1 00:00:00,754 --> 00:00:02,899 This is Laura Diet with the Becker healthcare 2 00:00:02,899 --> 00:00:04,885 podcast, I'm thrilled today to be joined by 3 00:00:04,965 --> 00:00:07,602 Doctor. Christopher Long chief Medical Ops. Or Chief 4 00:00:07,762 --> 00:00:09,752 Digital Officer an associate Dean as well as 5 00:00:09,752 --> 00:00:12,140 clinical professor at Uc San Diego Health. Doctor. 6 00:00:12,459 --> 00:00:13,573 Long hers is a pleasure to have you 7 00:00:13,573 --> 00:00:14,529 on the podcast today. 8 00:00:15,325 --> 00:00:17,095 Sir Oh, thank you so much for having 9 00:00:17,095 --> 00:00:17,254 me. 10 00:00:18,292 --> 00:00:19,887 Now I'm really looking forward to our discussion. 11 00:00:20,047 --> 00:00:21,643 I know there's a lot happening at Uc 12 00:00:21,722 --> 00:00:24,232 San Diego Hal having to do with technology, 13 00:00:24,848 --> 00:00:27,640 Ai, and really some revolutionary things on the 14 00:00:27,640 --> 00:00:29,873 clinical care side. But before we dive into 15 00:00:29,873 --> 00:00:31,548 that discussion, can talk a little bit more 16 00:00:31,548 --> 00:00:32,585 about yourself and your background? 17 00:00:33,638 --> 00:00:33,878 Sure. 18 00:00:34,917 --> 00:00:37,633 I've been involved in healthcare administration leadership for 19 00:00:37,633 --> 00:00:40,690 about 20 years. I finished my pediatric training 20 00:00:41,643 --> 00:00:44,359 Packard Children's Hospital at Stanford. And joined what 21 00:00:44,439 --> 00:00:45,478 I thought was going to be a 2 22 00:00:45,478 --> 00:00:47,850 year project to implement electronic health record at 23 00:00:48,048 --> 00:00:50,513 and, of course, that turned into a much 24 00:00:50,513 --> 00:00:53,297 longer and more involved project. It was a 25 00:00:53,297 --> 00:00:55,467 lot of fun because I had a background 26 00:00:55,523 --> 00:00:58,722 that helped perfectly prepare me. Which is I 27 00:00:58,722 --> 00:01:00,478 had taken time off during medical school to 28 00:01:00,478 --> 00:01:02,394 do a master's degree in healthcare care informatics. 29 00:01:03,192 --> 00:01:05,586 And so after implementation of the electronic health 30 00:01:05,586 --> 00:01:08,787 record there, we found some really important outcomes 31 00:01:08,787 --> 00:01:10,799 associated with reduction mortality 32 00:01:11,333 --> 00:01:13,935 when we implemented our physician order entry and 33 00:01:14,295 --> 00:01:15,034 I subsequently 34 00:01:15,655 --> 00:01:18,075 ended up as the Chief Medical Information Officer 35 00:01:18,854 --> 00:01:21,415 founded a fellowship program and clinical informatics at 36 00:01:21,655 --> 00:01:21,974 Stanford. 37 00:01:22,708 --> 00:01:24,613 I spent 15 years there 38 00:01:25,010 --> 00:01:26,757 in total before I came down here at 39 00:01:26,836 --> 00:01:29,797 Uc San Diego about 9 years ago. And 40 00:01:30,821 --> 00:01:32,490 Initially came down to Uc San Diego to 41 00:01:32,490 --> 00:01:34,420 serve as the Cio for the health system 42 00:01:34,715 --> 00:01:36,622 and the professional schools, including the school of 43 00:01:36,781 --> 00:01:37,281 Medicine 44 00:01:38,228 --> 00:01:39,825 But after a few years, I stepped into 45 00:01:39,825 --> 00:01:42,300 an additional role as the associate Chief Medical 46 00:01:42,459 --> 00:01:45,833 Officer hoping to lead our quality and safety 47 00:01:45,972 --> 00:01:48,376 in partnership with Our. Quality and patient safety 48 00:01:48,376 --> 00:01:48,773 officer. 