1 00:00:00,080 --> 00:00:02,560 Hi, everyone. This is Erica Spicer Mason with 2 00:00:02,560 --> 00:00:05,200 the Becker's Healthcare podcast series. Thank you so 3 00:00:05,200 --> 00:00:07,519 much for tuning in today. I'm thrilled to 4 00:00:07,519 --> 00:00:10,115 be joined by Steve Sutherland, the senior vice 5 00:00:10,115 --> 00:00:12,695 president of information systems at Saris, 6 00:00:13,074 --> 00:00:14,994 who will talk to us about AI and 7 00:00:14,994 --> 00:00:17,654 machine learning in the payment integrity space. 8 00:00:18,410 --> 00:00:20,410 Steve, welcome to the podcast. Thank you so 9 00:00:20,410 --> 00:00:21,789 much for joining us today. 10 00:00:22,410 --> 00:00:24,170 Hello, Erica. Thank you very much for having 11 00:00:24,170 --> 00:00:25,630 me. I appreciate the opportunity. 12 00:00:26,594 --> 00:00:28,914 We're thrilled to have you here to talk 13 00:00:28,914 --> 00:00:30,914 about this topic today. And before we really 14 00:00:30,914 --> 00:00:32,434 get into it, I wanted to see if 15 00:00:32,434 --> 00:00:33,954 you'd like to share just a little bit 16 00:00:33,954 --> 00:00:34,934 more about yourself, 17 00:00:35,350 --> 00:00:36,570 your role, your organization, 18 00:00:36,950 --> 00:00:39,049 whatever feels top of mind for you. 19 00:00:39,990 --> 00:00:42,149 Sure. Sure. Well, I'm a native Texan. I 20 00:00:42,149 --> 00:00:43,750 was born and raised here in in the 21 00:00:43,750 --> 00:00:45,674 Dallas Fort Worth area, and I have been 22 00:00:45,674 --> 00:00:46,174 with, 23 00:00:46,875 --> 00:00:50,475 the Saris Organization now for going on 30 24 00:00:50,475 --> 00:00:52,554 years, actually. I think I'm I think I'll 25 00:00:52,554 --> 00:00:54,335 celebrate my 29th anniversary 26 00:00:55,340 --> 00:00:56,719 in the fall or early 27 00:00:57,100 --> 00:00:59,340 early next year. And I've had I've served 28 00:00:59,340 --> 00:00:59,840 in 29 00:01:00,219 --> 00:01:02,960 a various different roles within the IT spectrum 30 00:01:03,100 --> 00:01:05,435 here with the company, but I really just 31 00:01:05,435 --> 00:01:06,575 have seen the company, 32 00:01:07,515 --> 00:01:10,234 grow exponentially and and really grown along with 33 00:01:10,234 --> 00:01:11,935 it from a very small 34 00:01:12,619 --> 00:01:15,340 organization into, you know, what it is today, 35 00:01:15,340 --> 00:01:17,439 which, you know, we've got almost a 1000 36 00:01:17,659 --> 00:01:20,079 folks within the within the Cirrus 37 00:01:20,465 --> 00:01:21,525 part of the company, 38 00:01:21,825 --> 00:01:23,505 and and we're, you know, a part of 39 00:01:23,505 --> 00:01:25,424 a bigger company, CorVel Corporation. So 40 00:01:26,784 --> 00:01:29,530 Wonderful. Thanks so much, Steve. Appreciate you sharing 41 00:01:29,609 --> 00:01:31,849 a little bit more about your background. It's 42 00:01:31,849 --> 00:01:34,250 incredible. You've been with Cerus for about 30 43 00:01:34,250 --> 00:01:34,750 years. 44 00:01:35,290 --> 00:01:37,390 So I'm sure you've seen so much change 45 00:01:37,450 --> 00:01:38,670 in in those decades. 46 00:01:39,305 --> 00:01:41,704 So I'm really looking forward to the perspective 47 00:01:41,704 --> 00:01:43,084 that you'll have here with 48 00:01:43,465 --> 00:01:45,644 technology and payment integrity. So 49 00:01:46,240 --> 00:01:47,599 I think we can kind of get into 50 00:01:47,599 --> 00:01:48,500 it from there. 51 00:01:49,200 --> 00:01:50,959 So I'm wondering if you can share with 52 00:01:50,959 --> 00:01:53,200 our audience what you see as the role 53 00:01:53,200 --> 00:01:54,500 of artificial intelligence 54 00:01:54,834 --> 00:01:58,354 and machine learning in addressing payment integrity pain 55 00:01:58,354 --> 00:01:58,854 points. 