1 00:00:00,080 --> 00:00:03,040 Hi, everyone. This is Erica Carbajal with Becker's 2 00:00:03,040 --> 00:00:03,540 Healthcare. 3 00:00:04,080 --> 00:00:05,599 Thank you so much for tuning in to 4 00:00:05,599 --> 00:00:08,900 this episode of the Becker's Healthcare podcast series. 5 00:00:09,359 --> 00:00:12,419 Today, we're gonna be talking about scaling smarter, 6 00:00:12,559 --> 00:00:16,135 how automation is redefining revenue for surgical centers. 7 00:00:16,515 --> 00:00:18,835 And joining me for today's discussion is Aman 8 00:00:18,835 --> 00:00:19,335 Magoon, 9 00:00:19,714 --> 00:00:21,894 cofounder and chief of product at Adonis. 10 00:00:22,274 --> 00:00:24,355 Aman, thanks so much for being on. Excited 11 00:00:24,355 --> 00:00:25,875 to have you. Thank you. Thank you for 12 00:00:25,875 --> 00:00:28,349 having me. Excited for the conversation. Yeah. Really 13 00:00:28,349 --> 00:00:30,509 relevant topic. But before we get into it, 14 00:00:30,509 --> 00:00:31,949 do you mind just sharing a little bit 15 00:00:31,949 --> 00:00:34,109 about yourself, your work in health care, and 16 00:00:34,109 --> 00:00:36,509 Adonis? Yeah. Absolutely. So as you mentioned, my 17 00:00:36,509 --> 00:00:38,509 name is Aman. I'm the chief product officer 18 00:00:38,509 --> 00:00:40,369 at Adonis. I'm also one of the cofounders 19 00:00:40,509 --> 00:00:41,009 alongside 20 00:00:41,375 --> 00:00:43,534 my younger brother, Akash. Him and I are 21 00:00:43,534 --> 00:00:46,414 second time entrepreneurs in the health tech space. 22 00:00:46,414 --> 00:00:48,114 So the first company we started, 23 00:00:48,655 --> 00:00:50,354 in 2019 was helping 24 00:00:50,734 --> 00:00:53,134 solve a different challenge at the intersection of 25 00:00:53,134 --> 00:00:54,000 insurance and technology, 26 00:00:55,200 --> 00:00:57,200 picking your benefits during open enrollment if you're 27 00:00:57,200 --> 00:00:58,659 an employee of a large company. 28 00:00:59,200 --> 00:01:00,259 And so our software 29 00:01:00,640 --> 00:01:02,880 in our first business, which was called NAYYA, 30 00:01:02,880 --> 00:01:05,120 n a y y a, was really helping 31 00:01:05,120 --> 00:01:07,519 everyday American employees pick their insurance plans during 32 00:01:07,519 --> 00:01:09,144 open enrollment in in a way that was 33 00:01:09,144 --> 00:01:11,305 very personalized to their financial needs and their 34 00:01:11,305 --> 00:01:12,364 physical health needs. 35 00:01:12,744 --> 00:01:15,784 And so that company building experience was really 36 00:01:15,784 --> 00:01:18,185 transformative in a number of ways, primarily in 37 00:01:18,185 --> 00:01:21,064 helping us understand the different challenges that those 38 00:01:21,064 --> 00:01:22,444 that interface insurance, 39 00:01:23,064 --> 00:01:23,564 are 40 00:01:24,250 --> 00:01:25,629 encountering on a daily basis, 41 00:01:26,009 --> 00:01:28,650 particularly the provider challenge of revenue cycle. And 42 00:01:28,650 --> 00:01:31,629 so over the course of our first business, 43 00:01:31,930 --> 00:01:33,769 which we spent about three years building before 44 00:01:33,769 --> 00:01:34,430 we exited, 45 00:01:34,890 --> 00:01:36,189 we learned a ton about 46 00:01:36,884 --> 00:01:38,825 revenue cycle and and the very intricate 47 00:01:39,125 --> 00:01:41,224 challenges in the nature of revenue cycle issues, 48 00:01:41,685 --> 00:01:43,444 between the payer and the provider, and that 49 00:01:43,444 --> 00:01:45,844 was really the inspiration for starting Adonis. So 50 00:01:45,844 --> 00:01:47,844 we've been building this company for about three 51 00:01:47,844 --> 00:01:50,564 years now as well. We've raised about $50,000,000 52 00:01:50,564 --> 00:01:52,340 across a number of different rounds of funding, 53 00:01:52,340 --> 00:01:54,280 and we're headquartered in in New York City. 54 00:01:54,659 --> 00:01:55,159 The 55 00:01:55,780 --> 00:01:58,579 central premise behind starting the company is really 56 00:01:58,579 --> 00:02:01,640 helping initially helping revenue cycle teams pinpoint 57 00:02:02,019 --> 00:02:03,000 root cause issues, 58 00:02:03,700 --> 00:02:05,775 that told the story of what was breaking 59 00:02:05,775 --> 00:02:08,014 across their revenue cycle organization on a daily 60 00:02:08,014 --> 00:02:08,514 basis, 61 00:02:08,814 --> 00:02:10,414 similar to how you might have fraud detection 62 00:02:10,414 --> 00:02:11,775 if you use a credit card or you 63 00:02:11,775 --> 00:02:12,435 might have 64 00:02:12,735 --> 00:02:14,414 threat detection if you have an alarm system 65 00:02:14,414 --> 00:02:15,854 in your house. You wanna know when a 66 00:02:15,854 --> 00:02:18,259 intruder's on the way. And so that was 67 00:02:18,259 --> 00:02:19,879 really our core focus was specifying, 68 00:02:20,740 --> 00:02:21,560 with a very 69 00:02:22,099 --> 00:02:22,599 distinct 70 00:02:23,139 --> 00:02:25,139 amount of detail the issues that teams are 71 00:02:25,139 --> 00:02:26,580 facing on a daily basis so that they 72 00:02:26,580 --> 00:02:27,240 could then 73 00:02:28,020 --> 00:02:29,719 affect those, issues. 74 00:02:30,395 --> 00:02:33,135 And more recently, given the proliferation of AI, 75 00:02:33,194 --> 00:02:35,435 we're not only identifying threats, we're actually mitigating 76 00:02:35,435 --> 00:02:37,375 them through a number of different modalities. 77 00:02:38,314 --> 00:02:40,155 Yeah. Thanks for sharing all that. It's interesting 78 00:02:40,155 --> 00:02:41,295 of the comparison 79 00:02:41,915 --> 00:02:43,040 you make of of kind of how it 80 00:02:43,040 --> 00:02:44,879 works with the credit card and the proactive 81 00:02:44,879 --> 00:02:46,580 nature of this, of really 82 00:02:46,879 --> 00:02:49,360 pinpointing those root cause issues, and imagine it's 83 00:02:49,360 --> 00:02:50,659 fun working with your brother. 84 00:02:51,199 --> 00:02:52,879 Yeah. It's a delight. We we get along 85 00:02:52,879 --> 00:02:56,205 really well. Yeah. Well, administrative costs already came 86 00:02:56,205 --> 00:02:58,365 up, continue to be a financial burden for 87 00:02:58,365 --> 00:03:00,844 organizations and and a potential threat to patient 88 00:03:00,844 --> 00:03:02,465 care in some cases. So 89 00:03:02,844 --> 00:03:05,564 what are the most time intensive or error 90 00:03:05,564 --> 00:03:09,180 prone RCM workflows that AI agents can actually 91 00:03:09,180 --> 00:03:11,919 automate today? And how should leaders prioritize 92 00:03:12,219 --> 00:03:13,520 what does get automated? 93 00:03:14,219 --> 00:03:15,580 Yeah. It's a great question and one that 94 00:03:15,580 --> 00:03:17,120 we think about quite often. 95 00:03:17,580 --> 00:03:19,840 I like to call it the standard operating 96 00:03:20,125 --> 00:03:22,525 procedure for revenue cycle really consists of four 97 00:03:22,525 --> 00:03:23,664 or five core steps. 