1 00:00:00,080 --> 00:00:02,639 Hi, everyone. This is Lucas Voss with Becker's 2 00:00:02,639 --> 00:00:04,400 Healthcare. Thanks so much for tuning in to 3 00:00:04,400 --> 00:00:07,519 the Becker's Healthcare podcast series. Fantastic to have 4 00:00:07,519 --> 00:00:10,160 you. Today, we're talking about from data to 5 00:00:10,160 --> 00:00:13,359 dollar strategies to curb rising healthcare costs, a 6 00:00:13,359 --> 00:00:15,855 very exciting topic, and I'm even more excited 7 00:00:16,254 --> 00:00:18,574 to be joined by Marcy Tach, executive vice 8 00:00:18,574 --> 00:00:21,454 president, proven by Meredith. Marcy, it's great to 9 00:00:21,454 --> 00:00:23,054 have you. Thanks for being here. Thank you 10 00:00:23,054 --> 00:00:25,074 so much, Lucas. I'm excited for the conversation. 11 00:00:25,614 --> 00:00:28,195 Absolutely. For those that might not know you 12 00:00:28,254 --> 00:00:30,094 yet at least, could you start by just 13 00:00:30,094 --> 00:00:31,869 introducing yourself to our audience and just share 14 00:00:31,869 --> 00:00:34,030 a little bit about your background in health 15 00:00:34,030 --> 00:00:35,250 care? Absolutely. 16 00:00:35,789 --> 00:00:38,289 So my career has been at an intersection 17 00:00:38,590 --> 00:00:41,070 of technology and health care. So I spent 18 00:00:41,070 --> 00:00:42,210 many years at McKesson, 19 00:00:42,750 --> 00:00:45,454 and then changed health care. I joined Meredith 20 00:00:45,515 --> 00:00:46,715 in 2023 21 00:00:46,715 --> 00:00:49,534 running their Curum business, and Curum is in 22 00:00:49,594 --> 00:00:51,934 the health and human services side. We have, 23 00:00:52,314 --> 00:00:54,475 lots of international business with that with that 24 00:00:54,475 --> 00:00:55,534 particular company. 25 00:00:56,155 --> 00:00:58,314 Last year, I, moved over to be the 26 00:00:58,314 --> 00:00:59,774 general manager of Truven. 27 00:01:00,310 --> 00:01:02,230 And just a a quick overview of what 28 00:01:02,230 --> 00:01:04,810 Truven does. Truven is a data and analytics 29 00:01:04,870 --> 00:01:06,010 company that serves 30 00:01:06,469 --> 00:01:09,909 40% of the Fortune 100 employers, and we 31 00:01:09,909 --> 00:01:12,150 work with seven of the top 10 health 32 00:01:12,150 --> 00:01:14,064 plans. We also, Lucas, just, 33 00:01:14,545 --> 00:01:17,424 acquired Springbok that was in the news last 34 00:01:17,424 --> 00:01:20,405 month, which added another 7,500 35 00:01:20,545 --> 00:01:23,665 small to mid market employers into our customer 36 00:01:23,665 --> 00:01:25,765 base. We're in a really exciting 37 00:01:26,064 --> 00:01:27,844 place to serve the employers. 38 00:01:28,659 --> 00:01:29,780 And it's so great to have you on. 39 00:01:29,780 --> 00:01:31,380 Again, you mentioned all of your experiences, such 40 00:01:31,380 --> 00:01:33,540 a great perspective, obviously. And then as you've 41 00:01:33,540 --> 00:01:34,760 just told us, 42 00:01:35,140 --> 00:01:37,380 so much available information that we're gonna get 43 00:01:37,380 --> 00:01:38,900 to hear about today, which I'm so excited 44 00:01:38,900 --> 00:01:40,579 about. And and I feel like this topic 45 00:01:40,579 --> 00:01:42,260 never really gets old. Right? 46 00:01:42,659 --> 00:01:44,864 There's a lot of focus on the high 47 00:01:44,864 --> 00:01:47,664 cost of health care right now specifically. We've 48 00:01:47,664 --> 00:01:48,564 had those conversations. 49 00:01:49,265 --> 00:01:51,504 From your perspective, what are some of the 50 00:01:51,504 --> 00:01:52,405 biggest factors 51 00:01:52,944 --> 00:01:54,804 driving those costs right now 52 00:01:55,185 --> 00:01:55,905 and why 53 00:01:56,469 --> 00:01:58,310 this is really important. Right? Why has it 54 00:01:58,310 --> 00:01:59,930 been so difficult for organizations 55 00:02:00,549 --> 00:02:01,849 to really make meaningful 56 00:02:02,310 --> 00:02:04,649 progress in addressing those costs? 57 00:02:05,269 --> 00:02:06,489 Great question. So, 58 00:02:07,109 --> 00:02:08,169 a few examples 59 00:02:08,915 --> 00:02:11,634 of what's driving cost would be the aging 60 00:02:11,634 --> 00:02:14,294 population and chronic disease. Right? So as people, 61 00:02:14,995 --> 00:02:17,555 get older, the chronic disease is just part 62 00:02:17,555 --> 00:02:18,455 of that equation. 