work, 161 00:06:11,644 --> 00:06:12,144 staffing 162 00:06:12,444 --> 00:06:12,944 and 163 00:06:15,240 --> 00:06:16,300 caregiver burnout 164 00:06:16,600 --> 00:06:17,579 where we are leveraging 165 00:06:18,680 --> 00:06:20,060 AI powered scribes. 166 00:06:20,600 --> 00:06:23,500 And think of this as physicians and nurses 167 00:06:23,720 --> 00:06:24,220 spend 168 00:06:25,785 --> 00:06:26,845 anywhere from 169 00:06:28,264 --> 00:06:30,045 1 to 3 hours a day 170 00:06:30,504 --> 00:06:31,965 on clinical documentation. 171 00:06:32,920 --> 00:06:36,040 Now this documentation is essential for patient care 172 00:06:36,040 --> 00:06:37,420 and for regulatory reasons, 173 00:06:37,879 --> 00:06:39,819 but it is very time consuming. 174 00:06:40,464 --> 00:06:43,025 And one can imagine that technology that can 175 00:06:43,025 --> 00:06:44,805 automatically listen to a conversation, 176 00:06:45,824 --> 00:06:49,490 transcribe it, summarize it, has the potential to 177 00:06:49,490 --> 00:06:50,709 significantly streamline 178 00:06:51,250 --> 00:06:52,790 the efficiency of the caregivers. 179 00:06:53,329 --> 00:06:54,870 So that's an area where 180 00:06:55,175 --> 00:06:57,514 we're actually working with a few different companies 181 00:06:57,574 --> 00:06:58,955 to test drive their technologies. 182 00:06:59,735 --> 00:07:00,235 And 183 00:07:00,775 --> 00:07:03,050 if you're successful in this effort, we will 184 00:07:03,050 --> 00:07:04,670 have the potential to substantially 185 00:07:05,290 --> 00:07:07,149 streamline the work of our caregivers. 186 00:07:08,810 --> 00:07:10,330 A third area, which is still in the 187 00:07:10,330 --> 00:07:12,535 early stages is actually the other extreme of 188 00:07:12,535 --> 00:07:14,475 the spectrum, which is back office. 189 00:07:15,095 --> 00:07:16,955 And there we're focusing on 190 00:07:17,574 --> 00:07:19,115 the problem of coding. 191 00:07:19,790 --> 00:07:23,410 Now coding is essentially the task of taking 192 00:07:23,550 --> 00:07:24,770 a patient encounter 193 00:07:25,709 --> 00:07:28,814 and the documentation associated with it and generating 194 00:07:28,814 --> 00:07:30,995 billing codes for reimbursement purposes. 195 00:07:31,535 --> 00:07:33,394 It's a necessary part of our business, 196 00:07:33,935 --> 00:07:36,834 but it is also very manual, very cumbersome, 197 00:07:37,055 --> 00:07:38,209 and very costly. I 198 00:07:42,102 --> 00:07:42,602 think 199 00:07:46,814 --> 00:07:48,675 Now if I step back, I think, 200 00:07:49,134 --> 00:07:51,154 the question you asked a little bit was 201 00:07:51,535 --> 00:07:52,975 what are the things that we're most proud 202 00:07:52,975 --> 00:07:54,720 of? I like these initiatives 203 00:07:55,339 --> 00:07:58,860 in that they touch on different areas of 204 00:07:58,860 --> 00:08:00,160 our operations. 205 00:08:01,180 --> 00:08:03,519 The other part I think that's important is 206 00:08:04,845 --> 00:08:07,004 they're a good set of initial steps to 207 00:08:07,004 --> 00:08:09,504 take in bringing AI into the organization. 208 00:08:10,365 --> 00:08:13,229 And they're teaching us the playbook, the methodology 209 00:08:13,449 --> 00:08:13,949 on 210 00:08:14,329 --> 00:08:16,889 how to engage with different partners, how to 211 00:08:16,889 --> 00:08:19,209 bring those technologies in, how to streamline the 212 00:08:19,209 --> 00:08:21,149 process of leveraging those technologies 213 00:08:21,584 --> 00:08:24,164 in a clinical care context or a caregiver 214 00:08:24,305 --> 00:08:26,865 context. And this methodology and playbook is I 215 00:08:26,865 --> 00:08:28,625 think going to be the foundation for our 216 00:08:28,625 --> 00:08:30,004 success going forward. 