1 00:00:00,640 --> 00:00:03,199 Philips is a health tech leader focused on 2 00:00:03,199 --> 00:00:06,000 innovation that improves the health and well-being of 3 00:00:06,000 --> 00:00:09,460 people. Our health care technology and informatics solutions 4 00:00:09,599 --> 00:00:12,559 help care teams diagnose, treat, and manage more 5 00:00:12,559 --> 00:00:16,244 patients with greater precision, speed, and confidence across 6 00:00:16,244 --> 00:00:19,225 the care journey. With Philips, clinicians are empowered 7 00:00:19,285 --> 00:00:21,765 with streamlined insights in the moments that matter 8 00:00:21,765 --> 00:00:24,904 for every patient. Better care for more people. 9 00:00:25,204 --> 00:00:25,704 Philips. 10 00:00:29,070 --> 00:00:31,309 This is Gracelyn Keller with the Bucharest Healthcare 11 00:00:31,309 --> 00:00:32,909 Podcast, and we are live at the 9th 12 00:00:32,909 --> 00:00:36,210 Annual Health IT Digital Health and RCM Conference. 13 00:00:36,750 --> 00:00:39,950 I am joined by Roberta Schwartz, who serves 14 00:00:39,950 --> 00:00:40,850 as the EVP 15 00:00:41,149 --> 00:00:44,445 of Houston Methodist Hospital, and Sarah Pletcher, who 16 00:00:44,445 --> 00:00:47,004 is the chief digital health officer at Houston 17 00:00:47,004 --> 00:00:49,484 Methodist Hospital. So thank you both for joining 18 00:00:49,484 --> 00:00:51,085 me today, and we'd love to have you 19 00:00:51,085 --> 00:00:53,725 start off by introducing yourselves a little bit 20 00:00:53,725 --> 00:00:54,225 further. 21 00:00:54,924 --> 00:00:57,550 Sure. I'm Roberta Schwartz, and I serve as 22 00:00:57,550 --> 00:01:00,850 executive vice president. I run our large academic 23 00:01:00,909 --> 00:01:01,810 medical center, 24 00:01:02,510 --> 00:01:05,329 in the Texas Medical Center, about a 1,000 25 00:01:05,469 --> 00:01:09,090 bed hospital. And I run our innovation activities 26 00:01:09,230 --> 00:01:11,010 for our 8 hospital system 27 00:01:11,390 --> 00:01:12,530 in Houston, Texas. 28 00:01:13,734 --> 00:01:16,295 And I'm Sarah Fletcher in the chief digital 29 00:01:16,295 --> 00:01:18,855 health officer role. I have things in my 30 00:01:18,855 --> 00:01:21,515 portfolio that include virtual services, 31 00:01:22,375 --> 00:01:24,635 some of our leveraging of remote monitoring, 32 00:01:25,390 --> 00:01:26,210 care redesign, 33 00:01:26,750 --> 00:01:29,810 really lots of innovation efforts throughout the system, 34 00:01:29,870 --> 00:01:32,049 especially those leveraging digital tools. 35 00:01:33,310 --> 00:01:35,790 Wonderful. Well, thanks for joining me. And we'd 36 00:01:35,790 --> 00:01:38,510 love to start our conversation off with AI 37 00:01:38,510 --> 00:01:40,575 adoption. So this is exploding in health care 38 00:01:40,575 --> 00:01:42,655 right now. In your view, what is the 39 00:01:42,655 --> 00:01:45,715 most significant or promising application of this technology, 40 00:01:45,854 --> 00:01:48,594 and how is this informing your organization's innovation 41 00:01:48,734 --> 00:01:49,234 strategy? 42 00:01:50,939 --> 00:01:52,079 I feel like anything 43 00:01:52,540 --> 00:01:54,700 that folks wanna talk about right now or 44 00:01:54,700 --> 00:01:56,780 everything that people wanna talk about is, 45 00:01:57,819 --> 00:02:00,140 ambient listening. So I'm not gonna pick ambient 46 00:02:00,140 --> 00:02:02,540 listening, because I feel like everyone else has 47 00:02:02,540 --> 00:02:04,319 probably talked to you about that already. 