56 00:01:59,555 --> 00:02:01,555 And in your experience working with payers, are 57 00:02:01,555 --> 00:02:04,180 there specific benefits that partners have realized in 58 00:02:04,180 --> 00:02:06,439 applying those tools to claims processes? 59 00:02:07,060 --> 00:02:08,979 Would love to know any success stories or 60 00:02:08,979 --> 00:02:10,680 case studies that might come to mind. 61 00:02:11,814 --> 00:02:13,594 Sure. No. That's a it's a great question 62 00:02:13,814 --> 00:02:16,715 and really a relative and and hot topic 63 00:02:16,775 --> 00:02:18,155 today in today's industry. 64 00:02:18,854 --> 00:02:21,400 And and those technologies are really 65 00:02:21,860 --> 00:02:23,079 helping improve 66 00:02:23,939 --> 00:02:27,935 accuracy and consistency and efficiencies and in payment 67 00:02:27,935 --> 00:02:31,215 processing in general. I mean, they're applicable all 68 00:02:31,215 --> 00:02:33,534 across all sorts of other industries, obviously, as 69 00:02:33,534 --> 00:02:34,995 well. But in payment processing, 70 00:02:35,375 --> 00:02:37,439 you know, payers have a very specific time 71 00:02:37,439 --> 00:02:37,939 frame 72 00:02:38,639 --> 00:02:39,139 within 73 00:02:39,439 --> 00:02:41,540 which they have to make payments to providers 74 00:02:41,599 --> 00:02:43,139 based on contractual 75 00:02:43,520 --> 00:02:44,500 and regulatory 76 00:02:44,800 --> 00:02:47,495 requirements. So they not only have a short 77 00:02:47,495 --> 00:02:47,995 window 78 00:02:48,294 --> 00:02:50,875 to do claim adjudication, but also, 79 00:02:51,655 --> 00:02:53,834 validate and apply various types of 80 00:02:54,409 --> 00:02:57,209 reviews and business logic and and all sorts 81 00:02:57,209 --> 00:02:59,550 of things that happen within the process while, 82 00:02:59,930 --> 00:03:02,324 you know, adhering to those very strict time 83 00:03:02,405 --> 00:03:04,884 time frame. So efficiency really is key. They've 84 00:03:05,044 --> 00:03:06,405 there's a lot to be done in a 85 00:03:06,405 --> 00:03:07,844 short amount of time frame, and it needs 86 00:03:07,844 --> 00:03:09,305 to be done accurately. So 87 00:03:09,909 --> 00:03:11,930 artificial intelligence and machine learning 88 00:03:12,229 --> 00:03:15,370 specifically can really help improve these processes by, 89 00:03:15,669 --> 00:03:18,409 you know, automation. So automating certain 90 00:03:18,905 --> 00:03:21,885 very manual and sometimes inefficient steps. 91 00:03:22,344 --> 00:03:23,085 Claim identification. 92 00:03:23,544 --> 00:03:24,764 So helping 93 00:03:25,064 --> 00:03:27,919 identify and tag or flag problematic 94 00:03:28,780 --> 00:03:31,280 claims or trends that they might see that 95 00:03:31,340 --> 00:03:33,040 it can be taken off of the 96 00:03:33,635 --> 00:03:35,155 conveyor belt, if you will, or out of 97 00:03:35,155 --> 00:03:36,915 the process and looked at by a by 98 00:03:36,915 --> 00:03:38,915 an expert or some some other type of 99 00:03:38,915 --> 00:03:40,375 of audit reviewer. So, 100 00:03:40,675 --> 00:03:42,935 those are just a few of those of, 101 00:03:43,060 --> 00:03:44,739 you know, examples of how it can be 102 00:03:44,739 --> 00:03:45,239 used. 103 00:03:45,620 --> 00:03:48,099 Very general, but we we have several specific 104 00:03:48,099 --> 00:03:49,639 use cases within our workflow 105 00:03:50,099 --> 00:03:52,120 where we've taken very manual processes 106 00:03:52,419 --> 00:03:55,504 like like data capture, you know, keying data 107 00:03:55,504 --> 00:03:57,665 from an image or something and improving that 108 00:03:57,665 --> 00:04:00,564 dramatically by applying this type of technology. 