217 00:08:31,540 --> 00:08:34,100 Wow. Well, those are some really fantastic examples. 218 00:08:34,100 --> 00:08:35,779 I I appreciate you sharing those with us. 219 00:08:35,779 --> 00:08:37,860 And that that last one you mentioned about 220 00:08:37,860 --> 00:08:39,160 back office coding, 221 00:08:39,574 --> 00:08:41,735 you mentioned that was new, Rohit. Where are 222 00:08:41,735 --> 00:08:43,495 you at with that? What's what's the timeline 223 00:08:43,495 --> 00:08:45,034 of that most recent initiative? 224 00:08:46,454 --> 00:08:46,855 So, 225 00:08:47,889 --> 00:08:49,830 we're engaging with a variety of partners. 226 00:08:50,289 --> 00:08:52,610 The timeline, I would say, is we're somewhat, 227 00:08:52,610 --> 00:08:55,090 I'd say, in the early stages of doing 228 00:08:55,090 --> 00:08:56,629 a pilot where we've identified 229 00:08:57,764 --> 00:08:59,144 areas within coding, 230 00:09:00,325 --> 00:09:02,485 just the whole revenue cycle management in the 231 00:09:02,485 --> 00:09:04,264 industry is a complicated space. 232 00:09:04,779 --> 00:09:06,079 There is prior authorization 233 00:09:06,539 --> 00:09:08,699 for a set of encounters. There is the 234 00:09:08,699 --> 00:09:11,519 actual coding and billing exercise, and then there's 235 00:09:11,745 --> 00:09:14,065 a subsequent denials and claims exercise. So revenue 236 00:09:14,065 --> 00:09:16,085 cycle management is a complicated area. 237 00:09:16,705 --> 00:09:18,245 Where we are is 238 00:09:18,545 --> 00:09:20,304 we're pushing on each one of those, but 239 00:09:20,304 --> 00:09:22,639 we're at different stages on each one. So 240 00:09:22,639 --> 00:09:24,879 right now, we're currently more focused on the 241 00:09:24,879 --> 00:09:26,899 middle area, which is converting 242 00:09:27,440 --> 00:09:30,080 the a patient chart to the corresponding billing 243 00:09:30,080 --> 00:09:32,194 codes doing and being able to do that 244 00:09:32,194 --> 00:09:33,814 more automatically and more efficiently 245 00:09:34,514 --> 00:09:36,914 than we do today. And that's an area 246 00:09:36,914 --> 00:09:38,434 where we hope to have results within the 247 00:09:38,434 --> 00:09:39,950 next, I would say, 6 to 12 248 00:09:40,429 --> 00:09:42,990 That's really interesting. And I imagine someone who 249 00:09:42,990 --> 00:09:44,429 will be helping you on a lot of 250 00:09:44,429 --> 00:09:45,169 these initiatives 251 00:09:45,549 --> 00:09:46,850 is your new chief 252 00:09:47,309 --> 00:09:47,889 AI officer, 253 00:09:48,625 --> 00:09:50,865 who you actually just announced today here at 254 00:09:50,865 --> 00:09:51,764 the end of July. 255 00:09:52,304 --> 00:09:54,144 You're one of only a handful of health 256 00:09:54,144 --> 00:09:56,324 systems nationwide that now has a position 257 00:09:56,759 --> 00:09:57,899 like this in place. 258 00:09:58,279 --> 00:09:59,639 So can you talk a little bit about 259 00:09:59,639 --> 00:10:01,980 that? What went into that decision? And, 260 00:10:02,600 --> 00:10:04,200 do do you think that all health systems 261 00:10:04,200 --> 00:10:05,740 should be moving in this direction? 262 00:10:06,784 --> 00:10:07,524 So I'll 263 00:10:08,065 --> 00:10:10,304 I think it's important to step back, and 264 00:10:10,304 --> 00:10:12,384 our thinking on this has evolved over time 265 00:10:12,384 --> 00:10:12,964 as well. 266 00:10:13,950 --> 00:10:15,950 Our beliefs have evolved over the last couple 267 00:10:15,950 --> 00:10:18,210 of years to think that both the developments 268 00:10:18,269 --> 00:10:19,730 in technology and AI 269 00:10:20,269 --> 00:10:21,649 are actually accelerating 270 00:10:22,595 --> 00:10:25,654 And the potential for these technologies to transform 271 00:10:25,794 --> 00:10:26,774 entire industries 272 00:10:27,875 --> 00:10:28,934 is remarkable. 