49 00:01:49,647 --> 00:01:52,110 We learned a lot in that, particularly around 50 00:01:52,110 --> 00:01:55,129 our public data reporting and local quality improvement 51 00:01:55,129 --> 00:01:57,455 initiatives, with And I continue to chair the 52 00:01:57,455 --> 00:01:59,528 quality council to this day, which has been 53 00:01:59,528 --> 00:02:01,203 a really gratifying part of my role, 54 00:02:02,320 --> 00:02:02,820 ultimately 55 00:02:03,437 --> 00:02:05,805 ended up in the Chief Medical Officer role 56 00:02:06,003 --> 00:02:06,503 and 57 00:02:07,912 --> 00:02:08,412 been 58 00:02:08,866 --> 00:02:12,047 continuing to help lead enterprise initiatives and we're 59 00:02:12,047 --> 00:02:14,830 now expanding as we acquire new hospitals and 60 00:02:14,830 --> 00:02:15,759 affiliate with others 61 00:02:16,276 --> 00:02:16,753 and 62 00:02:17,150 --> 00:02:18,444 have actually just hired 63 00:02:18,818 --> 00:02:21,123 Chief Medical Officer for Hill crest La jolla, 64 00:02:21,441 --> 00:02:23,983 as well as for our East campus, which 65 00:02:23,983 --> 00:02:25,827 used to be the Prime auto hospital. 66 00:02:26,383 --> 00:02:27,974 And so it's been a lot of fun 67 00:02:27,974 --> 00:02:31,393 to experience period of huge clinical growth under 68 00:02:31,393 --> 00:02:33,381 the leadership of our Ceo, Patty Mason. 69 00:02:35,543 --> 00:02:37,532 That's great to hear. And you know, really 70 00:02:37,532 --> 00:02:39,918 exciting to think about how technology has evolved 71 00:02:39,918 --> 00:02:42,241 in the health space from coming in to 72 00:02:42,241 --> 00:02:45,507 help implement a Ehr system to then completely 73 00:02:45,507 --> 00:02:48,215 leading a, you know, a It digital initiatives, 74 00:02:48,613 --> 00:02:51,016 it is really a cool career trajectory, from 75 00:02:51,016 --> 00:02:52,762 your perspective, what are some of the biggest 76 00:02:52,762 --> 00:02:54,665 issues that you're following in health care for 77 00:02:54,665 --> 00:02:55,459 20 24? 78 00:02:58,014 --> 00:03:00,086 Sure. There's a lot going on in health. 79 00:03:00,325 --> 00:03:00,804 And 80 00:03:02,079 --> 00:03:04,630 like, any large portion of the economy, I 81 00:03:04,630 --> 00:03:05,746 think it starts with finance. 82 00:03:06,399 --> 00:03:09,770 That we've seen things like Medicare advantage 83 00:03:10,147 --> 00:03:11,423 plans going belly up, 84 00:03:12,539 --> 00:03:13,837 our colleagues 85 00:03:14,373 --> 00:03:15,544 at Scripps health 86 00:03:16,139 --> 00:03:16,639 recently 87 00:03:17,169 --> 00:03:17,669 discontinued 88 00:03:18,279 --> 00:03:21,132 support of their Medicare Advantage plan. We've seen 89 00:03:21,132 --> 00:03:22,821 a bunch more patients 90 00:03:23,129 --> 00:03:25,921 And these are older sicker patients that are 91 00:03:25,921 --> 00:03:27,995 really hard to manage from a risk standpoint. 92 00:03:28,234 --> 00:03:28,814 And so 93 00:03:30,148 --> 00:03:32,461 these type of financial questions are particularly difficult. 94 00:03:33,114 --> 00:03:35,268 In the setting of the much larger trends. 95 00:03:35,507 --> 00:03:36,007 So 96 00:03:36,544 --> 00:03:39,734 we've seen, of course, inflation affecting supply cost 97 00:03:39,734 --> 00:03:41,888 by 10 percent to 12 percent particularly here 98 00:03:41,888 --> 00:03:42,627 in California, 99 00:03:43,099 --> 00:03:46,833 We have strong labor presence and we've seen 100 00:03:46,833 --> 00:03:49,558 labor costs increased 10 percent to 15 percent 101 00:03:49,773 --> 00:03:51,362 and of course all the while. 102 00:03:51,934 --> 00:03:54,811 Our reimbursement is not going up 10 or 103 00:03:54,811 --> 00:03:55,370 15 percent. 104 00:03:56,090 --> 00:03:57,368 It might be going up 2 or 3 105 00:03:57,368 --> 00:04:00,245 percent or 4 percent and that's only for 106 00:04:00,245 --> 00:04:02,896 the commercially insured patients, you know, we still 107 00:04:02,896 --> 00:04:03,216 have 108 00:04:03,855 --> 00:04:06,572 pretty fixed reimbursement in our government plans. And 109 00:04:06,572 --> 00:04:08,010 so taken all together, 110 00:04:08,662 --> 00:04:10,729 it's a really challenging time from a financial 111 00:04:10,729 --> 00:04:13,352 standpoint and forces us all to really focus 112 00:04:13,352 --> 00:04:16,467 on value and what investments we're making that 113 00:04:16,467 --> 00:04:18,452 most directly benefit the patients that we serve. 