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 00:00:29,329 --> 00:00:31,089 Hello, and welcome to the Becker's Health Care 11 00:00:31,089 --> 00:00:33,329 podcast recorded at the 9th annual health IT, 12 00:00:33,329 --> 00:00:35,670 digital health, and RCM conference in Chicago. 13 00:00:36,049 --> 00:00:39,250 We're joined today by Randy Farmer, chief operating 14 00:00:39,250 --> 00:00:41,510 officer at Delaware Health Information Network. 15 00:00:41,905 --> 00:00:43,184 Randy, to get us started, can you please 16 00:00:43,184 --> 00:00:44,545 tell us a little bit about yourself, your 17 00:00:44,545 --> 00:00:46,545 background, and your role at your organization? Yes. 18 00:00:46,545 --> 00:00:48,225 Absolutely. And, Jeremy, thank you very much for 19 00:00:48,225 --> 00:00:49,984 the opportunity. It's it's great to be here 20 00:00:49,984 --> 00:00:52,384 at Becker's. This is an awesome conference. Love 21 00:00:52,384 --> 00:00:53,364 coming every year. 22 00:00:54,170 --> 00:00:56,409 Currently serving as the COO at the Delaware 23 00:00:56,409 --> 00:00:58,170 Health Health Information Network. 24 00:00:58,810 --> 00:01:01,130 We basically are the central nervous system for 25 00:01:01,130 --> 00:01:01,450 health, 26 00:01:02,090 --> 00:01:04,489 data for, Delaware and much of the surrounding 27 00:01:04,489 --> 00:01:06,650 region. So, like, if somebody has a test 28 00:01:06,650 --> 00:01:09,310 at a Delaware hospital, lab, radiology farm, 29 00:01:09,734 --> 00:01:11,895 or even at like, let's say, Johns Hopkins 30 00:01:11,895 --> 00:01:14,615 in Baltimore, Maryland, or George Washington Hospital in 31 00:01:14,615 --> 00:01:15,594 Washington, DC, 32 00:01:16,055 --> 00:01:18,694 we get information about that encounter to help 33 00:01:18,694 --> 00:01:20,375 coordinate care. Our, 34 00:01:20,855 --> 00:01:23,015 basic service lines go in the area of 35 00:01:23,015 --> 00:01:23,915 results delivery, 36 00:01:24,640 --> 00:01:26,959 and care coordination, as well as in data 37 00:01:26,959 --> 00:01:31,140 analytics as and, including, supporting clinical trial research. 38 00:01:31,359 --> 00:01:33,680 So it's a a pretty eclectic but a 39 00:01:33,680 --> 00:01:34,659 very exciting, 40 00:01:35,359 --> 00:01:37,680 set of services that we're able to engage 41 00:01:37,680 --> 00:01:39,359 with and and help to make health care 42 00:01:39,359 --> 00:01:40,180 data useful. 43 00:01:40,614 --> 00:01:42,694 Great. As I'm sure you're aware, AI adoption 44 00:01:42,694 --> 00:01:44,234 is exploding in health care. 45 00:01:44,614 --> 00:01:46,215 In your view, what would you say, you 46 00:01:46,215 --> 00:01:48,534 know, the most significant or promising application of 47 00:01:48,534 --> 00:01:51,015 this technology is right now? How is this 48 00:01:51,015 --> 00:01:53,849 informing your organization's innovation strategy? Well, you know, 49 00:01:54,090 --> 00:01:56,909 that's a great question. I mean, AI is 50 00:01:56,969 --> 00:01:59,450 the I guess maybe the the the next 51 00:01:59,450 --> 00:02:02,090 big thing, obviously, in in in data, not 52 00:02:02,090 --> 00:02:04,090 just in health care, but in so many 53 00:02:04,090 --> 00:02:05,229 big data fields. 54 00:02:06,009 --> 00:02:07,609 Of the way we see this as really 55 00:02:07,609 --> 00:02:10,294 being an asset is an area both prescriptive 56 00:02:10,835 --> 00:02:12,135 and predictive analytics. 