48 00:02:04,939 --> 00:02:06,640 So I love the 49 00:02:06,984 --> 00:02:09,245 work that we're doing in the audio 50 00:02:10,185 --> 00:02:11,965 and video environment 51 00:02:12,425 --> 00:02:13,884 where we are 52 00:02:14,264 --> 00:02:14,664 both, 53 00:02:15,064 --> 00:02:15,564 listening 54 00:02:15,944 --> 00:02:16,925 and watching 55 00:02:17,384 --> 00:02:18,125 various events 56 00:02:19,550 --> 00:02:22,129 and then using the video 57 00:02:22,430 --> 00:02:23,490 or the information, 58 00:02:23,950 --> 00:02:26,689 the artificial intelligence that comes off of that 59 00:02:26,750 --> 00:02:28,770 to help us become more efficient 60 00:02:29,550 --> 00:02:30,450 and or 61 00:02:30,909 --> 00:02:31,409 watch 62 00:02:31,710 --> 00:02:32,210 various 63 00:02:32,510 --> 00:02:34,129 activities that are happening 64 00:02:34,974 --> 00:02:37,555 around our patients or with our patients. 65 00:02:38,735 --> 00:02:40,354 And I think at a high level, 66 00:02:41,215 --> 00:02:42,194 it's anything 67 00:02:42,735 --> 00:02:45,555 where we're getting that huge payout in 68 00:02:46,014 --> 00:02:47,155 managing complexity 69 00:02:47,614 --> 00:02:49,074 and mountains of information 70 00:02:49,750 --> 00:02:52,550 and giving time back, whether it's time to 71 00:02:52,550 --> 00:02:55,030 the patients, whether it's time to the care 72 00:02:55,030 --> 00:02:57,269 team, whether it's time to the burned out 73 00:02:57,269 --> 00:02:57,769 physician. 74 00:02:58,629 --> 00:03:01,050 The good news is there's so many opportunities 75 00:03:01,269 --> 00:03:02,169 within AI, 76 00:03:02,629 --> 00:03:05,334 to really maximize both of those fronts. And 77 00:03:05,334 --> 00:03:07,134 I think that that's really what we're looking 78 00:03:07,134 --> 00:03:09,555 for when we're choosing our strategic portfolio 79 00:03:09,935 --> 00:03:12,014 is how can we stay on top of 80 00:03:12,014 --> 00:03:13,394 this immense complexity, 81 00:03:14,174 --> 00:03:15,534 and then do so in a way that 82 00:03:15,534 --> 00:03:18,094 provides a better experience for our patients in 83 00:03:18,094 --> 00:03:18,995 our care genes. 84 00:03:19,919 --> 00:03:21,919 The other thing that I really like is, 85 00:03:22,239 --> 00:03:24,419 the work we're doing around predictive analytics, 86 00:03:25,199 --> 00:03:27,280 where we're able to actually take now this 87 00:03:27,280 --> 00:03:29,859 big amount of data, this enormous, 88 00:03:30,959 --> 00:03:31,459 plethora 89 00:03:31,805 --> 00:03:34,385 of things that happen every minute, every second, 90 00:03:34,764 --> 00:03:36,305 every moment, every medication, 91 00:03:37,245 --> 00:03:40,305 every vital sign, and actually using that information 92 00:03:40,365 --> 00:03:43,165 to predict whether or not our patients are 93 00:03:43,165 --> 00:03:45,105 gonna have a fall, gonna have a readmission, 94 00:03:46,069 --> 00:03:48,229 going to need a next level of care 95 00:03:48,229 --> 00:03:50,889 in a particular location that's gonna help them 96 00:03:51,030 --> 00:03:52,250 and using the information 97 00:03:52,870 --> 00:03:53,930 to guide us 98 00:03:54,469 --> 00:03:57,289 rather than just judgment. Because, again, you get 99 00:03:57,349 --> 00:03:59,689 so much information that comes in 100 00:04:00,125 --> 00:04:00,625 on 101 00:04:01,004 --> 00:04:02,224 a, momentary 102 00:04:02,525 --> 00:04:03,025 basis 103 00:04:03,644 --> 00:04:05,965 that it's it's hard for our physicians to 104 00:04:05,965 --> 00:04:07,264 keep up with the, 105 00:04:07,965 --> 00:04:10,284 bazillion things that are in the medical record, 106 00:04:10,284 --> 00:04:13,180 both from today's visit, but also in the 107 00:04:13,180 --> 00:04:13,680 hundreds 108 00:04:14,300 --> 00:04:16,620 of pages of medical records that have happened 109 00:04:16,620 --> 00:04:18,960 in the last month, year, and 10 years. 