63 00:02:18,915 --> 00:02:20,615 That coupled with the complexity 64 00:02:20,995 --> 00:02:21,895 of our systems 65 00:02:22,435 --> 00:02:23,974 drive cost up. So 66 00:02:24,560 --> 00:02:26,800 most companies, when I when I meet with 67 00:02:26,800 --> 00:02:27,300 clients, 68 00:02:27,919 --> 00:02:29,139 they want to have 69 00:02:29,680 --> 00:02:31,540 high quality care for their employees. 70 00:02:32,240 --> 00:02:33,139 But they're also, 71 00:02:33,760 --> 00:02:35,680 you know, the the two sides pulling is 72 00:02:35,680 --> 00:02:38,260 they're also trying to balance rising costs. So 73 00:02:38,504 --> 00:02:39,805 benefits for these employers 74 00:02:40,504 --> 00:02:42,584 is one of their largest expenses. And and 75 00:02:42,584 --> 00:02:44,425 most people know this, but it's really second 76 00:02:44,425 --> 00:02:46,344 to cost of goods and services. Right? So 77 00:02:46,584 --> 00:02:48,824 Yeah. You look at, in 2024, 78 00:02:48,824 --> 00:02:52,264 this just astounds me, the average annual premium 79 00:02:52,264 --> 00:02:53,004 per family 80 00:02:53,870 --> 00:02:56,349 was over $25,000 81 00:02:56,349 --> 00:02:57,090 for coverage. 82 00:02:57,550 --> 00:03:01,389 Workers covered 6,000 of that. Employers contributed about 83 00:03:01,389 --> 00:03:02,270 19,000. 84 00:03:02,270 --> 00:03:05,090 So the cost is is high. 85 00:03:05,724 --> 00:03:07,645 We we know that in 2025, 86 00:03:07,645 --> 00:03:09,485 when we end twenty twenty five, we think 87 00:03:09,485 --> 00:03:12,205 those costs will have risen another 9%. 88 00:03:12,205 --> 00:03:14,625 So it's just not sustainable. And 89 00:03:15,245 --> 00:03:17,485 to your second question of why is this 90 00:03:17,485 --> 00:03:19,969 so difficult, it's there's a mass amount of 91 00:03:19,969 --> 00:03:22,290 data out there. And and because of the 92 00:03:22,290 --> 00:03:22,790 complexity 93 00:03:23,650 --> 00:03:26,449 of the conditions and the data available and 94 00:03:26,449 --> 00:03:29,330 the skywriting cost, companies really need to use 95 00:03:29,330 --> 00:03:31,889 data and insights that they can drive from 96 00:03:31,889 --> 00:03:34,514 that data to make better informed decisions. And 97 00:03:34,514 --> 00:03:37,014 so when I am talking to companies 98 00:03:38,115 --> 00:03:39,175 and health plans 99 00:03:39,474 --> 00:03:41,014 and and many of our customers, 100 00:03:41,395 --> 00:03:42,534 they're really looking, 101 00:03:43,314 --> 00:03:45,814 for data to provide insights 102 00:03:46,120 --> 00:03:47,960 to make so that they can all make 103 00:03:47,960 --> 00:03:50,680 much more informed decisions for their population based. 104 00:03:50,680 --> 00:03:52,760 And as we know, no two employers is 105 00:03:52,760 --> 00:03:54,599 the same. You have you have different regions. 106 00:03:54,599 --> 00:03:55,819 You have different demographics. 107 00:03:56,360 --> 00:03:58,760 And so really diving into that data and 108 00:03:58,760 --> 00:04:01,099 making sure your data is curated and accurate 109 00:04:01,405 --> 00:04:04,305 is really important to really curb that cost 110 00:04:04,525 --> 00:04:06,764 for this ongoing trend that we're seeing in 111 00:04:06,764 --> 00:04:07,824 The United States. 112 00:04:08,525 --> 00:04:10,205 And I think that's even more important now 113 00:04:10,205 --> 00:04:12,924 because you've mentioned, you know, aging population, etcetera. 114 00:04:12,924 --> 00:04:14,144 There's so many factors 115 00:04:14,870 --> 00:04:17,110 driving all of this that you have to 116 00:04:17,110 --> 00:04:19,610 have good data to be able to influence 117 00:04:19,750 --> 00:04:21,990 and make meaningful change there. And and I 118 00:04:21,990 --> 00:04:23,509 think one of the interesting things is, right, 119 00:04:23,509 --> 00:04:24,949 we we talk a lot about long term 120 00:04:24,949 --> 00:04:26,709 and and short term. Right? Where can we 121 00:04:26,709 --> 00:04:27,449 make impact? 