109 00:04:01,990 --> 00:04:04,550 Yeah. I appreciate the example, Steve, and I 110 00:04:04,550 --> 00:04:07,430 think everything you outlined really speaks to what 111 00:04:07,430 --> 00:04:08,169 an impact 112 00:04:08,709 --> 00:04:10,090 efficiency in this process 113 00:04:10,745 --> 00:04:13,224 can play when it comes to compliance, timely 114 00:04:13,224 --> 00:04:15,724 payments, adhering to contractual agreements. 115 00:04:16,105 --> 00:04:17,785 There's a lot kind of on the line 116 00:04:17,785 --> 00:04:19,305 in that in that window of time as 117 00:04:19,305 --> 00:04:19,964 you mentioned. 118 00:04:20,819 --> 00:04:22,500 And I know you also mentioned how payment 119 00:04:22,500 --> 00:04:24,899 integrity plays kind of this key role in 120 00:04:24,899 --> 00:04:27,879 health care where it ensures that claims adjudication 121 00:04:27,939 --> 00:04:30,475 is accurate and that health care organizations are 122 00:04:30,855 --> 00:04:33,514 appropriately reimbursed for the care that they provide. 123 00:04:34,214 --> 00:04:37,254 So as health care billing processes become more 124 00:04:37,254 --> 00:04:37,754 complex, 125 00:04:38,089 --> 00:04:39,930 what key challenges are there when it comes 126 00:04:39,930 --> 00:04:42,330 to the widespread adoption of AI in the 127 00:04:42,330 --> 00:04:43,230 PI space? 128 00:04:43,770 --> 00:04:46,009 And aside from complexity, are there other factors 129 00:04:46,009 --> 00:04:47,629 that are kind of driving those issues? 130 00:04:48,555 --> 00:04:49,435 Sure. Yeah. 131 00:04:50,154 --> 00:04:52,735 There's always challenges, you know, with everything, especially 132 00:04:53,194 --> 00:04:56,074 anything new and and new technologies. So, you 133 00:04:56,074 --> 00:04:57,375 know, applying this 134 00:04:58,060 --> 00:04:58,720 complex technology 135 00:04:59,580 --> 00:05:02,699 to complex billing processes in general and then 136 00:05:02,699 --> 00:05:04,699 also health care data in itself can be 137 00:05:04,699 --> 00:05:05,360 a challenge. 138 00:05:06,060 --> 00:05:06,560 It's 139 00:05:07,125 --> 00:05:07,944 not always 140 00:05:08,485 --> 00:05:11,444 consistent. It's not always standardized based on your 141 00:05:11,444 --> 00:05:13,365 billing practices and those sorts of things. So 142 00:05:13,365 --> 00:05:15,204 the data working with the data in itself 143 00:05:15,204 --> 00:05:15,865 can be 144 00:05:16,289 --> 00:05:17,029 very complicated 145 00:05:17,729 --> 00:05:18,470 and challenging. 146 00:05:19,250 --> 00:05:20,470 So there are certain 147 00:05:20,930 --> 00:05:21,409 pitfall 148 00:05:21,810 --> 00:05:23,970 or many pitfalls you want to try to 149 00:05:23,970 --> 00:05:26,654 avoid. A very important one is to exercise 150 00:05:26,654 --> 00:05:28,814 caution and and be diligent when it comes 151 00:05:28,814 --> 00:05:30,435 to data privacy and security, 152 00:05:31,214 --> 00:05:33,394 as well as transparency and defensibility. 153 00:05:33,694 --> 00:05:36,479 So those all kind of, you know, go 154 00:05:36,479 --> 00:05:37,379 along together, 155 00:05:37,839 --> 00:05:38,339 regulatory 156 00:05:38,720 --> 00:05:40,180 challenges in some cases. 157 00:05:40,560 --> 00:05:43,395 So really relying on your compliance and security 158 00:05:43,395 --> 00:05:44,375 teams to provide 159 00:05:44,754 --> 00:05:46,935 a sound governance and policy, 160 00:05:47,634 --> 00:05:48,935 when you go into these 161 00:05:49,395 --> 00:05:51,735 deploying these types of solutions. Really, you need 162 00:05:51,795 --> 00:05:53,839 to need to look at that upfront and 163 00:05:53,839 --> 00:05:55,220 really have that established. 