273 00:10:30,420 --> 00:10:32,200 You combine that with the 274 00:10:33,700 --> 00:10:35,399 health care as an industry 275 00:10:36,019 --> 00:10:37,480 that is in substantial 276 00:10:38,019 --> 00:10:39,705 need for this transformation. 277 00:10:40,565 --> 00:10:42,164 So sitting in our shoes, I think there's 278 00:10:42,164 --> 00:10:44,725 a unique opportunity to say healthcare as an 279 00:10:44,725 --> 00:10:47,820 industry really needs technology power transformation and the 280 00:10:47,820 --> 00:10:50,399 emerging technologies have remarkable capabilities. 281 00:10:51,340 --> 00:10:52,940 So that's what prompted us to say, how 282 00:10:52,940 --> 00:10:55,100 do we elevate this to a first class 283 00:10:55,100 --> 00:10:55,894 effort for us? 284 00:10:56,615 --> 00:10:58,054 And that's what led us to say that 285 00:10:58,054 --> 00:11:00,455 if it is an important enough area for 286 00:11:00,455 --> 00:11:02,955 us that has the potential to transform healthcare, 287 00:11:03,539 --> 00:11:05,299 then it needs to be something where we 288 00:11:05,299 --> 00:11:06,840 need to make sure that we have 289 00:11:07,299 --> 00:11:08,279 internal expertise, 290 00:11:08,740 --> 00:11:10,774 not just working with external partners, but we 291 00:11:10,774 --> 00:11:12,934 need to have in house expertise that can 292 00:11:12,934 --> 00:11:13,835 help us navigate 293 00:11:14,375 --> 00:11:17,495 these waters as technology evolves and as healthcare 294 00:11:17,495 --> 00:11:18,315 needs evolve. 295 00:11:19,680 --> 00:11:21,680 I think the related question that you so 296 00:11:21,680 --> 00:11:23,600 that's what went into our decision to say, 297 00:11:23,600 --> 00:11:25,940 hey, let's bring on somebody with AI expertise 298 00:11:26,000 --> 00:11:26,899 into the organization. 299 00:11:28,754 --> 00:11:30,355 I think the second part of your question, 300 00:11:30,355 --> 00:11:32,134 should all systems do the same? 301 00:11:33,315 --> 00:11:35,075 The way I would approach it is I 302 00:11:35,075 --> 00:11:38,054 think it is important for every system to 303 00:11:38,240 --> 00:11:41,279 approach AI with the potential for it to 304 00:11:41,279 --> 00:11:42,100 over time 305 00:11:42,720 --> 00:11:43,220 transform 306 00:11:43,920 --> 00:11:45,860 many of their core operations 307 00:11:46,394 --> 00:11:49,034 ranging from clinical to non clinical. And I 308 00:11:49,034 --> 00:11:51,995 think that positioning themselves to navigate those waters 309 00:11:51,995 --> 00:11:52,654 is important. 310 00:11:53,195 --> 00:11:55,600 Do you need a chief AI officer necessarily? 311 00:11:55,740 --> 00:11:58,540 Those organizational constructs may vary from organization to 312 00:11:58,540 --> 00:11:59,040 organization, 313 00:11:59,580 --> 00:12:01,180 but I would say that there needs to 314 00:12:01,180 --> 00:12:03,580 be a process by which organizations are being 315 00:12:03,580 --> 00:12:04,080 intentional 316 00:12:04,995 --> 00:12:06,995 about the areas that are most important to 317 00:12:06,995 --> 00:12:07,495 them, 318 00:12:08,355 --> 00:12:10,995 intersecting them with the potential for technology and 319 00:12:10,995 --> 00:12:11,495 AI, 320 00:12:12,009 --> 00:12:14,350 and engaging with the health tech ecosystem 321 00:12:14,730 --> 00:12:16,990 with that mindset. I think that's an imperative, 322 00:12:17,289 --> 00:12:19,850 a chief AI officer is obviously interesting construct 323 00:12:19,850 --> 00:12:21,605 to go about it. But I think that 324 00:12:21,605 --> 00:12:23,924 every health system should be thinking in those 325 00:12:23,924 --> 00:12:24,424 terms. 