57 00:02:12,594 --> 00:02:14,534 You know, we we have, like, over 200,000,000, 58 00:02:15,794 --> 00:02:17,014 medical claims over, 59 00:02:17,555 --> 00:02:20,435 medical claims and pharmacy claims. We have well 60 00:02:20,435 --> 00:02:22,534 over 200,000,000, I think around 300,000,000 61 00:02:22,835 --> 00:02:23,655 clinical messages 62 00:02:24,115 --> 00:02:26,349 and and results that have been accumulated 63 00:02:27,129 --> 00:02:28,349 since 2007, 64 00:02:29,209 --> 00:02:32,250 and over 3,000,000 unique patients across the this 65 00:02:32,250 --> 00:02:32,750 landscape. 66 00:02:33,289 --> 00:02:35,449 So with with that amount of data, we 67 00:02:35,449 --> 00:02:37,069 should be able to start leveraging 68 00:02:37,544 --> 00:02:39,544 AI tools to be able to identify those 69 00:02:39,544 --> 00:02:41,625 pockets of patients that may be most at 70 00:02:41,625 --> 00:02:44,185 risk at certain points of their health care 71 00:02:44,185 --> 00:02:44,685 journey. 72 00:02:45,064 --> 00:02:47,144 Case in point, let's say somebody is released 73 00:02:47,144 --> 00:02:48,924 from a correctional institution. 74 00:02:49,379 --> 00:02:51,300 Who are those patients that are most likely 75 00:02:51,300 --> 00:02:53,620 to have a relapse, either in behavior health 76 00:02:53,620 --> 00:02:55,240 and or substance abuse? 77 00:02:56,180 --> 00:02:58,099 Can we use that data in order to 78 00:02:58,099 --> 00:02:59,080 better allocate 79 00:02:59,540 --> 00:03:00,919 care coordination resources 80 00:03:01,460 --> 00:03:04,164 to better support those individuals so that, well, 81 00:03:04,164 --> 00:03:05,925 you know, they they don't end up having 82 00:03:05,925 --> 00:03:06,585 to need, 83 00:03:07,205 --> 00:03:09,844 resources down the road or or or get 84 00:03:09,844 --> 00:03:10,584 re incarcerated. 85 00:03:10,965 --> 00:03:12,485 And the same thing for those folks that, 86 00:03:12,485 --> 00:03:14,824 like, let's say, have substance abuse disorders. 87 00:03:15,125 --> 00:03:17,284 Can we look at patterns in terms of 88 00:03:17,284 --> 00:03:18,664 those folks that have, 89 00:03:19,639 --> 00:03:20,139 had 90 00:03:20,560 --> 00:03:21,379 to be readmitted 91 00:03:21,680 --> 00:03:22,900 or have had relapses 92 00:03:23,199 --> 00:03:26,400 in these areas? And identify folks prescriptively or 93 00:03:26,400 --> 00:03:26,900 predictably 94 00:03:27,439 --> 00:03:29,120 to say, hey, these folks are most at 95 00:03:29,120 --> 00:03:32,485 risk. Put your care coordination resources here. And, 96 00:03:32,485 --> 00:03:33,545 you know, by, 97 00:03:34,085 --> 00:03:37,284 that extra effort, that extra energy, maybe help 98 00:03:37,284 --> 00:03:39,705 save lives and save money in the process. 99 00:03:40,885 --> 00:03:42,724 So as you mentioned, you know, health care 100 00:03:42,724 --> 00:03:45,205 leaders are managing greater and greater volumes of 101 00:03:45,205 --> 00:03:46,745 data across more devices 102 00:03:47,180 --> 00:03:49,020 and a growing number of care settings and 103 00:03:49,020 --> 00:03:49,520 populations. 104 00:03:50,139 --> 00:03:53,260 In this complex environment, what clinical data integration 105 00:03:53,260 --> 00:03:55,580 tools or practices are you seeing drive these 106 00:03:55,580 --> 00:03:57,439 improvements in patient outcomes operations, 107 00:03:58,139 --> 00:04:00,060 and can you share an example? Sure. You 108 00:04:00,060 --> 00:04:02,240 know, to me, there's nothing more important 109 00:04:02,645 --> 00:04:03,784 than, let's say, 110 00:04:04,485 --> 00:04:06,425 the speed in which you're able to empower 111 00:04:06,564 --> 00:04:08,805 decision makers, you know, with with the data. 112 00:04:08,805 --> 00:04:10,405 You know, it's one thing if