164 00:05:56,080 --> 00:05:59,060 There can also be a steep learning curve 165 00:05:59,199 --> 00:06:01,034 with staff and finding, 166 00:06:01,735 --> 00:06:02,235 expertise 167 00:06:03,094 --> 00:06:03,995 and resources 168 00:06:04,615 --> 00:06:06,454 to be able to to build these types 169 00:06:06,454 --> 00:06:08,699 of solutions and have the knowledge and and 170 00:06:08,699 --> 00:06:11,500 expertise. So really just have patience and be 171 00:06:11,500 --> 00:06:14,620 persistent as you try to recruit talent and 172 00:06:14,620 --> 00:06:17,935 expertise and and also train current staff on 173 00:06:18,095 --> 00:06:19,235 learning these technologies. 174 00:06:19,935 --> 00:06:21,394 There can also be some gaps 175 00:06:21,774 --> 00:06:22,175 with, 176 00:06:22,735 --> 00:06:24,735 the output of some of these solutions and 177 00:06:24,735 --> 00:06:25,235 tools. 178 00:06:25,829 --> 00:06:28,009 So as you deploy these things, 179 00:06:28,389 --> 00:06:30,550 you know, what the analysis showed or what 180 00:06:30,550 --> 00:06:31,129 the research 181 00:06:31,589 --> 00:06:33,050 showed might not necessarily 182 00:06:33,350 --> 00:06:34,169 come to fruition 183 00:06:34,785 --> 00:06:36,725 when you put it in in an operational 184 00:06:36,865 --> 00:06:38,865 setting and the actual results might be off. 185 00:06:38,865 --> 00:06:40,004 So really just having, 186 00:06:40,705 --> 00:06:43,504 a continuous improvement and validation process so that 187 00:06:43,504 --> 00:06:45,889 you're always making sure that, you know, the 188 00:06:45,889 --> 00:06:47,810 output is what it should be and that 189 00:06:47,810 --> 00:06:50,310 it's valid and continually improving 190 00:06:50,769 --> 00:06:53,509 whether it's a a machine learning model or, 191 00:06:53,764 --> 00:06:54,504 know, an 192 00:06:54,884 --> 00:06:55,865 AI, solution 193 00:06:56,324 --> 00:06:57,064 in itself. 194 00:06:57,925 --> 00:07:00,405 Mhmm. And probably the last thing I'd mention 195 00:07:00,405 --> 00:07:03,550 is just is just to avoid becoming too 196 00:07:03,550 --> 00:07:05,170 reliant on technology, 197 00:07:05,870 --> 00:07:08,910 on this specific technology or any really in 198 00:07:08,910 --> 00:07:09,410 general 199 00:07:09,894 --> 00:07:12,714 over, you know, human knowledge and expertise. So 200 00:07:12,774 --> 00:07:15,415 finding that good balance between, you know, how 201 00:07:15,415 --> 00:07:18,199 the technology can be used and what parts 202 00:07:18,199 --> 00:07:18,939 of processes 203 00:07:19,240 --> 00:07:22,439 we can make more efficient and take and 204 00:07:22,439 --> 00:07:24,279 take the human element out of it. But 205 00:07:24,279 --> 00:07:27,254 then, you know, finding that balance of there's 206 00:07:27,254 --> 00:07:28,394 always going to 207 00:07:28,774 --> 00:07:30,774 be some of that that that really does 208 00:07:30,774 --> 00:07:31,914 need a human touch. 209 00:07:32,920 --> 00:07:36,759 Yeah. That balanced approach sounds essential, and I 210 00:07:36,759 --> 00:07:38,439 appreciate what you said at the beginning of 211 00:07:38,439 --> 00:07:41,625 your response, Steve, that applying complex technology to 212 00:07:41,625 --> 00:07:43,404 already complex billing processes 213 00:07:44,105 --> 00:07:46,125 bound to have some challenges and barriers. 214 00:07:46,504 --> 00:07:48,600 So I appreciate you outlining some of those 215 00:07:48,839 --> 00:07:51,959 kind of pitfalls that organizations can avoid and 216 00:07:51,959 --> 00:07:54,139 acknowledging there will be a steep learning curve. 