326 00:12:25,125 --> 00:12:28,164 Absolutely. That makes complete sense. I I appreciate 327 00:12:28,164 --> 00:12:29,919 you sharing a little bit about your thoughts 328 00:12:30,000 --> 00:12:31,059 on on that topic, 329 00:12:32,000 --> 00:12:34,639 which I imagine will be a big point 330 00:12:34,639 --> 00:12:36,259 of conversation at our upcoming, 331 00:12:37,039 --> 00:12:39,759 health IT event in Chicago in October, which 332 00:12:39,759 --> 00:12:41,685 I know you'll be a speaker at. So, 333 00:12:42,945 --> 00:12:44,384 one thing I did wanna ask you is 334 00:12:44,384 --> 00:12:46,384 if, and I and I imagine this some 335 00:12:46,384 --> 00:12:48,305 some thoughts you'll be sharing similarly at that 336 00:12:48,305 --> 00:12:50,429 event. But is there any advice that you 337 00:12:50,429 --> 00:12:53,570 would give to evolving leaders serving in similar 338 00:12:53,789 --> 00:12:55,250 roles like yours? 339 00:12:55,870 --> 00:12:57,789 What would you share on this podcast while 340 00:12:57,789 --> 00:12:59,394 you have the ears of of a lot 341 00:12:59,394 --> 00:13:01,654 of health system leaders from across the country? 342 00:13:02,115 --> 00:13:02,615 I 343 00:13:03,075 --> 00:13:04,054 think my learning 344 00:13:04,434 --> 00:13:07,014 over the last couple of years is 345 00:13:07,740 --> 00:13:08,799 it's very important 346 00:13:09,500 --> 00:13:10,240 to be 347 00:13:11,259 --> 00:13:11,759 intentional 348 00:13:12,139 --> 00:13:12,639 about 349 00:13:13,179 --> 00:13:15,440 the problems that we are trying to tackle. 350 00:13:16,414 --> 00:13:17,394 While the evolutions 351 00:13:18,014 --> 00:13:20,254 and sort of the developments in technology and 352 00:13:20,254 --> 00:13:22,194 AI are amazing, 353 00:13:22,894 --> 00:13:25,029 eventually they need to be in service of 354 00:13:25,190 --> 00:13:27,290 a healthcare problem or need. 355 00:13:28,389 --> 00:13:30,330 This becomes all the more important 356 00:13:30,710 --> 00:13:31,210 because 357 00:13:31,830 --> 00:13:33,690 technology is just an enabler. 358 00:13:34,294 --> 00:13:37,195 The adoption of technology, the adoption of AI, 359 00:13:37,735 --> 00:13:40,934 the culture, the process, the people, the change 360 00:13:40,934 --> 00:13:43,355 management required to actually drive 361 00:13:44,159 --> 00:13:46,980 change and change in outcomes and the transformation 362 00:13:47,039 --> 00:13:48,579 in healthcare is not easy. 363 00:13:49,120 --> 00:13:51,839 So it's important not to underestimate that adoption 364 00:13:51,839 --> 00:13:52,579 of AI 365 00:13:53,164 --> 00:13:54,764 that it makes sense is not easy. So 366 00:13:54,764 --> 00:13:57,424 it's important to start with a very intentional 367 00:13:57,485 --> 00:13:59,644 mindset that says, hey, these are our key 368 00:13:59,644 --> 00:14:00,144 problems. 369 00:14:00,620 --> 00:14:02,779 This is where technology fits in. And if 370 00:14:02,779 --> 00:14:05,100 we have technology that works, this is how 371 00:14:05,100 --> 00:14:07,919 we're gonna drive the change management, the adoption, 372 00:14:08,059 --> 00:14:10,245 and the process changes that need to go 373 00:14:10,245 --> 00:14:10,745 in 374 00:14:11,365 --> 00:14:11,865 into, 375 00:14:12,644 --> 00:14:14,345 actually helping transform healthcare. 376 00:14:14,804 --> 00:14:16,565 Just as an example, if you look at 377 00:14:16,565 --> 00:14:18,420 the sepsis example that I use where I 378 00:14:18,420 --> 00:14:20,580 said, hey, we're using AI for the prediction 379 00:14:20,580 --> 00:14:21,879 of the onset of sepsis. 380 00:14:22,340 --> 00:14:24,120 That's only part of the story. 