122 00:04:28,115 --> 00:04:30,754 Where do you see the biggest near term 123 00:04:30,754 --> 00:04:33,254 opportunities versus that long term piece, 124 00:04:33,875 --> 00:04:36,914 structural fixes right to to really create lasting 125 00:04:36,914 --> 00:04:38,914 change in that space? What are some of 126 00:04:38,914 --> 00:04:41,334 those opportunities that you're looking to right now? 127 00:04:41,680 --> 00:04:44,419 Sure. So one of the near term opportunities 128 00:04:44,560 --> 00:04:46,879 is GLP one drugs. So taking a step 129 00:04:46,879 --> 00:04:47,779 back on Truven, 130 00:04:48,399 --> 00:04:50,079 one of the the other tools we have 131 00:04:50,079 --> 00:04:52,339 in the Truven portfolio is is the MarketScan 132 00:04:52,560 --> 00:04:55,379 database, which is a large longitudinal record that 133 00:04:55,680 --> 00:04:57,975 we use to help clients do, 134 00:04:58,535 --> 00:05:00,394 studies and and help them understand. 135 00:05:00,774 --> 00:05:03,014 And what we we know is most clients 136 00:05:03,014 --> 00:05:04,475 are seeing skyrocketing 137 00:05:04,854 --> 00:05:07,254 cost on GLP one drugs. One in eight 138 00:05:07,254 --> 00:05:10,154 American adults have taken a GLP one drug. 139 00:05:10,649 --> 00:05:12,889 We don't expect this to to to stop. 140 00:05:12,889 --> 00:05:14,889 Most of the employers are planning in their 141 00:05:14,889 --> 00:05:16,589 benefit cycle of how to help, 142 00:05:16,970 --> 00:05:19,050 help their employees have access to these drugs, 143 00:05:19,050 --> 00:05:21,529 but to do it in a successful way. 144 00:05:21,529 --> 00:05:24,685 So these are expensive medications as as most 145 00:05:24,685 --> 00:05:25,585 people know. 146 00:05:25,965 --> 00:05:28,925 And and to drive an outcome, we're finding 147 00:05:28,925 --> 00:05:31,645 in the data shows that the drug plus 148 00:05:31,645 --> 00:05:32,145 services 149 00:05:33,404 --> 00:05:35,345 actually leads to better results. So 150 00:05:35,884 --> 00:05:37,805 we ask employers and when we talk to 151 00:05:37,805 --> 00:05:38,305 employers, 152 00:05:38,670 --> 00:05:41,710 you they need to combine the drug, the 153 00:05:41,710 --> 00:05:42,210 medical, 154 00:05:42,670 --> 00:05:45,569 biometric data, lab data, and actually 155 00:05:45,870 --> 00:05:46,930 monitor utilization 156 00:05:47,629 --> 00:05:48,370 and outcomes. 157 00:05:48,750 --> 00:05:50,509 And and when they do that, they can 158 00:05:50,509 --> 00:05:53,069 better manage cost. Right? So these drugs have 159 00:05:53,069 --> 00:05:53,650 a benefit 160 00:05:54,055 --> 00:05:55,595 to many different populations, 161 00:05:56,294 --> 00:05:57,894 and so there's a definite need for the 162 00:05:57,894 --> 00:05:59,894 drugs. It's just coupling them with the right 163 00:06:00,134 --> 00:06:01,754 like, in a weight loss situation, 164 00:06:02,134 --> 00:06:04,394 coupling with weight loss services and so that 165 00:06:04,454 --> 00:06:06,534 better habits are created for long term so 166 00:06:06,534 --> 00:06:08,449 that people maybe don't have to be on 167 00:06:08,449 --> 00:06:10,370 the same doses of the GLP ones long 168 00:06:10,370 --> 00:06:12,470 term. On a structural side, 169 00:06:12,850 --> 00:06:14,629 I I would really lean into, 170 00:06:14,930 --> 00:06:17,810 you know, mental health and wellness. So one 171 00:06:17,810 --> 00:06:20,770 in five adults who United States adults experience 172 00:06:20,770 --> 00:06:21,910 mental health illness. 173 00:06:22,495 --> 00:06:24,334 And, you know, we I think we really 174 00:06:24,334 --> 00:06:26,334 started to see that come to light during 175 00:06:26,334 --> 00:06:29,055 during COVID, right, when people were were home 176 00:06:29,055 --> 00:06:30,035 and more isolated. 177 00:06:31,454 --> 00:06:34,194 Employers are offering more mental health resources now, 178 00:06:34,574 --> 00:06:37,474 and they they need to help employees engage 179 00:06:38,040 --> 00:06:39,720 around those support system. And so there's a 180 00:06:39,720 --> 00:06:41,339 lot of point solutions out there. 181 00:06:41,720 --> 00:06:43,879 There's a lot of wave ways for companies 182 00:06:43,879 --> 00:06:46,220 to combine their data with sociodemographic 183 00:06:46,759 --> 00:06:47,259 data, 184 00:06:47,879 --> 00:06:49,899 and they can get a complete picture 185 00:06:50,439 --> 00:06:53,019 of of the employee and tailor the approach 186 00:06:53,264 --> 00:06:55,584 to ensure that that employee is engaged in 187 00:06:55,584 --> 00:06:56,884 getting the most benefit, 188 00:06:57,985 --> 00:07:00,084 for mental health and for their for, 189 00:07:00,625 --> 00:07:03,185 their illness. And, you know, companies can also 190 00:07:03,185 --> 00:07:05,664 use data and