114 00:04:20,200 --> 00:04:22,265 That's such a great point. And know interesting 115 00:04:22,265 --> 00:04:23,774 they bring up some of the different health 116 00:04:23,774 --> 00:04:27,287 plans in the landscape of working with payers 117 00:04:27,287 --> 00:04:28,185 as well as 118 00:04:28,559 --> 00:04:30,468 thinking through how patients were able to access 119 00:04:30,468 --> 00:04:32,694 and afford care, and then bringing that into 120 00:04:32,694 --> 00:04:34,228 the broader strategy of 121 00:04:34,699 --> 00:04:36,693 how you're caring for patients and thinking about 122 00:04:36,693 --> 00:04:39,804 those patient populations. So, it's really interesting to 123 00:04:39,804 --> 00:04:42,611 hear from your perspective and when taking that 124 00:04:42,611 --> 00:04:44,683 into consideration and and looking ahead, what are 125 00:04:44,683 --> 00:04:46,358 you most excited about for the future and 126 00:04:46,358 --> 00:04:47,155 what makes you nervous? 127 00:04:50,199 --> 00:04:53,889 Well, I am extraordinarily excited about the digital 128 00:04:54,027 --> 00:04:54,926 transformation of health 129 00:04:55,383 --> 00:04:57,776 and not just Ai, but that's certainly the 130 00:04:57,776 --> 00:04:59,291 latest in a suite of tools. 131 00:05:00,103 --> 00:05:04,088 For example, in our managed population, we run 132 00:05:04,088 --> 00:05:06,979 a large Medicare shared savings plan accountable care 133 00:05:07,117 --> 00:05:09,997 organization. And we started in that population doing 134 00:05:09,997 --> 00:05:11,053 remote patient monitoring 135 00:05:11,906 --> 00:05:15,423 and actually have over 3000 patients now. With 136 00:05:15,423 --> 00:05:19,171 devices at home, transmitting blood pressure and glucose 137 00:05:19,171 --> 00:05:20,947 ratings and our patients with diabetes 138 00:05:21,882 --> 00:05:22,382 and 139 00:05:22,839 --> 00:05:23,817 those type of 140 00:05:24,527 --> 00:05:26,357 remote patient digital kind of tools. 141 00:05:26,993 --> 00:05:28,823 And that's allowed us to better manage population. 142 00:05:28,982 --> 00:05:31,185 We actually have data showing a reduction 143 00:05:31,624 --> 00:05:34,740 blood pressure in our hypertensive patients, a reduction 144 00:05:34,740 --> 00:05:36,838 hemoglobin A1c of our patients with diabetes. 145 00:05:37,537 --> 00:05:39,635 And the kind of care 146 00:05:40,187 --> 00:05:42,099 allows us to not only drive better outcomes 147 00:05:42,099 --> 00:05:45,045 and reduce hospital emissions, but also save cost 148 00:05:45,045 --> 00:05:47,116 to system overall and keep people healthy, keep 149 00:05:47,116 --> 00:05:49,360 people out of the hospital So that's really 150 00:05:49,360 --> 00:05:51,586 exciting from my standpoint because it's not something 151 00:05:51,586 --> 00:05:53,017 it would have been possible 10 or 20 152 00:05:53,017 --> 00:05:53,494 years ago. 153 00:05:54,369 --> 00:05:56,357 On top of that, you layer in some 154 00:05:56,357 --> 00:05:59,000 of these new Ai tools such as large 155 00:05:59,000 --> 00:05:59,899 language models 156 00:06:00,199 --> 00:06:03,399 or even just traditional machine learning. And we're 157 00:06:03,399 --> 00:06:04,540 finding all sorts of 158 00:06:04,920 --> 00:06:07,729 outstanding outcomes in the clinical side we found 159 00:06:07,729 --> 00:06:10,509 earlier detection of Sepsis resulting in 20 percent 160 00:06:10,509 --> 00:06:13,630 drop of mortality in our emergency department patients 161 00:06:13,766 --> 00:06:15,219 with Sepsis, which is 162 00:06:15,609 --> 00:06:16,406 really, really huge. 163 00:06:17,284 --> 00:06:17,784 And 164 00:06:18,161 --> 00:06:20,793 translates to 50 lives saves on annual basis 165 00:06:20,793 --> 00:06:22,149 just here at Uc San Diego. 