217 00:07:54,439 --> 00:07:57,100 The continuous improvement and validation is key. 218 00:07:57,415 --> 00:07:59,595 So, yeah, thank you again for for outlining 219 00:07:59,654 --> 00:08:01,014 all of that. And I think that leads 220 00:08:01,014 --> 00:08:02,855 me into the next question that I had 221 00:08:02,855 --> 00:08:03,675 for you, which 222 00:08:04,214 --> 00:08:06,110 is, you know, just kind of acknowledging this 223 00:08:06,110 --> 00:08:09,490 idea of change management and demonstrating value anytime 224 00:08:09,789 --> 00:08:10,689 leaders are 225 00:08:11,149 --> 00:08:13,654 kind of encouraging their teams embrace new technology. 226 00:08:13,794 --> 00:08:15,394 We hear from leaders all the time that 227 00:08:15,394 --> 00:08:17,475 change management is one of the the most 228 00:08:17,475 --> 00:08:20,139 challenging aspects of introducing new tech. 229 00:08:20,780 --> 00:08:23,340 So wondering if you have best practices or 230 00:08:23,340 --> 00:08:25,580 resources that you'd recommend to leaders who are 231 00:08:25,580 --> 00:08:28,455 planning to or have even newly adopted AI 232 00:08:28,455 --> 00:08:30,395 and machine learning for payment integrity. 233 00:08:31,014 --> 00:08:32,615 And maybe you can say a few words 234 00:08:32,615 --> 00:08:35,355 on how they can prepare for future advancements, 235 00:08:35,894 --> 00:08:37,259 and other changes ahead. 236 00:08:38,220 --> 00:08:40,700 Definitely. I can share some thoughts and and 237 00:08:40,700 --> 00:08:43,100 and some of my experiences in in that 238 00:08:43,100 --> 00:08:44,000 space. So 239 00:08:44,620 --> 00:08:45,759 I I always say 240 00:08:46,394 --> 00:08:48,815 and and have found based on our experience, 241 00:08:49,274 --> 00:08:51,934 identifying the right use cases upfront. 242 00:08:52,315 --> 00:08:54,500 These technologies can be used to do 243 00:08:54,980 --> 00:08:57,059 a plethora of things. There's a there's a 244 00:08:57,059 --> 00:08:59,700 1,000,000 use cases. Right? But really identifying what 245 00:08:59,700 --> 00:09:01,725 is the right use case to start with. 246 00:09:01,804 --> 00:09:03,485 So if you're just getting into this, how 247 00:09:03,485 --> 00:09:04,764 do I how do I get my foot 248 00:09:04,764 --> 00:09:07,164 in the door? What can I do? What 249 00:09:07,164 --> 00:09:08,704 you don't want to do is 250 00:09:09,120 --> 00:09:10,980 bite off the most complex 251 00:09:12,000 --> 00:09:14,399 project that that's out there that's on your 252 00:09:14,399 --> 00:09:14,899 radar. 253 00:09:15,279 --> 00:09:16,259 Keep it simple. 254 00:09:16,705 --> 00:09:18,245 Find a very 255 00:09:18,784 --> 00:09:19,284 simple, 256 00:09:20,065 --> 00:09:23,264 easy use case to start with. Don't try 257 00:09:23,264 --> 00:09:26,250 to tackle that most complex solution first, but 258 00:09:26,250 --> 00:09:27,929 keep it simple. And then that way, you 259 00:09:27,929 --> 00:09:29,769 can get, you know, get your foot in 260 00:09:29,769 --> 00:09:31,690 the door, get your feet wet. You can 261 00:09:31,690 --> 00:09:34,029 you can get some experience and have some 262 00:09:34,215 --> 00:09:36,535 some quick wins without, you know, having to 263 00:09:36,535 --> 00:09:38,475 put in all of the time and effort 264 00:09:38,774 --> 00:09:40,695 before you're able to see sort of the 265 00:09:40,695 --> 00:09:42,154 fruits of your labor. Right? 