somebody's trying 113 00:04:10,405 --> 00:04:12,564 to make decisions off claims data, which can 114 00:04:12,564 --> 00:04:14,084 have, you know, anywhere from, 115 00:04:14,884 --> 00:04:17,305 10 days to several months in in lag, 116 00:04:17,509 --> 00:04:19,829 versus like the real time clinical data. You 117 00:04:19,829 --> 00:04:21,589 know, is when somebody gets admitted to a 118 00:04:21,589 --> 00:04:23,669 hospital, if you're a care coordinator, don't you 119 00:04:23,669 --> 00:04:25,829 wanna know within minutes? And that's the power, 120 00:04:25,829 --> 00:04:27,289 I think, of many of our networks, 121 00:04:27,910 --> 00:04:29,850 or at least networks that are health information 122 00:04:29,910 --> 00:04:30,410 exchanges. 123 00:04:31,035 --> 00:04:33,435 We are able to empower payers and their 124 00:04:33,435 --> 00:04:35,354 care coordination teams, as well as like the 125 00:04:35,354 --> 00:04:38,175 care coordination teams of clinically integrated networks, 126 00:04:38,555 --> 00:04:41,694 or with the accountable care organizations, the ACOs, 127 00:04:42,235 --> 00:04:45,050 that are working really hard to get on 128 00:04:45,050 --> 00:04:48,490 top of conditions before they graduate to more 129 00:04:48,490 --> 00:04:50,750 severe cases and more expensive, 130 00:04:51,370 --> 00:04:53,370 cases. So to me, I think like the 131 00:04:53,449 --> 00:04:56,250 the real time notifications. When is somebody transferred 132 00:04:56,250 --> 00:04:58,425 out of a skilled nursing facility or into 133 00:04:58,425 --> 00:05:01,064 a skilled nursing facility? When are they discharged 134 00:05:01,064 --> 00:05:02,745 from an ER with a potential, 135 00:05:03,545 --> 00:05:05,564 diagnosis of having, let's say, congestive, 136 00:05:06,185 --> 00:05:08,764 heart disorder? And if those are very expensive 137 00:05:08,824 --> 00:05:11,145 type conditions, don't you wanna have your care 138 00:05:11,145 --> 00:05:13,370 coordination teams right on top of that? I 139 00:05:13,370 --> 00:05:15,129 mean, we did a great pilot with a, 140 00:05:15,689 --> 00:05:16,910 with one of our commercial, 141 00:05:17,290 --> 00:05:18,589 healthcare payers recently, 142 00:05:19,129 --> 00:05:20,189 and we basically 143 00:05:20,730 --> 00:05:21,870 doubled their effectiveness 144 00:05:22,250 --> 00:05:23,149 in terms of, 145 00:05:23,610 --> 00:05:24,990 getting it patient engagement. 146 00:05:25,305 --> 00:05:27,964 And then in that group, we increased their 147 00:05:28,104 --> 00:05:28,604 enrollment, 148 00:05:29,384 --> 00:05:30,925 rate from 50% 149 00:05:31,305 --> 00:05:32,745 of the folks that they were able to 150 00:05:32,745 --> 00:05:34,365 engage with to 90% 151 00:05:34,745 --> 00:05:36,664 of the folks that they engage with were, 152 00:05:37,625 --> 00:05:40,024 enrolled in a care coordination program. And when 153 00:05:40,024 --> 00:05:41,220 you're looking at, like, 154 00:05:41,620 --> 00:05:43,620 ER cost of like an average visit of 155 00:05:43,620 --> 00:05:44,839 like 15 to $17,000 156 00:05:46,180 --> 00:05:48,819 or let's say in the more, like extreme 157 00:05:48,819 --> 00:05:51,079 cases, like with a cardiology or an oncology 158 00:05:51,220 --> 00:05:51,720 type, 159 00:05:52,180 --> 00:05:52,680 condition 160 00:05:53,060 --> 00:05:54,839 of being around 30 some $1,000, 161 00:05:55,384 --> 00:05:57,725 Oh my gosh. That is such a tremendous 162 00:05:57,785 --> 00:05:59,944 benefit to these care coordination teams and and 163 00:05:59,944 --> 00:06:02,044 to the bottom line business of these organizations. 