some of the research that 191 00:07:05,664 --> 00:07:07,345 they can they can get from the data 192 00:07:07,345 --> 00:07:10,250 to to hold vendors accountable. There's a number 193 00:07:10,250 --> 00:07:12,329 of point solution vendors out there, many very 194 00:07:12,329 --> 00:07:14,029 good, but not every 195 00:07:14,729 --> 00:07:18,250 solution is tailored for every employee population. So, 196 00:07:18,250 --> 00:07:20,250 you know, we really like to to tell 197 00:07:20,250 --> 00:07:22,794 companies, do you know? Look. You try these, 198 00:07:22,794 --> 00:07:25,035 do analysis, see where the return is, and 199 00:07:25,035 --> 00:07:27,055 really make sure you're coupling the right tools 200 00:07:27,435 --> 00:07:29,435 for your population to get the best the 201 00:07:29,435 --> 00:07:31,294 best outcome for those systemic issues. 202 00:07:32,394 --> 00:07:34,660 I love that you mentioned the specific approach, 203 00:07:34,660 --> 00:07:36,180 right, because I think it comes back to 204 00:07:36,180 --> 00:07:38,740 we need a holistic approach that also addresses 205 00:07:38,740 --> 00:07:41,139 things like social determinants of health and all 206 00:07:41,139 --> 00:07:42,980 of these different aspects, and that's where data 207 00:07:42,980 --> 00:07:44,899 can really shine is being able to address 208 00:07:44,899 --> 00:07:46,839 some of this. It's it's so, so key 209 00:07:46,899 --> 00:07:47,879 and so important. 210 00:07:48,524 --> 00:07:50,365 I wanna tie us back to what we 211 00:07:50,365 --> 00:07:53,245 originally started our conversation with, which was the 212 00:07:53,245 --> 00:07:55,404 high cost of health care. And I wanna 213 00:07:55,404 --> 00:07:57,644 talk a little bit about how we can 214 00:07:57,644 --> 00:08:00,045 fix this. Right? How we can address some 215 00:08:00,045 --> 00:08:01,644 of this, how we can make it more 216 00:08:01,644 --> 00:08:02,144 affordable. 217 00:08:02,589 --> 00:08:04,669 What are some of the strategies and approaches 218 00:08:04,669 --> 00:08:07,789 that you've seen actually work when it comes 219 00:08:07,789 --> 00:08:09,789 to making health care more affordable? And I'm 220 00:08:09,789 --> 00:08:11,649 also wondering if you could share an example, 221 00:08:12,110 --> 00:08:13,970 from from your work and from the conversations 222 00:08:14,029 --> 00:08:16,050 that you're having that that really illustrates 223 00:08:16,350 --> 00:08:18,194 this approach in in action. 224 00:08:19,235 --> 00:08:19,735 Absolutely. 225 00:08:20,115 --> 00:08:22,115 So, again, I I go back to, you 226 00:08:22,115 --> 00:08:24,355 know, a data driven approach works best. And 227 00:08:24,355 --> 00:08:26,295 I I say that because we all have, 228 00:08:26,435 --> 00:08:27,574 you know, our hypothesis 229 00:08:28,274 --> 00:08:30,754 when we look at our employee populations of 230 00:08:30,754 --> 00:08:31,975 of what is needed. 231 00:08:32,459 --> 00:08:34,139 And sometimes we have our own filters, and 232 00:08:34,139 --> 00:08:36,220 we don't see the total picture. So, you 233 00:08:36,220 --> 00:08:38,639 know, data and a data driven approach, 234 00:08:39,179 --> 00:08:40,159 always lends, 235 00:08:40,779 --> 00:08:42,559 to the to the to the best 236 00:08:42,939 --> 00:08:45,179 ability to follow trends and to look at 237 00:08:45,179 --> 00:08:48,054 outcomes and to see, you know, if the 238 00:08:48,054 --> 00:08:51,335 quality of care is is matching the cost 239 00:08:51,335 --> 00:08:53,014 of care. And in many cases, you can 240 00:08:53,014 --> 00:08:55,175 drive to quality of care at lower cost 241 00:08:55,175 --> 00:08:56,875 of settings potentially. So, 242 00:08:57,254 --> 00:08:58,774 one thing Truven has and then I'll 243 00:08:59,575 --> 00:09:01,335 I'm gonna circle back to your question about 244 00:09:01,335 --> 00:09:04,450 an example. So Truvent has an interesting portfolio 245 00:09:04,669 --> 00:09:07,149 of analytic enrichments and models, and we've had 246 00:09:07,149 --> 00:09:09,149 this for a number of years. And one 247 00:09:09,149 --> 00:09:12,190 of the examples of those enrichments is an 248 00:09:12,190 --> 00:09:14,509 episode grouper, and it basically it takes data 249 00:09:14,509 --> 00:09:16,750 in, it groups it clinically, and it will 250 00:09:16,750 --> 00:09:19,424 look at disease staging. With