110 00:04:19,420 --> 00:04:21,020 Yeah. I mean, that's the reality. Right? There's 111 00:04:21,020 --> 00:04:23,580 so much already there. So the spinning straw 112 00:04:23,580 --> 00:04:25,585 in the gold is really the promise of 113 00:04:25,585 --> 00:04:27,185 a lot of these tools, to take what's 114 00:04:27,185 --> 00:04:29,764 already in there, but just curate it, 115 00:04:30,064 --> 00:04:32,464 create insights, and put it in the right 116 00:04:32,464 --> 00:04:34,004 hands at the right time, 117 00:04:34,464 --> 00:04:36,785 with the right human in the loop, in 118 00:04:36,785 --> 00:04:38,785 order to really leverage the power of what 119 00:04:38,785 --> 00:04:39,845 we already have. 120 00:04:41,009 --> 00:04:43,189 And so switching gears just slightly, 121 00:04:43,490 --> 00:04:44,949 I would love to kind of 122 00:04:45,490 --> 00:04:48,389 talk about data. So healthcare leaders are managing 123 00:04:48,449 --> 00:04:51,889 greater volumes of data and more devices across 124 00:04:51,889 --> 00:04:53,589 a growing number of care settings. 125 00:04:54,064 --> 00:04:57,125 And in this complex environment, what clinical integration 126 00:04:57,264 --> 00:04:59,764 tools or practices are you seeing drive improvements 127 00:04:59,824 --> 00:05:00,884 in patient outcomes? 128 00:05:01,264 --> 00:05:03,024 And I'd love for you to share specific 129 00:05:03,024 --> 00:05:04,164 examples of these. 130 00:05:05,264 --> 00:05:07,504 We're doing, work with one of your companies 131 00:05:07,504 --> 00:05:08,805 here, health data analytics, 132 00:05:09,639 --> 00:05:10,139 HDAI, 133 00:05:11,240 --> 00:05:12,060 that is 134 00:05:12,680 --> 00:05:14,860 working it pulls in all of our data. 135 00:05:15,160 --> 00:05:17,420 It then twins it against, 136 00:05:17,879 --> 00:05:20,439 like, 20 to 30 years of Medicare records 137 00:05:20,439 --> 00:05:23,165 as well as all payer claims data and 138 00:05:23,165 --> 00:05:25,024 then uses that information 139 00:05:25,805 --> 00:05:27,745 to actually give a, 140 00:05:28,524 --> 00:05:30,685 set of scores to our doctors, likelihood of 141 00:05:30,685 --> 00:05:33,185 mortality, likelihood of being readmitted, likelihood 142 00:05:33,564 --> 00:05:34,064 of, 143 00:05:34,524 --> 00:05:36,225 having long lengths of stay. 144 00:05:36,580 --> 00:05:38,660 It gives them insight about things that are 145 00:05:38,660 --> 00:05:40,740 in the medical record that may or may 146 00:05:40,740 --> 00:05:42,199 not have been missed. Or, 147 00:05:42,660 --> 00:05:44,819 in patients like this, you may not be 148 00:05:44,819 --> 00:05:46,199 looking for these conditions. 149 00:05:46,819 --> 00:05:49,139 In that case, it serves that information up 150 00:05:49,139 --> 00:05:50,040 to our clinicians 151 00:05:50,500 --> 00:05:51,000 and 152 00:05:51,335 --> 00:05:53,895 basically provides it to them in a a 153 00:05:53,975 --> 00:05:57,495 almost a dashboard like reporting and says, hey. 154 00:05:57,495 --> 00:05:59,175 These are things you may wanna keep an 155 00:05:59,175 --> 00:06:01,194 eye out for. It's provided 156 00:06:01,495 --> 00:06:03,355 huge amounts of our doctor 157 00:06:03,990 --> 00:06:05,610 information to our doctors presurgery 158 00:06:06,389 --> 00:06:08,629 to get these patients prepared and talk