164 00:06:02,824 --> 00:06:04,185 So, you know, as your role as COO, 165 00:06:04,185 --> 00:06:05,785 you probably have a pretty good insight into 166 00:06:05,785 --> 00:06:06,285 this. 167 00:06:06,664 --> 00:06:07,964 How can health care organizations 168 00:06:08,425 --> 00:06:09,245 better support, 169 00:06:09,669 --> 00:06:11,669 you know, IT teams and clinical teams as 170 00:06:11,669 --> 00:06:14,389 they carry out, their innovation efforts? And, you 171 00:06:14,389 --> 00:06:15,829 know, what are some of the common pitfalls 172 00:06:15,829 --> 00:06:17,349 we see here? Yeah. You know what? I 173 00:06:17,349 --> 00:06:18,789 I think a a lot of it has 174 00:06:18,789 --> 00:06:20,069 to do with, like, you know, how we 175 00:06:20,069 --> 00:06:22,004 go with our speed to market in terms 176 00:06:22,004 --> 00:06:23,925 of, like, how we how we innovate and 177 00:06:23,925 --> 00:06:26,904 how we change, and understanding and scoping out, 178 00:06:27,925 --> 00:06:29,444 how long it's gonna take for us, and 179 00:06:29,444 --> 00:06:31,285 what's gonna be required for us to do 180 00:06:31,285 --> 00:06:33,524 a deployment. I mean, that's kinda getting into 181 00:06:33,524 --> 00:06:35,419 the, you know, nuts and bolts of, like, 182 00:06:35,419 --> 00:06:37,680 you know, program management, project management. 183 00:06:38,220 --> 00:06:40,800 So but that's and, you know, execute execution 184 00:06:40,860 --> 00:06:43,019 is the chariot of genius. Right? Mhmm. You 185 00:06:43,019 --> 00:06:45,579 know, and how well you can basically go 186 00:06:45,579 --> 00:06:48,160 from a plan to something being real 187 00:06:48,485 --> 00:06:51,285 is at the heart of of excellence. Mhmm. 188 00:06:51,285 --> 00:06:53,704 So that's something that I think our organization 189 00:06:54,324 --> 00:06:55,545 continues. And any 190 00:06:56,004 --> 00:06:59,044 organization that wants to be, well, a problem 191 00:06:59,044 --> 00:07:01,285 solver for its clients, that wants to be 192 00:07:01,285 --> 00:07:03,544 a valued contributor, wants to be a strategic 193 00:07:03,764 --> 00:07:05,920 partner to its most valuable customers, 194 00:07:06,379 --> 00:07:08,860 you gotta focus on execution. So, yeah, I 195 00:07:08,939 --> 00:07:11,180 I'd I'd say that that's where any good 196 00:07:11,180 --> 00:07:13,180 organization that wants to be great needs to 197 00:07:13,180 --> 00:07:13,680 focus. 198 00:07:14,139 --> 00:07:14,639 Okay. 199 00:07:15,259 --> 00:07:17,735 So what's your top, piece of advice for 200 00:07:17,735 --> 00:07:19,574 other health care leaders as they prepare for 201 00:07:19,574 --> 00:07:22,935 further advancements in technology and, greater demands for 202 00:07:22,935 --> 00:07:26,375 care? Wow. Advice for for, like, health health 203 00:07:26,375 --> 00:07:28,795 care, for leaders out there. Yeah. 204 00:07:29,519 --> 00:07:32,480 Data is king, which they probably know. Mhmm. 205 00:07:32,480 --> 00:07:34,720 Mhmm. And choose partners that are the best 206 00:07:34,720 --> 00:07:36,959 at making health care data useful. Look, the 207 00:07:36,959 --> 00:07:39,759 the the health care data landscape Mhmm. Is 208 00:07:39,759 --> 00:07:42,259 full of chaos. There's a lot of hay. 209 00:07:42,319 --> 00:07:43,540 There's a lot of needles, 210 00:07:44,035 --> 00:07:46,355 but it's hidden within the hay. You know, 211 00:07:46,355 --> 00:07:49,095 how can you leverage the the the outstanding 212 00:07:49,314 --> 00:07:51,795 software tools that are out there with people 213 00:07:51,795 --> 00:07:53,875 that are able to execute, companies that are 214 00:07:53,875 --> 00:07:56,274 able to execute in order to find that 215 00:07:56,274 --> 00:07:58,194 needles in real time Mhmm. And to be 216 00:07:58,194 --> 00:07:59,334 able to make it actionable. 