large data pools, 251 00:09:19,424 --> 00:09:21,985 you can actually, with that disease staging, you 252 00:09:21,985 --> 00:09:25,105 can learn to manage population risk and drive 253 00:09:25,105 --> 00:09:26,164 outcomes. So 254 00:09:26,625 --> 00:09:28,164 moving populations from 255 00:09:28,504 --> 00:09:29,004 a, 256 00:09:29,345 --> 00:09:31,139 like, a stage three of a disease back 257 00:09:31,139 --> 00:09:32,899 to a stage two, in some cases, if 258 00:09:32,899 --> 00:09:34,120 you think of diabetes 259 00:09:34,580 --> 00:09:35,639 or other situations, 260 00:09:36,420 --> 00:09:39,700 can drastically reduce the cost. So we had 261 00:09:39,700 --> 00:09:40,679 a recent customer 262 00:09:41,139 --> 00:09:44,420 who was, you know, looking at moving from, 263 00:09:44,740 --> 00:09:47,054 fee for service to value based systems, And 264 00:09:47,054 --> 00:09:49,715 they used Truven's medical officer gripper, 265 00:09:50,414 --> 00:09:51,715 and they looked at physicians, 266 00:09:52,174 --> 00:09:54,335 and they looked at clinical performance and sites 267 00:09:54,335 --> 00:09:54,995 of care. 268 00:09:55,615 --> 00:09:57,855 And by using that episode gripper, they were 269 00:09:57,855 --> 00:09:59,790 able to save 6 and a half million 270 00:09:59,790 --> 00:10:00,529 dollars annually. 271 00:10:00,910 --> 00:10:02,830 And in overall state, you may say, well, 272 00:10:02,830 --> 00:10:04,370 6 and a half is a great number, 273 00:10:04,509 --> 00:10:06,429 but not it's, you know, not game changing 274 00:10:06,429 --> 00:10:09,170 if your if your total cost is 200,000,000. 275 00:10:09,230 --> 00:10:10,910 Right? But if you think there's just little 276 00:10:10,910 --> 00:10:13,004 things you can do, if if you started 277 00:10:13,004 --> 00:10:14,684 to to cut the cost by a million 278 00:10:14,684 --> 00:10:16,764 dollars here and a million dollars there, just 279 00:10:16,764 --> 00:10:19,264 by making better choices and and getting, 280 00:10:19,644 --> 00:10:22,125 patients to the right setting of care at 281 00:10:22,125 --> 00:10:23,985 the right time and getting them intervention 282 00:10:24,950 --> 00:10:25,450 sooner. 283 00:10:25,830 --> 00:10:28,790 These are big steps employers can can take, 284 00:10:29,029 --> 00:10:31,290 to manage their population. And with that, 285 00:10:31,670 --> 00:10:33,990 then they're able to provide additional benefits, right, 286 00:10:33,990 --> 00:10:35,049 for for things 287 00:10:35,350 --> 00:10:36,809 that these employee populations, 288 00:10:37,924 --> 00:10:39,924 need and want as, you know, as they're 289 00:10:40,164 --> 00:10:42,644 because benefits, honestly, for these employers becomes a 290 00:10:42,644 --> 00:10:43,144 differentiation, 291 00:10:44,245 --> 00:10:45,625 when you're going after, 292 00:10:46,565 --> 00:10:48,485 employees and you're you're wanting to have 293 00:10:49,125 --> 00:10:50,884 they want to have the best for for 294 00:10:50,884 --> 00:10:51,384 their, 295 00:10:51,845 --> 00:10:53,580 for their teams. And I think, you know, 296 00:10:53,580 --> 00:10:54,320 really using 297 00:10:55,180 --> 00:10:57,040 data and these outcomes based 298 00:10:57,420 --> 00:10:59,759 scenarios allows them more optionality 299 00:11:00,139 --> 00:11:02,460 for offering more services, which is which is 300 00:11:02,460 --> 00:11:04,300 really what they wanna do. I think it 301 00:11:04,300 --> 00:11:06,794 is worth noting one change we're making in 302 00:11:06,794 --> 00:11:07,674 2026 303 00:11:07,674 --> 00:11:09,214 is these analytic methods 304 00:11:09,754 --> 00:11:12,235 and groupers are only gonna be available through 305 00:11:12,235 --> 00:11:14,634 Truven. And, you know, we we decided to 306 00:11:14,634 --> 00:11:16,315 do that because we we need to be 307 00:11:16,315 --> 00:11:18,495 as close to the market as possible. The 308 00:11:18,554 --> 00:11:19,774 the ability to 309 00:11:20,139 --> 00:11:22,779 help with risks in population and drive outcomes 310 00:11:22,779 --> 00:11:23,679 is so important 311 00:11:24,059 --> 00:11:26,620 that getting closer to the clients and understanding 312 00:11:26,620 --> 00:11:28,459 what their needs are really does help us 313 00:11:28,459 --> 00:11:30,620 improve our portfolio, which is something our clients 314 00:11:30,620 --> 00:11:33,794 were looking