166 00:06:22,787 --> 00:06:25,361 Imagine if that, you know, translated across the 167 00:06:25,832 --> 00:06:26,708 300000 168 00:06:26,708 --> 00:06:28,698 lives lost every year across America. 169 00:06:29,892 --> 00:06:32,996 Similarly, we've leveraged large language models for things 170 00:06:32,996 --> 00:06:36,034 like Ai generated draft messages to respond to 171 00:06:36,034 --> 00:06:38,341 patients on the portal. And we found that 172 00:06:38,341 --> 00:06:41,942 that decreases is cognitive burden decreases physician burnout 173 00:06:42,173 --> 00:06:43,787 and results in increased satisfaction. 174 00:06:44,560 --> 00:06:47,662 So lots of promising opportunities, and that's just 175 00:06:47,662 --> 00:06:49,988 on the clinical side. Think about the business 176 00:06:49,988 --> 00:06:53,024 side and there's certainly huge opportunities to help 177 00:06:53,024 --> 00:06:55,362 make our revenue cycle more efficient so that 178 00:06:55,661 --> 00:06:56,320 as we 179 00:06:57,031 --> 00:06:59,734 acquire and affiliate with new hospitals, we can 180 00:06:59,734 --> 00:07:00,711 expand our services 181 00:07:01,165 --> 00:07:03,811 without having to, you know, add as many 182 00:07:04,106 --> 00:07:05,458 people as we would otherwise. 183 00:07:06,189 --> 00:07:09,938 And that efficiency also translates to better employee 184 00:07:09,938 --> 00:07:10,438 satisfaction. 185 00:07:11,054 --> 00:07:13,128 You know, we know that some of the 186 00:07:13,128 --> 00:07:15,622 wrote tasks that can be done by Ai 187 00:07:16,569 --> 00:07:18,314 you know, when we automate that that makes 188 00:07:18,314 --> 00:07:20,931 our employees happier as well. So that that 189 00:07:20,931 --> 00:07:23,334 leads to increased retention, increased 190 00:07:24,039 --> 00:07:25,737 satisfaction at work and 191 00:07:26,514 --> 00:07:28,270 overall sort of better healthcare care experience. 192 00:07:29,707 --> 00:07:31,144 That's been fantastic to hear. And you know, 193 00:07:31,544 --> 00:07:34,831 especially thinking about Ai in artificial intelligence, 194 00:07:35,548 --> 00:07:36,825 in in some of the ways that as 195 00:07:36,825 --> 00:07:39,058 you said, you can really help people have 196 00:07:39,058 --> 00:07:40,095 a better work experience. 197 00:07:40,892 --> 00:07:43,307 And at the same time, you know, help 198 00:07:43,779 --> 00:07:45,533 become more efficient overall, I think that's really 199 00:07:45,533 --> 00:07:46,410 cool and beneficial. 200 00:07:47,208 --> 00:07:49,600 I I know, looking ahead to... Do you 201 00:07:49,600 --> 00:07:52,870 see that will make health leaders more effective 202 00:07:52,870 --> 00:07:54,483 and successful over the next 2 to 3 203 00:07:54,483 --> 00:07:56,401 years? What do they need to know, especially 204 00:07:56,401 --> 00:07:57,280 given the, 205 00:07:57,759 --> 00:08:00,317 evolving health landscape and in tools that are 206 00:08:00,317 --> 00:08:01,595 gonna become available to them? 207 00:08:02,409 --> 00:08:03,287 That's a great question. 208 00:08:04,883 --> 00:08:07,777 I think the skills from a leadership standpoint 209 00:08:08,474 --> 00:08:11,121 don't change much over time. And building a 210 00:08:11,121 --> 00:08:13,216 high functioning teams, teams that are 211 00:08:13,593 --> 00:08:16,485 diverse and work well together, I think as 212 00:08:16,543 --> 00:08:18,877 a skill that will continue to be important 213 00:08:19,348 --> 00:08:21,652 I I think that as Ai begins to 214 00:08:21,652 --> 00:08:23,876 really creep into every corner of health care 215 00:08:23,876 --> 00:08:26,974 delivery, having a superficial understanding of how these 216 00:08:26,974 --> 00:08:27,451 tools work. 217 00:08:28,343 --> 00:08:30,338 Is actually important for some of our executive 218 00:08:30,338 --> 00:08:30,657 leaders. 