217 00:07:59,709 --> 00:08:01,550 And, you know, look, just because you get 218 00:08:01,550 --> 00:08:03,149 a lot of data or you you get 219 00:08:03,149 --> 00:08:06,029 a lot of, reports doesn't mean necessarily that 220 00:08:06,029 --> 00:08:06,769 it's actionable. 221 00:08:07,470 --> 00:08:09,149 I think that in terms of, like, how 222 00:08:09,149 --> 00:08:12,029 we serve our clients, understand one problem to 223 00:08:12,029 --> 00:08:14,294 solve. If if if your client can tell 224 00:08:14,294 --> 00:08:16,375 you one problem that they're committed to solving 225 00:08:16,375 --> 00:08:19,495 with you, focus and execute. The the you 226 00:08:19,495 --> 00:08:19,995 know, 227 00:08:20,375 --> 00:08:22,875 be fierce in terms of your commitment 228 00:08:23,495 --> 00:08:25,274 to conquer that problem. 229 00:08:25,670 --> 00:08:26,170 And, 230 00:08:26,550 --> 00:08:28,709 and and and yeah. I I think that 231 00:08:28,709 --> 00:08:30,949 that's again, it goes back to, like, how 232 00:08:30,949 --> 00:08:33,590 execution is king. Mhmm. Brady, I think it's 233 00:08:33,590 --> 00:08:34,870 safe to say our listeners got a lot 234 00:08:34,870 --> 00:08:36,549 out of this conversation. But before I wrap 235 00:08:36,549 --> 00:08:37,830 up, do you have any final thoughts you'd 236 00:08:37,830 --> 00:08:38,809 like to live with them? 237 00:08:39,664 --> 00:08:42,245 Yeah. Number 1, Becker's is an awesome conference. 238 00:08:42,384 --> 00:08:45,424 There's a lot of of of great high 239 00:08:45,424 --> 00:08:48,884 level executives here. And I totally recommend, 240 00:08:49,424 --> 00:08:51,985 of folks joining the conference and going to 241 00:08:51,985 --> 00:08:52,644 the sessions. 242 00:08:53,105 --> 00:08:54,324 They're very thought provoking, 243 00:08:54,889 --> 00:08:57,129 and it's great to have the after panel 244 00:08:57,129 --> 00:08:59,710 discussions with folks and continue the conversation. 245 00:09:00,649 --> 00:09:02,029 In in terms of generally, 246 00:09:03,129 --> 00:09:04,830 you know, how do you manage your business? 247 00:09:04,970 --> 00:09:06,889 You know, look, I would just, recommend is 248 00:09:06,889 --> 00:09:08,649 that you you you look to grow your 249 00:09:08,649 --> 00:09:09,149 relationships. 250 00:09:09,804 --> 00:09:11,884 Don't be satisfied with, let's say, your top 251 00:09:11,884 --> 00:09:14,144 10 customers and just being an order taker. 252 00:09:14,445 --> 00:09:16,285 Find a way to become a trusted advisor 253 00:09:16,285 --> 00:09:17,585 and a strategic partner. 254 00:09:18,045 --> 00:09:19,985 No. Understand that's a long, hard slog. 255 00:09:20,684 --> 00:09:22,225 But put together a plan, 256 00:09:22,559 --> 00:09:24,740 understand what problems they have to to solve, 257 00:09:25,039 --> 00:09:26,559 execute, and you will, 258 00:09:26,960 --> 00:09:28,879 eventually be elevated to a level of being 259 00:09:28,879 --> 00:09:31,440 a strategic partner to your customers. Mhmm. Well, 260 00:09:31,440 --> 00:09:32,880 Randy, thank you again for taking the time 261 00:09:32,880 --> 00:09:34,480 to join us on the show today. I 262 00:09:34,480 --> 00:09:35,920 really appreciate it. Thank you very much, and 263 00:09:35,920 --> 00:09:36,960 you have a lovely rest of your day. 264 00:09:36,960 --> 00:09:38,900 It was my pleasure. Thanks, Jeremy. Thank you.