for. Another approach to making health 315 00:11:33,794 --> 00:11:35,394 care more affordable. You know, we hear so 316 00:11:35,394 --> 00:11:36,855 much about AI, 317 00:11:37,475 --> 00:11:39,235 and and some of the some of the 318 00:11:39,235 --> 00:11:42,115 challenges, right, with the early stage return on 319 00:11:42,115 --> 00:11:44,115 investment. But there's things you can do with 320 00:11:44,115 --> 00:11:44,615 data 321 00:11:45,154 --> 00:11:46,995 to use it from models and to use 322 00:11:46,995 --> 00:11:49,700 it to to do predictive models 323 00:11:50,080 --> 00:11:52,419 that really do help employers 324 00:11:53,519 --> 00:11:54,340 look ahead 325 00:11:54,720 --> 00:11:56,320 and and really start to map out the 326 00:11:56,320 --> 00:11:57,840 what if model. So I think that's really 327 00:11:57,840 --> 00:12:00,240 important in the future. And, you know, Truven, 328 00:12:00,240 --> 00:12:00,899 we we 329 00:12:01,274 --> 00:12:03,674 are always exploring, you know, different ways to 330 00:12:03,674 --> 00:12:07,115 help, our our customers with you know, to 331 00:12:07,115 --> 00:12:09,214 to detect the risk of rising costs. 332 00:12:10,074 --> 00:12:12,815 Let's say I'm a very forward thinking organization. 333 00:12:13,034 --> 00:12:15,179 I've established all of this. I am looking 334 00:12:15,179 --> 00:12:17,059 at my data. I'm looking at my analytics. 335 00:12:17,059 --> 00:12:18,899 But now I I really need to do 336 00:12:19,059 --> 00:12:20,179 I need to know what to do with 337 00:12:20,179 --> 00:12:21,539 it. I need to know how to how 338 00:12:21,539 --> 00:12:23,299 to best utilize this. And I I think 339 00:12:23,299 --> 00:12:24,899 this is a this is a great question, 340 00:12:24,899 --> 00:12:26,419 I think, because it it get goes to 341 00:12:26,419 --> 00:12:28,014 the heart of of some of the issues 342 00:12:28,014 --> 00:12:30,174 that that we're seeing right now. Right? From 343 00:12:30,174 --> 00:12:32,514 your perspective and and, again, from the conversations 344 00:12:32,575 --> 00:12:34,754 that you're having, what should organizations 345 00:12:35,215 --> 00:12:38,174 prioritize right now to get the most out 346 00:12:38,174 --> 00:12:40,870 of their data? What's the that key thing 347 00:12:40,870 --> 00:12:42,709 right? And then, again, tying it back, you 348 00:12:42,709 --> 00:12:45,350 mentioned the example with the the 6,000,000 savings. 349 00:12:45,350 --> 00:12:45,850 Right? 350 00:12:46,629 --> 00:12:49,589 How can insights really help reduce the costs 351 00:12:49,589 --> 00:12:52,549 and and improve overall value in health care 352 00:12:52,549 --> 00:12:53,929 when it's done right? 353 00:12:54,284 --> 00:12:55,745 Yeah. Great question. 354 00:12:56,125 --> 00:12:58,704 So I would tell organizations they should prioritize 355 00:12:58,924 --> 00:13:01,424 finding gaps in their pop in their population. 356 00:13:01,485 --> 00:13:04,204 So there are there's lots of savings to 357 00:13:04,204 --> 00:13:05,804 be found when you start to look at 358 00:13:05,804 --> 00:13:07,664 and and really start to break down 359 00:13:08,044 --> 00:13:10,490 your costs and what's driving those costs. So 360 00:13:11,049 --> 00:13:13,209 but to find gaps and the right gaps, 361 00:13:13,209 --> 00:13:14,970 you have to have a holistic view of 362 00:13:14,970 --> 00:13:16,829 data, which which needs 363 00:13:17,370 --> 00:13:19,789 data curation and aggregation. So, 364 00:13:20,250 --> 00:13:22,009 you know, years and years ago, you could 365 00:13:22,009 --> 00:13:23,389 just have claims data. 366 00:13:24,544 --> 00:13:25,904 Now, you know, we're in a world where 367 00:13:25,904 --> 00:13:29,184 there's devices and wearables and all kinds of 368 00:13:29,184 --> 00:13:31,205 amazing data that you can pull in. 369 00:13:31,664 --> 00:13:33,825 And, you know, with the right the right 370 00:13:33,825 --> 00:13:35,924 aggregation can give you new insights. 371 00:13:36,790 --> 00:13:38,470 I would also say, you know, we're in 372 00:13:38,470 --> 00:13:40,309 a world where you have to link claims 373 00:13:40,309 --> 00:13:42,870 data with clinical data, looking at those, you 374 00:13:42,870 --> 00:13:44,809 know, both what's paid and the outcomes. 