98 00:03:24,284 --> 00:03:25,584 The first step is usually 99 00:03:25,965 --> 00:03:28,284 fetching information from one spot and moving it 100 00:03:28,284 --> 00:03:30,205 into another spot. Let's say if you're adding 101 00:03:30,205 --> 00:03:31,884 a modifier to a claim, but you don't 102 00:03:31,884 --> 00:03:33,405 know about a payer policy change, maybe you're 103 00:03:33,405 --> 00:03:34,979 doing a little bit of research on a 104 00:03:35,060 --> 00:03:36,840 on a payer policy and then 105 00:03:37,219 --> 00:03:38,739 making an update to a claim. So you're 106 00:03:38,739 --> 00:03:40,759 moving information from point a to point b 107 00:03:40,819 --> 00:03:43,060 as, like, job number one. Job number two 108 00:03:43,060 --> 00:03:44,759 is fetching information from, 109 00:03:45,459 --> 00:03:46,979 the payer. So it might be through a 110 00:03:46,979 --> 00:03:49,645 phone call or a portal based intervention that 111 00:03:49,645 --> 00:03:51,004 you might have to make if you're on 112 00:03:51,004 --> 00:03:52,925 a revenue cycle team as, like, job number 113 00:03:52,925 --> 00:03:55,245 two, fetching information through the phone or through 114 00:03:55,245 --> 00:03:55,825 the portal. 115 00:03:56,205 --> 00:03:59,025 The third is document transmission. So, like, taking 116 00:03:59,405 --> 00:04:01,745 medical records and moving them from an EHR 117 00:04:01,805 --> 00:04:03,665 to a payer portal, for example. 118 00:04:04,280 --> 00:04:06,199 And the fourth is typically some sort of 119 00:04:06,199 --> 00:04:09,099 patient outreach, whether it's previsit to get eligibility 120 00:04:09,159 --> 00:04:10,699 or prior auth related details 121 00:04:11,080 --> 00:04:13,800 or postvisit to inform them on their patient 122 00:04:13,800 --> 00:04:16,294 responsibility or any other follow ups. And so 123 00:04:16,294 --> 00:04:18,134 if you think about those four core steps, 124 00:04:18,134 --> 00:04:19,894 you can assemble them in a number of 125 00:04:19,894 --> 00:04:23,254 different ways to recreate basically any standard operating 126 00:04:23,254 --> 00:04:25,595 procedure, business procedure within a revenue cycle. 127 00:04:26,055 --> 00:04:27,735 And what's interesting is that each of those 128 00:04:27,735 --> 00:04:28,235 four 129 00:04:28,810 --> 00:04:31,209 jobs to be done can almost effectively be 130 00:04:31,209 --> 00:04:31,949 replaced by 131 00:04:32,329 --> 00:04:35,129 artificial intelligence now. The challenge really is how 132 00:04:35,129 --> 00:04:37,370 do you stitch together the right actions and 133 00:04:37,370 --> 00:04:40,250 orchestrate the right combination of those four things 134 00:04:40,250 --> 00:04:42,169 in a way that actually recreates that standard 135 00:04:42,169 --> 00:04:44,675 operating procedure. And that's really where our technology 136 00:04:44,814 --> 00:04:47,694 specializes in the orchestration of AI and is 137 00:04:47,694 --> 00:04:49,154 less focused on the actual 138 00:04:49,774 --> 00:04:51,694 verb, if you will, the phone call or 139 00:04:51,694 --> 00:04:54,654 the portal upload or the patient outreach. Like, 140 00:04:54,654 --> 00:04:55,714 those are really exciting 141 00:04:56,360 --> 00:04:58,279 verbs. But in order to tell a complete 142 00:04:58,279 --> 00:05:00,360 story, in order to speak a full sentence, 143 00:05:00,360 --> 00:05:01,879 you'd you can't just use a verb. You 144 00:05:01,879 --> 00:05:04,120 have to be able to specify a subject 145 00:05:04,120 --> 00:05:05,639 and a noun and a predicate or whatever 146 00:05:05,639 --> 00:05:07,160 it is. I'm I didn't really do that 147 00:05:07,160 --> 00:05:08,854 well in English. But, yeah, that's really what 148 00:05:08,854 --> 00:05:10,535 our technology is meant to do is take 149 00:05:10,535 --> 00:05:13,014 those actions, but turn them into actual business 150 00:05:13,014 --> 00:05:14,774 processes. Yeah. It makes sense. And kind of 151 00:05:14,774 --> 00:05:17,495 like along the continuum versus kind of this 152 00:05:17,574 --> 00:05:20,235 the siloed actions with AI can help streamline 153 00:05:20,294 --> 00:05:22,714 the whole process. Can you share an example 154 00:05:22,774 --> 00:05:24,720 of what this kind of looks like in 155 00:05:24,720 --> 00:05:27,040 practice? What you just mentioned about those those 156 00:05:27,040 --> 00:05:28,899 core steps and how the AI can help. 157 00:05:29,040 --> 00:05:31,759 Totally. So one of the costliest things within 158 00:05:31,759 --> 00:05:34,959 revenue cycle is working aged AR. You know, 159 00:05:34,959 --> 00:05:36,579 if you're a revenue cycle team, 160 00:05:37,064 --> 00:05:39,225 you're almost always tasked or you have someone 161 00:05:39,225 --> 00:05:41,465 on your team that's working that ninety day 162 00:05:41,465 --> 00:05:42,685 bucket and is prioritizing, 163 00:05:43,305 --> 00:05:45,705 let's say, claims with a higher dollar amount 164 00:05:45,705 --> 00:05:47,865 relative to what's average. Right? And so you're 165 00:05:47,865 --> 00:05:49,865 working through a a stack ranking of maybe 166 00:05:49,865 --> 00:05:51,545 thousands of claims depending on the size of 167 00:05:51,545 --> 00:05:52,205 your organization 168 00:05:52,589 --> 00:05:54,290 that are sitting in this age bucket. 169 00:05:55,069 --> 00:05:56,529 And typically, the first step 170 00:05:56,910 --> 00:05:59,310 to to recovering those those proceeds or those 171 00:05:59,310 --> 00:06:00,990 funds is to do some sort of forensic 172 00:06:00,990 --> 00:06:02,750 analysis on what the actual status of that 173 00:06:02,750 --> 00:06:03,729 claim might be. 174 00:06:04,269 --> 00:06:05,329 In the old world, 175 00:06:05,709 --> 00:06:07,009 statusing each of those 176 00:06:07,524 --> 00:06:09,865 thousand several thousand claims would require 177 00:06:10,564 --> 00:06:12,564 taking the claim level details, going into the 178 00:06:12,564 --> 00:06:14,964 payer portal, searching for that claim ID, hopefully 179 00:06:14,964 --> 00:06:17,204 getting a response back, and getting enough granular 180 00:06:17,204 --> 00:06:17,704 detail 181 00:06:18,245 --> 00:06:19,845 on the outstanding status of the claim so 182 00:06:19,845 --> 00:06:22,050 that you can then resubmit it or take 183 00:06:22,050 --> 00:06:23,810 some sort of action to get that revenue 184 00:06:23,810 --> 00:06:24,310 recovered. 185 00:06:25,410 --> 00:06:27,649 But that forensic analysis or that research was 186 00:06:27,649 --> 00:06:29,410 highly costly. It would take, you know, ten 187 00:06:29,410 --> 00:06:30,930 to fifteen minutes to get into the portal, 188 00:06:30,930 --> 00:06:32,610 and then it would take maybe ten to 189 00:06:32,610 --> 00:06:34,290 fifteen minutes to actually make sense of the 190 00:06:34,290 --> 00:06:35,509 information in the portal. 