381 00:14:24,660 --> 00:14:25,160 Those 382 00:14:25,714 --> 00:14:27,174 technology powered alerts 383 00:14:28,355 --> 00:14:30,774 need to be baked into the clinical workflows 384 00:14:30,834 --> 00:14:32,595 that are used by nurses and are used 385 00:14:32,595 --> 00:14:35,170 by doctors so that they adopt them, they 386 00:14:35,170 --> 00:14:37,649 take action based on them and it drives 387 00:14:37,649 --> 00:14:38,549 the right interventions. 388 00:14:39,250 --> 00:14:41,590 So the process change cannot be underestimated. 389 00:14:42,049 --> 00:14:43,625 So I think those are the 2 aspects, 390 00:14:43,625 --> 00:14:45,545 which is be very intentional about the problem 391 00:14:45,545 --> 00:14:47,465 areas that you're trying to go after and 392 00:14:47,465 --> 00:14:49,384 be very mindful that it's not just about 393 00:14:49,384 --> 00:14:52,250 technology. The change management is a significant lift. 394 00:14:52,889 --> 00:14:55,710 Mhmm. Well, it's it's great and timely advice. 395 00:14:55,850 --> 00:14:58,570 So appreciate you sharing that. Anything else we're 396 00:14:58,570 --> 00:15:01,210 missing? Rohit, any any final thoughts you'd like 397 00:15:01,210 --> 00:15:01,789 to share? 398 00:15:02,714 --> 00:15:04,735 I think the main thing is that, 399 00:15:06,235 --> 00:15:09,134 like the developments in technology are fascinating. 400 00:15:09,809 --> 00:15:11,970 It becomes tricky on how to navigate them. 401 00:15:11,970 --> 00:15:13,490 All of the things that I alluded to, 402 00:15:13,490 --> 00:15:15,329 which is, are you intentional about the problems? 403 00:15:15,329 --> 00:15:16,745 Are you intentional about 404 00:15:17,304 --> 00:15:19,644 the change management? All of this is hard 405 00:15:20,024 --> 00:15:21,565 work. I think at the same time, 406 00:15:22,024 --> 00:15:25,565 the part that fascinates and excites me is 407 00:15:27,610 --> 00:15:29,549 the potential for these technologies 408 00:15:29,929 --> 00:15:32,909 is amazing. One can imagine a future where 409 00:15:34,009 --> 00:15:34,509 the 410 00:15:35,475 --> 00:15:39,394 amazing amount of inefficient back office paperwork that 411 00:15:39,394 --> 00:15:41,095 we do is mostly automated 412 00:15:41,634 --> 00:15:42,134 with 413 00:15:42,470 --> 00:15:44,329 technology, AI, and generative 414 00:15:45,029 --> 00:15:47,269 AI. One can imagine clinical care that is 415 00:15:47,269 --> 00:15:50,490 increasingly powered by a highly intelligent agent. 416 00:15:51,245 --> 00:15:53,004 I don't know the path to get from 417 00:15:53,004 --> 00:15:54,684 here to there and I don't know how 418 00:15:54,684 --> 00:15:57,664 long it will take, but I am convinced 419 00:15:57,884 --> 00:16:00,304 that if you navigate these waters well, 420 00:16:01,870 --> 00:16:03,970 this technology has amazing potential 421 00:16:04,430 --> 00:16:07,170 to transform healthcare and make it 422 00:16:07,629 --> 00:16:08,450 more accessible, 423 00:16:09,455 --> 00:16:09,955 safer, 424 00:16:10,415 --> 00:16:12,915 more scalable, and available to many more patients. 425 00:16:13,215 --> 00:16:15,235 So I'm very intrigued by the potential 426 00:16:15,774 --> 00:16:17,695 and that's a journey that I think they're 427 00:16:17,695 --> 00:16:18,355 all on. 428 00:16:20,070 --> 00:16:23,029 Fantastic. It's exciting stuff. So, doctor Chandra, thank 429 00:16:23,029 --> 00:16:25,190 you for taking the time to sit down 430 00:16:25,190 --> 00:16:27,829 with us and for sharing your insights with 431 00:16:27,829 --> 00:16:28,570 our listeners. 432 00:16:30,134 --> 00:16:31,274 You're welcome, Jacob. 433 00:16:31,735 --> 00:16:33,654 If you'd like to listen to more podcasts 434 00:16:33,654 --> 00:16:35,754 from Becker's Healthcare, you can visit beckershospitalreview.com.