219 00:08:31,216 --> 00:08:33,530 And I recently wrote an opinion with Dr. 220 00:08:33,769 --> 00:08:36,095 Bob Walked and others about by the role 221 00:08:36,095 --> 00:08:37,761 of the Chief Health Ai officer, 222 00:08:38,237 --> 00:08:41,094 and we're fortunate to recruit. Doctor Car duke 223 00:08:41,173 --> 00:08:43,236 Singh that serve as our Chief Health Ai 224 00:08:43,236 --> 00:08:43,633 officer, 225 00:08:44,124 --> 00:08:46,111 here at Uc San Diego Health. And I 226 00:08:46,111 --> 00:08:48,338 think it's just a fantastic and important role 227 00:08:48,338 --> 00:08:50,110 in moving forward to 228 00:08:50,802 --> 00:08:53,108 identify where the opportunities are to leverage these 229 00:08:53,108 --> 00:08:56,561 tools to drive the, clinical and business outcomes 230 00:08:56,696 --> 00:08:59,736 that matter to our patients and to the 231 00:08:59,871 --> 00:09:02,252 organization. And so that's 1 area that I 232 00:09:02,252 --> 00:09:04,923 think investment is really needed. And 233 00:09:05,461 --> 00:09:07,539 the thing that makes me nervous is not 234 00:09:07,539 --> 00:09:09,936 about sort of bad tools it's about tools 235 00:09:09,936 --> 00:09:12,269 that are poorly implemented with And so having 236 00:09:12,269 --> 00:09:14,990 those local Ai governance and review processes, I 237 00:09:14,990 --> 00:09:17,230 think are gonna be really, really important to 238 00:09:17,230 --> 00:09:19,470 making sure that we drive the outcomes we 239 00:09:19,470 --> 00:09:20,430 want and do not... 240 00:09:21,241 --> 00:09:23,572 Inadvertently introduce unintended consequences. 241 00:09:24,982 --> 00:09:26,256 That's touch a great point. Do you bring 242 00:09:26,256 --> 00:09:28,882 up especially on the governance side. I know 243 00:09:28,882 --> 00:09:31,467 things are changing quickly? So for health systems 244 00:09:31,523 --> 00:09:33,592 that are really at their front stages of 245 00:09:33,592 --> 00:09:35,421 of trying to implement Ai or figure out 246 00:09:35,421 --> 00:09:37,092 what their strategy is. Where do you start? 247 00:09:37,251 --> 00:09:40,447 What is most important for health organizations to 248 00:09:40,447 --> 00:09:42,594 tackle so that they're not putting themselves to 249 00:09:42,594 --> 00:09:44,264 undue risk as they're taking on and, 250 00:09:44,979 --> 00:09:47,047 try and to implement new Ai technologies. 251 00:09:48,017 --> 00:09:50,326 Yes, our local governance has been really helpful 252 00:09:50,326 --> 00:09:52,555 in thinking through that. So for example, some 253 00:09:52,555 --> 00:09:53,533 of our early 254 00:09:53,988 --> 00:09:54,488 opportunities 255 00:09:54,864 --> 00:09:55,683 were around 256 00:09:56,074 --> 00:09:58,008 a low risk kind of areas 257 00:09:58,384 --> 00:10:00,695 in the business that would not impact kind 258 00:10:00,695 --> 00:10:03,165 of clinical care. And so we were looking 259 00:10:03,165 --> 00:10:05,657 in cycle operations and other areas 260 00:10:05,969 --> 00:10:07,346 even when we again 261 00:10:07,883 --> 00:10:09,000 introducing clinical Ai, 262 00:10:09,638 --> 00:10:10,138 we 263 00:10:10,595 --> 00:10:13,387 ensure that our governance processes and principles like 264 00:10:13,387 --> 00:10:15,243 keeping a human in the loop for accountability 265 00:10:15,874 --> 00:10:18,189 we're adhere to, and that helps to really 266 00:10:18,189 --> 00:10:21,860 mitigate potential risk. So that local governance, local 267 00:10:22,020 --> 00:10:23,936 Ai principles, you, driving, 268 00:10:24,669 --> 00:10:26,503 the problems is that should be solved. I 269 00:10:26,503 --> 00:10:27,380 think is really key. 270 00:10:30,571 --> 00:10:32,405 Fantastic. Doctor. Wagner. Thank you so much for 271 00:10:32,405 --> 00:10:33,840 joining the podcast today. This has been a 272 00:10:33,840 --> 00:10:35,684 really amazing discussion. And I look forward to 273 00:10:35,684 --> 00:10:36,637 connecting with you again soon. 274 00:10:37,988 --> 00:10:39,259 Absolutely. Thanks for having me, Laura.