191 00:06:36,074 --> 00:06:38,495 And should that information not be comprehensive enough, 192 00:06:38,555 --> 00:06:39,835 you're then having to pick up the phone 193 00:06:39,835 --> 00:06:41,835 and call the payer. And so one example 194 00:06:41,835 --> 00:06:42,814 of how AI 195 00:06:43,835 --> 00:06:45,855 specifically deployed through Adonis Intelligence 196 00:06:46,314 --> 00:06:49,134 is intervening and actually creating economies of scale 197 00:06:49,435 --> 00:06:49,935 is 198 00:06:50,329 --> 00:06:53,610 basically replacing that entire forensic process by using 199 00:06:53,610 --> 00:06:55,050 the payer phone call as the first line 200 00:06:55,050 --> 00:06:55,790 of defense. 201 00:06:56,410 --> 00:06:58,009 We all know that the phone call to 202 00:06:58,009 --> 00:06:59,949 the payer is oftentimes the most comprehensive 203 00:07:00,490 --> 00:07:02,250 way of getting information, but it used to 204 00:07:02,250 --> 00:07:03,230 be the most expensive. 205 00:07:04,274 --> 00:07:05,794 And what AI has allowed us to do 206 00:07:05,794 --> 00:07:08,115 is take that most comprehensive method, the phone 207 00:07:08,115 --> 00:07:10,115 call, and actually make it the least expensive. 208 00:07:10,115 --> 00:07:12,134 So it's becoming the first resort, 209 00:07:12,834 --> 00:07:14,354 you know, in in how we think about 210 00:07:14,354 --> 00:07:16,055 working AR, where 211 00:07:17,620 --> 00:07:20,379 in kind of yesteryear, the status quo is 212 00:07:20,379 --> 00:07:22,259 is usually the last and final resort. So 213 00:07:22,259 --> 00:07:24,579 that's one example of how this technology is 214 00:07:24,579 --> 00:07:25,079 really 215 00:07:25,379 --> 00:07:27,479 changing the paradigm of how revenue cycle 216 00:07:27,779 --> 00:07:30,259 can operate and certainly changing the the paradigm 217 00:07:30,259 --> 00:07:30,759 around 218 00:07:32,125 --> 00:07:34,704 the efficiency associated with collecting aged AR. 219 00:07:35,084 --> 00:07:36,764 Yeah. And it's it's interesting how the it 220 00:07:36,764 --> 00:07:38,604 kind of flips the process on its head 221 00:07:38,604 --> 00:07:41,004 and, as you said, like, goes straight to 222 00:07:41,004 --> 00:07:42,764 calling the payer as the first line of 223 00:07:42,764 --> 00:07:43,259 defense. 224 00:07:43,980 --> 00:07:46,480 On the topic of of payers, the requirements, 225 00:07:46,620 --> 00:07:49,819 documentation standards, they're complex. They continue to evolve, 226 00:07:49,819 --> 00:07:52,300 something we hear from all health system leaders 227 00:07:52,300 --> 00:07:53,600 that we we talk to. 228 00:07:53,900 --> 00:07:57,204 What role can AI agents play in improving 229 00:07:57,204 --> 00:07:59,384 claims accuracy and overall compliance? 230 00:07:59,845 --> 00:08:02,024 And what are some of the common misconceptions 231 00:08:02,084 --> 00:08:04,324 that you hear from leaders that they may 232 00:08:04,324 --> 00:08:07,305 have about using AI specifically in those areas? 233 00:08:07,925 --> 00:08:08,745 Sure. So 234 00:08:09,329 --> 00:08:11,569 I think there's a number of best practices 235 00:08:11,569 --> 00:08:12,949 that AI can now 236 00:08:13,569 --> 00:08:15,189 help accelerate for organizations 237 00:08:15,649 --> 00:08:17,969 as it relates to payer compliance and just 238 00:08:17,969 --> 00:08:18,949 staying on top 239 00:08:19,250 --> 00:08:21,490 of payer integrity, I'll call it. And I 240 00:08:21,490 --> 00:08:23,669 think number one is staying apprised of changes 241 00:08:23,729 --> 00:08:25,029 changes to payer policies. 