266 00:09:42,670 --> 00:09:44,590 So the and then building proof of concepts, 267 00:09:44,590 --> 00:09:46,670 it's kind of the same it's kind of 268 00:09:46,670 --> 00:09:48,910 the same thought process. Right? So let's build 269 00:09:48,910 --> 00:09:49,970 a proof of concept 270 00:09:50,430 --> 00:09:52,684 on this use case, prove it out, 271 00:09:53,065 --> 00:09:55,465 get your feet wet, you know, make sure 272 00:09:55,465 --> 00:09:58,205 that it that it proves out what your 273 00:09:58,504 --> 00:10:00,450 goal was to begin with and that and 274 00:10:00,450 --> 00:10:02,610 then you can lower your risks and and 275 00:10:02,610 --> 00:10:04,610 increase your success by having those proof of 276 00:10:04,610 --> 00:10:06,529 concepts because you're gonna have some failures. Right? 277 00:10:06,529 --> 00:10:08,504 So if you build a proof of concept 278 00:10:08,504 --> 00:10:10,585 and it doesn't quite work the way you 279 00:10:10,585 --> 00:10:11,404 thought it would, 280 00:10:11,785 --> 00:10:14,425 then, you know, you sort of back up, 281 00:10:14,425 --> 00:10:17,480 make some adjustments, and and start again. So 282 00:10:18,100 --> 00:10:19,620 we we always like to do proof of 283 00:10:19,620 --> 00:10:20,120 concepts 284 00:10:20,580 --> 00:10:21,800 on especially 285 00:10:22,259 --> 00:10:25,095 complex projects like this or or with the 286 00:10:25,095 --> 00:10:26,554 potential to be really complex. 287 00:10:27,575 --> 00:10:29,335 One of the other things is to get 288 00:10:29,335 --> 00:10:31,575 early buy in from key stakeholders. I mean, 289 00:10:31,575 --> 00:10:34,000 this may sound like a a no brainer, 290 00:10:34,059 --> 00:10:36,300 but you really gotta make sure that you've 291 00:10:36,300 --> 00:10:38,540 got your business strategy and your and your 292 00:10:38,540 --> 00:10:41,804 technology initiatives aligned so that everybody is on 293 00:10:41,804 --> 00:10:43,725 the same page as far as, you know, 294 00:10:43,725 --> 00:10:45,825 what the goals are and what the expectations 295 00:10:45,965 --> 00:10:46,465 are. 296 00:10:47,325 --> 00:10:48,304 Like a lot of 297 00:10:48,709 --> 00:10:51,190 current technologies out there, data really is the 298 00:10:51,190 --> 00:10:51,690 key. 299 00:10:52,309 --> 00:10:55,610 So having good sound data government management 300 00:10:56,295 --> 00:10:58,394 around that data and then the architecture. 301 00:10:59,175 --> 00:11:00,075 The old saying, 302 00:11:00,455 --> 00:11:02,695 garbage in and garbage out definitely applies in 303 00:11:02,695 --> 00:11:05,079 this scenario. You've gotta have good data, and 304 00:11:05,079 --> 00:11:07,240 you've gotta have a good strategy and management 305 00:11:07,240 --> 00:11:08,220 around that data. 306 00:11:08,600 --> 00:11:10,039 And I guess the last thing I would 307 00:11:10,039 --> 00:11:11,500 say is just to educate, 308 00:11:12,425 --> 00:11:14,825 your staff and and your business units about 309 00:11:14,825 --> 00:11:16,845 these technologies, about data science, 310 00:11:17,705 --> 00:11:20,605 explaining these use cases and proof of concepts 311 00:11:20,665 --> 00:11:23,350 and, you know, getting folks to buy in 312 00:11:23,730 --> 00:11:25,830 and and get excited about these things. 313 00:11:26,210 --> 00:11:29,370 Training always helps with the learning curve. But, 314 00:11:29,370 --> 00:11:32,115 I mean, having some positive outcomes and realizing 315 00:11:32,115 --> 00:11:35,554 value really will increase your excitement and building 316 00:11:35,554 --> 00:11:38,215 trust in these these new tools and technologies 317 00:11:38,274 --> 00:11:39,095 and the processes. 