about 159 00:06:08,629 --> 00:06:10,389 what they need to do to optimize these 160 00:06:10,389 --> 00:06:12,629 patients and or whether or not it's really 161 00:06:12,629 --> 00:06:14,470 a good idea to do surgery on these 162 00:06:14,470 --> 00:06:16,089 patients. Can they survive it? 163 00:06:16,915 --> 00:06:18,115 Yeah. And I think and, I mean, there's 164 00:06:18,115 --> 00:06:20,595 so many examples. Another one is we have 165 00:06:20,595 --> 00:06:22,995 a wearable device that all patients wear when 166 00:06:22,995 --> 00:06:24,855 they come in the hospital, and it's collecting 167 00:06:24,915 --> 00:06:28,035 high frequency vital sign data. But rather than 168 00:06:28,035 --> 00:06:30,595 just deliver this whole new mountain of data, 169 00:06:30,595 --> 00:06:33,879 it's at it's applying algorithms and protocols against 170 00:06:33,879 --> 00:06:36,060 that data and leveraging a central team 171 00:06:36,439 --> 00:06:38,699 so that we're integrating that insight, 172 00:06:39,240 --> 00:06:42,540 across our system to, again, allow better care, 173 00:06:43,080 --> 00:06:45,814 but actually less burdensome time on the care 174 00:06:45,814 --> 00:06:47,514 teams trying to provide that care. 175 00:06:47,894 --> 00:06:50,694 We're also leveraging cameras in our operating rooms 176 00:06:50,694 --> 00:06:53,254 and in our inpatient rooms that have AI 177 00:06:53,254 --> 00:06:55,914 capabilities that, again, allow us to really consider 178 00:06:56,214 --> 00:06:57,115 data around 179 00:06:57,495 --> 00:06:57,995 throughput 180 00:06:58,370 --> 00:06:59,029 and efficiency 181 00:06:59,409 --> 00:06:59,909 and, 182 00:07:00,449 --> 00:07:02,529 corridors of care to be able to drive 183 00:07:02,529 --> 00:07:05,409 improvements in, our hospital and in our operating 184 00:07:05,409 --> 00:07:06,149 room experience. 185 00:07:07,569 --> 00:07:10,529 And with greater volumes of data and AI 186 00:07:10,529 --> 00:07:12,550 adoptions, our IT teams 187 00:07:12,875 --> 00:07:14,795 have become a lot more critical. So I 188 00:07:14,795 --> 00:07:16,715 would love to hear how you believe that 189 00:07:16,715 --> 00:07:19,595 healthcare organizations can better support both IT and 190 00:07:19,595 --> 00:07:22,634 clinical teams as they carry out innovation efforts. 191 00:07:22,634 --> 00:07:23,915 And what do you see as the most 192 00:07:23,915 --> 00:07:25,295 common pitfalls here? 193 00:07:26,339 --> 00:07:28,339 Yeah. I think we are certainly one of 194 00:07:28,339 --> 00:07:30,259 the new groups of people that are burdening 195 00:07:30,259 --> 00:07:30,759 IT, 196 00:07:31,379 --> 00:07:34,020 and asking a lot of them. And we're 197 00:07:34,020 --> 00:07:36,339 asking a lot of them because now as 198 00:07:36,339 --> 00:07:37,240 we put in 199 00:07:37,540 --> 00:07:40,435 4,000 cameras into our infrastructure, 200 00:07:41,055 --> 00:07:42,995 building out this Internet of things, 201 00:07:43,375 --> 00:07:45,535 in both the patient care rooms and or 202 00:07:45,535 --> 00:07:47,394 sending the patients home with them, 203 00:07:48,175 --> 00:07:50,175 we're doing a lot. And the in what 204 00:07:50,175 --> 00:07:52,675 we found is the infrastructure of our organization 205 00:07:53,055 --> 00:07:53,954 had to change 206 00:07:54,350 --> 00:07:56,430 from one that was kind of an on 207 00:07:56,430 --> 00:07:56,930 prem, 208 00:07:57,470 --> 00:08:00,509 you know, boxes in a data center to 209 00:08:00,509 --> 00:08:01,949 one that was really 210 00:08:02,430 --> 00:08:02,930 would 211 00:08:03,470 --> 00:08:05,250 have the ability to to have 212 00:08:05,709 --> 00:08:07,569 multiple points of use, 213 00:08:08,384 --> 00:08:11,024 both in the hospital environment and home, and 214 00:08:11,024 --> 00:08:12,165 that's that's a challenge. 