375 00:13:45,429 --> 00:13:46,170 That becomes 376 00:13:46,629 --> 00:13:48,090 really important to, 377 00:13:48,710 --> 00:13:49,769 coming up with your 378 00:13:50,230 --> 00:13:52,090 your strategies of how to impact 379 00:13:52,605 --> 00:13:53,745 utilization rates 380 00:13:54,205 --> 00:13:55,804 and and and how to really have the 381 00:13:55,804 --> 00:13:56,705 best outcomes, 382 00:13:57,485 --> 00:13:59,804 for these employee groups. And I I I 383 00:13:59,804 --> 00:14:01,164 gotta be honest. It's hard work. 384 00:14:02,044 --> 00:14:04,284 As I said, the there's there's more data. 385 00:14:04,284 --> 00:14:05,644 Like, I I every time I walk in 386 00:14:05,644 --> 00:14:07,649 the mall, there's there's a wearable. Yeah. And 387 00:14:07,649 --> 00:14:09,250 I'm thinking to myself, well, what what could 388 00:14:09,250 --> 00:14:11,089 you do that if you if with that, 389 00:14:11,089 --> 00:14:12,690 if you aggregated it with all the other 390 00:14:12,690 --> 00:14:14,769 data I know of that's out there. It 391 00:14:14,769 --> 00:14:15,829 requires sophistication, 392 00:14:17,089 --> 00:14:19,009 to bring this all together in a meaningful 393 00:14:19,009 --> 00:14:21,965 way. And, you know, some some organizations are 394 00:14:21,965 --> 00:14:23,985 large and they have data science teams, 395 00:14:24,605 --> 00:14:26,524 and they can do this. You know, others 396 00:14:26,524 --> 00:14:28,125 need a partner and they need somebody to 397 00:14:28,125 --> 00:14:29,325 help. And I I just say you have 398 00:14:29,325 --> 00:14:32,125 to really do, an assessment of where you 399 00:14:32,125 --> 00:14:34,144 are and what your capabilities are 400 00:14:34,820 --> 00:14:37,460 because getting that that insight from all that 401 00:14:37,460 --> 00:14:38,360 data aggregation 402 00:14:38,980 --> 00:14:40,740 is really valuable in the long term, and 403 00:14:40,740 --> 00:14:41,799 it's worth the investment. 404 00:14:42,259 --> 00:14:43,860 You'd asked about an example, so I wanna 405 00:14:43,860 --> 00:14:45,299 I wanna touch on that really quick. Yeah. 406 00:14:45,299 --> 00:14:47,264 Yeah. Yeah. Absolutely. Yeah. So we had a 407 00:14:47,345 --> 00:14:49,424 a large employer we were working with who 408 00:14:49,424 --> 00:14:50,325 wanted to understand 409 00:14:51,345 --> 00:14:53,424 why their specialty drug costs were so high. 410 00:14:53,424 --> 00:14:54,945 And this is a common question. I was 411 00:14:54,945 --> 00:14:56,785 in a meeting yesterday with, 412 00:14:57,504 --> 00:14:58,245 a large, 413 00:14:58,625 --> 00:15:00,225 public plan sponsor, and they had they had 414 00:15:00,225 --> 00:15:03,029 the same question, like, specialty drugs, specialty drugs. 415 00:15:03,029 --> 00:15:05,589 So Mhmm. We dug into to their data 416 00:15:05,589 --> 00:15:06,089 and, 417 00:15:07,350 --> 00:15:09,289 we found that, you know, there were conditions 418 00:15:09,429 --> 00:15:11,589 that were driving costs. And when you when 419 00:15:11,589 --> 00:15:13,509 you took all that and looked at other 420 00:15:13,509 --> 00:15:14,009 populations, 421 00:15:14,709 --> 00:15:15,449 we identified 422 00:15:15,909 --> 00:15:18,304 supplementary treatments like physical therapy 423 00:15:18,605 --> 00:15:21,825 or alternatives to the highest cost drug 424 00:15:22,445 --> 00:15:24,845 for conditions like rheumatoid arthritis. And when you 425 00:15:24,845 --> 00:15:26,304 couple those things together 426 00:15:27,085 --> 00:15:28,304 while still evaluating 427 00:15:28,605 --> 00:15:30,065 quality, safety, and effectiveness, 428 00:15:30,980 --> 00:15:33,720 you can save a significant amount of dollars 429 00:15:33,779 --> 00:15:36,579 for, for for your organization. So in this 430 00:15:36,579 --> 00:15:39,299 particular study, they found $50,000,000 431 00:15:39,299 --> 00:15:42,440 of annual savings just by looking at alternative 432 00:15:42,579 --> 00:15:43,079 ways, 433 00:15:43,700 --> 00:15:44,440 to couple 434 00:15:44,955 --> 00:15:47,035 treatment plans and things that we're working in 435 00:15:47,035 --> 00:15:48,815 other areas, again, those benchmarks. 436 00:15:49,195 --> 00:15:51,695 And that's meaningful dollars, right, for an organization 437 00:15:51,754 --> 00:15:53,115 to take back and then can, 438 00:15:53,995 --> 00:15:56,075 use for other benefits or other areas of 439 00:15:56,075 --> 00:15:58,014 care that they're they're needing for their populations. 