318 00:11:40,950 --> 00:11:43,269 Thanks so much, Steve. I think this is 319 00:11:43,269 --> 00:11:46,230 really great advice, and I especially appreciate that 320 00:11:46,230 --> 00:11:48,230 point that you said about keeping it simple, 321 00:11:48,230 --> 00:11:48,730 especially 322 00:11:49,269 --> 00:11:52,534 in the beginning of deploying this technology, perhaps 323 00:11:52,534 --> 00:11:54,054 an organization is doing it for the first 324 00:11:54,054 --> 00:11:54,794 time and, 325 00:11:55,414 --> 00:11:57,174 keeping it simple can help help them get 326 00:11:57,174 --> 00:11:59,159 those quick wins, which I imagine would go 327 00:11:59,159 --> 00:12:01,519 a long way in demonstrating value upfront and 328 00:12:01,519 --> 00:12:03,620 kind of justifying the investment. So, 329 00:12:04,159 --> 00:12:06,659 again, great advice for our listeners. And 330 00:12:07,014 --> 00:12:09,654 before we end our time together today, is 331 00:12:09,654 --> 00:12:11,495 there anything else you wanted to share that 332 00:12:11,495 --> 00:12:14,134 maybe we didn't cover already or any final 333 00:12:14,134 --> 00:12:16,394 takeaways that you'd like our audience to know? 334 00:12:17,669 --> 00:12:19,909 No. Maybe maybe just reiterate, you know, kinda 335 00:12:19,909 --> 00:12:21,429 what you just said, you know, making sure 336 00:12:21,429 --> 00:12:22,889 that your strategy and technology 337 00:12:23,509 --> 00:12:25,750 initiatives are aligned, getting that early buy in, 338 00:12:25,750 --> 00:12:27,815 and building bonds with your key stakeholders. 339 00:12:28,274 --> 00:12:30,134 Keep it simple. Keep it flexible. 340 00:12:30,595 --> 00:12:32,134 You know, the right use cases, 341 00:12:32,674 --> 00:12:35,379 proof of concepts, those quick wins, build some 342 00:12:35,379 --> 00:12:36,519 momentum gradually. 343 00:12:37,220 --> 00:12:38,519 The quicker you can demonstrate 344 00:12:38,899 --> 00:12:42,039 value, the quicker you're gonna get not only 345 00:12:42,339 --> 00:12:42,839 management, 346 00:12:43,274 --> 00:12:45,514 buy in, and confidence, but also your end 347 00:12:45,514 --> 00:12:47,274 users, the folks who are actually going to 348 00:12:47,274 --> 00:12:48,575 be using these solutions, 349 00:12:49,674 --> 00:12:51,695 and show that value within the organization. 350 00:12:52,529 --> 00:12:53,970 And just just to kinda wrap it up, 351 00:12:53,970 --> 00:12:56,049 I mean, you know, Saris I've been, you 352 00:12:56,049 --> 00:12:57,490 know, in the business for, like I said, 353 00:12:57,490 --> 00:12:59,250 for 30 years with Saris. I mean, we 354 00:12:59,250 --> 00:13:01,825 we are a technology leader in the PI 355 00:13:01,825 --> 00:13:03,904 space, and we've been doing this for a 356 00:13:03,904 --> 00:13:06,325 long time. So I would just, you know, 357 00:13:06,465 --> 00:13:08,625 extend the offer to reach out if if 358 00:13:08,625 --> 00:13:10,360 there are any questions or anything that, you 359 00:13:10,360 --> 00:13:11,800 know, I could assist with, and, 360 00:13:12,519 --> 00:13:14,060 my door is always open. 361 00:13:15,399 --> 00:13:15,899 Fantastic. 362 00:13:16,279 --> 00:13:19,644 Thank you so much, Steve. Really appreciate the 363 00:13:19,644 --> 00:13:21,725 time and all of the insights today. It's 364 00:13:21,725 --> 00:13:22,705 been a great discussion. 365 00:13:23,404 --> 00:13:25,644 Thank you very much, Eric. I've I've enjoyed 366 00:13:25,644 --> 00:13:27,789 it, and, it's a it's been a pleasure. 367 00:13:28,569 --> 00:13:30,569 Well, thank you again, Steve. And we'd also 368 00:13:30,569 --> 00:13:33,424 like to thank Saris for sponsoring today's episode. 369 00:13:33,985 --> 00:13:36,065 You can tune into our podcast from Becker's 370 00:13:36,065 --> 00:13:39,044 Healthcare by visiting our podcast page at beckershospitalreview.com.