215 00:08:13,345 --> 00:08:15,345 I think one of the pitfalls is is 216 00:08:15,345 --> 00:08:18,725 siloing and also sort of managing new complexities 217 00:08:19,185 --> 00:08:20,085 of contracts 218 00:08:20,399 --> 00:08:21,540 and data management 219 00:08:21,920 --> 00:08:24,899 and technologies that some folks haven't seen before. 220 00:08:25,279 --> 00:08:27,519 And so really to try to improve that, 221 00:08:27,920 --> 00:08:29,220 recruiting and fostering 222 00:08:29,680 --> 00:08:32,820 people and a culture where your IT leaders 223 00:08:33,279 --> 00:08:35,940 get genuinely excited about the clinical 224 00:08:36,264 --> 00:08:38,664 side and improving the care experience. And you 225 00:08:38,664 --> 00:08:41,625 have clinical leaders who are genuinely interested and 226 00:08:41,625 --> 00:08:44,024 willing to learn about IT where they can 227 00:08:44,024 --> 00:08:46,345 kinda come together and maybe not be fluent 228 00:08:46,345 --> 00:08:47,644 in one another's language, 229 00:08:47,945 --> 00:08:49,669 but at least make an effort to be 230 00:08:49,669 --> 00:08:50,169 conversational 231 00:08:50,629 --> 00:08:52,789 in one another's language to really help you 232 00:08:52,789 --> 00:08:53,289 integrate, 233 00:08:53,750 --> 00:08:55,209 across the teams because 234 00:08:55,669 --> 00:08:58,309 Roberta's right. The the complexity of these of 235 00:08:58,309 --> 00:09:00,389 these new endeavors is great. So you need 236 00:09:00,389 --> 00:09:02,169 you need all the people in the village 237 00:09:02,475 --> 00:09:04,634 leaning in to try to solve a common 238 00:09:04,634 --> 00:09:05,134 problem. 239 00:09:06,154 --> 00:09:07,695 And as we wrap up our conversation, 240 00:09:08,075 --> 00:09:09,434 I would love to hear your top piece 241 00:09:09,434 --> 00:09:11,514 of advice for health care leaders as they 242 00:09:11,514 --> 00:09:14,554 prepare for further advancements in technology and greater 243 00:09:14,554 --> 00:09:15,695 demands for care. 244 00:09:16,509 --> 00:09:18,350 You know, our health care leaders and our 245 00:09:18,350 --> 00:09:20,990 innovation leaders as well as IT need to 246 00:09:20,990 --> 00:09:22,690 be bold and not back down. 247 00:09:23,149 --> 00:09:25,070 And if we all kind of are out 248 00:09:25,070 --> 00:09:27,389 there being bold and not backing down when 249 00:09:27,389 --> 00:09:28,529 it gets difficult, 250 00:09:29,004 --> 00:09:31,985 then we will push through and drive transformation 251 00:09:32,204 --> 00:09:33,105 in our industry. 252 00:09:33,964 --> 00:09:35,404 And I would just say beware of the 253 00:09:35,404 --> 00:09:37,884 pilot. It's okay to start small, but you 254 00:09:37,884 --> 00:09:39,884 really do need to be dreaming and planning 255 00:09:39,884 --> 00:09:40,384 big, 256 00:09:40,860 --> 00:09:42,480 in order to move beyond, 257 00:09:43,019 --> 00:09:45,279 the death of a 1,000 paper cut pilots. 258 00:09:46,379 --> 00:09:48,539 Wonderful. Well, Roberta and Sarah, thanks so much 259 00:09:48,539 --> 00:09:50,460 for joining me today on the Becker's Healthcare 260 00:09:50,460 --> 00:09:52,460 Podcast. Again, we're live at the 9th Annual 261 00:09:52,460 --> 00:09:55,179 Health IT Digital Health and RCM Conference. Thank 262 00:09:55,179 --> 00:09:55,679 you.