440 00:15:59,309 --> 00:16:00,910 And, again, like you said, but you can 441 00:16:00,910 --> 00:16:03,570 only find those areas if you're actively engaging 442 00:16:03,870 --> 00:16:05,649 in the data and you have a holistic 443 00:16:05,710 --> 00:16:07,389 view of it. You have a holistic view 444 00:16:07,389 --> 00:16:09,230 of what you have available to be able 445 00:16:09,230 --> 00:16:11,330 to analyze and then then make those decisions. 446 00:16:12,024 --> 00:16:13,945 Marcy, what a great conversation. Thanks so much 447 00:16:13,945 --> 00:16:15,785 for being here. I wanna turn the floor 448 00:16:15,785 --> 00:16:17,144 over to you. Anything else that that you 449 00:16:17,144 --> 00:16:18,745 wanna mention to our audience that we might 450 00:16:18,745 --> 00:16:20,105 have not touched on or anything else that 451 00:16:20,105 --> 00:16:22,285 you think is important for them to know? 452 00:16:22,345 --> 00:16:24,424 Yeah. And, Lucas, I really appreciate the opportunity 453 00:16:24,424 --> 00:16:25,919 to talk with you today. You know, before 454 00:16:25,919 --> 00:16:28,240 I wrap up, I would just say data 455 00:16:28,240 --> 00:16:28,740 sophistication 456 00:16:29,679 --> 00:16:32,799 is essential to lowering cost. So we think 457 00:16:32,799 --> 00:16:34,799 about tools and we think we don't wanna 458 00:16:34,799 --> 00:16:36,179 spend money on the tool. 459 00:16:36,639 --> 00:16:38,320 I gave you a couple of examples today 460 00:16:38,320 --> 00:16:40,485 there. If you just apply the right the 461 00:16:40,485 --> 00:16:42,745 right data and the right trending information, 462 00:16:43,205 --> 00:16:43,705 organizations 463 00:16:44,164 --> 00:16:46,884 can can move the dial on their cost 464 00:16:46,884 --> 00:16:48,345 while improving the outcome 465 00:16:48,804 --> 00:16:51,445 for their employees and their beneficiaries, which is 466 00:16:51,445 --> 00:16:53,845 really what they're looking to do. Everyone's looking 467 00:16:53,845 --> 00:16:56,019 to have high quality of care. 468 00:16:56,399 --> 00:16:58,799 Data sources, as we talked about, are there's 469 00:16:58,799 --> 00:17:01,139 many, many sources in today's world, and 470 00:17:01,440 --> 00:17:03,759 you can no longer make really effective decisions 471 00:17:03,759 --> 00:17:05,140 with one or two data sources. 472 00:17:05,679 --> 00:17:07,519 It's just not enough to meet the challenge. 473 00:17:07,519 --> 00:17:10,179 So aggregating that data, curating that data, 474 00:17:10,894 --> 00:17:13,075 ensuring you have the right level of sophistication 475 00:17:13,214 --> 00:17:13,954 in your organization, 476 00:17:14,575 --> 00:17:16,835 or or asking for help getting sophistication. 477 00:17:17,454 --> 00:17:19,875 Organizations like Truven, this is what we do. 478 00:17:20,414 --> 00:17:22,829 You know, but the cost saving and the 479 00:17:22,829 --> 00:17:25,390 protection of high quality care is worth every 480 00:17:25,390 --> 00:17:27,789 effort in this environment. So I encourage people 481 00:17:27,789 --> 00:17:29,630 just to continue to dig into their data 482 00:17:29,630 --> 00:17:32,190 and look at all all the possible trends 483 00:17:32,190 --> 00:17:34,669 and and continue to, you know, look at 484 00:17:34,669 --> 00:17:36,765 what is best for their employees because no, 485 00:17:37,085 --> 00:17:39,325 no population is the same, and it it 486 00:17:39,325 --> 00:17:41,005 does take work. But the as I said, 487 00:17:41,005 --> 00:17:41,505 the, 488 00:17:42,125 --> 00:17:43,884 the investment is is worth it in the 489 00:17:43,884 --> 00:17:45,265 end for these for these employers. 490 00:17:45,964 --> 00:17:47,884 And don't be afraid to ask for help. 491 00:17:47,884 --> 00:17:49,805 I think that that's really key as well. 492 00:17:49,805 --> 00:17:52,379 Really, really important. Marcy, thanks so much again 493 00:17:52,379 --> 00:17:53,980 for being here and for your time. What 494 00:17:53,980 --> 00:17:56,639 a fantastic conversation. Thank you so much, Lucas. 495 00:17:56,859 --> 00:17:58,539 And we also want to thank our podcast 496 00:17:58,539 --> 00:18:00,220 sponsor, Meredith. You can tune in to more 497 00:18:00,220 --> 00:18:03,019 podcasts from Becker's Healthcare by visiting our podcast 498 00:18:03,019 --> 00:18:06,159 page at beckershospitalreview.com.