242 00:08:25,865 --> 00:08:28,524 If you think about how payer policies were, 243 00:08:28,985 --> 00:08:31,305 you know, very sneakily modified by payers in 244 00:08:31,305 --> 00:08:31,964 the past, 245 00:08:32,264 --> 00:08:33,784 it was almost designed to make it hard 246 00:08:33,784 --> 00:08:36,105 for humans to detect. And therefore, it wasn't 247 00:08:36,105 --> 00:08:38,424 until there's a large kind of backlog of 248 00:08:38,424 --> 00:08:40,860 AR that indicated a payer policy change that 249 00:08:41,100 --> 00:08:44,139 organizations were actually picking up on relevant changes 250 00:08:44,139 --> 00:08:44,799 to policies. 251 00:08:45,659 --> 00:08:48,799 What our technology is doing, through Adonis Intelligence 252 00:08:48,860 --> 00:08:50,700 is act is we're actually scanning on a 253 00:08:50,700 --> 00:08:51,519 daily basis 254 00:08:51,980 --> 00:08:52,959 all of the relevant 255 00:08:53,794 --> 00:08:57,095 pair policies and almost doing a a comparison 256 00:08:57,315 --> 00:08:57,815 from, 257 00:08:58,355 --> 00:09:00,034 you know, what the policy looked like last 258 00:09:00,034 --> 00:09:01,254 week relative today. 259 00:09:01,875 --> 00:09:04,115 And from there, looking at those relevant changes 260 00:09:04,115 --> 00:09:07,080 and identifying which of those changes might be 261 00:09:07,320 --> 00:09:09,720 consequential to the organization that we're working with. 262 00:09:09,720 --> 00:09:11,879 So within the orthopedic space, you know, there's 263 00:09:11,879 --> 00:09:13,399 a lot of changes to prior auth rules. 264 00:09:13,399 --> 00:09:14,779 There's a lot of changes around, 265 00:09:15,240 --> 00:09:17,080 the amount of documentation that needs to be 266 00:09:17,080 --> 00:09:19,980 required, submitted should there be an appeal situation. 267 00:09:20,625 --> 00:09:22,164 And so being able to accelerate 268 00:09:22,544 --> 00:09:25,924 the awareness around payer policy changes is a 269 00:09:25,985 --> 00:09:28,625 is a huge, I I think, way we're 270 00:09:28,625 --> 00:09:30,965 enabling teams to stay on top of 271 00:09:31,424 --> 00:09:33,044 payer integrity or or, 272 00:09:33,424 --> 00:09:34,325 payer compliance. 273 00:09:35,589 --> 00:09:36,409 Furthermore, I 274 00:09:36,950 --> 00:09:39,129 think many revenue cycle teams will tell you 275 00:09:39,509 --> 00:09:40,089 that documentation 276 00:09:40,470 --> 00:09:42,089 uploads or, documentation 277 00:09:43,269 --> 00:09:44,809 requests from the payer are 278 00:09:45,990 --> 00:09:46,490 usually 279 00:09:47,269 --> 00:09:49,190 amongst the most costly things that they have 280 00:09:49,190 --> 00:09:52,095 to do. And the challenge is not that 281 00:09:52,315 --> 00:09:54,634 the providers don't know which claims are gonna 282 00:09:54,634 --> 00:09:56,334 require record requests. 283 00:09:56,634 --> 00:09:58,235 It's more so having to wait for the 284 00:09:58,235 --> 00:10:00,894 denial to come back before they can actually 285 00:10:01,355 --> 00:10:02,575 take action against, 286 00:10:03,355 --> 00:10:05,454 those known claims that are gonna get denied. 287 00:10:05,870 --> 00:10:07,549 And to so through our technology, what we're 288 00:10:07,549 --> 00:10:10,029 able to do is not only identify which 289 00:10:10,029 --> 00:10:12,690 claims are highly likely to get denied for 290 00:10:12,829 --> 00:10:13,329 lacking 291 00:10:13,949 --> 00:10:17,309 records, medical records, but actually proactively take the 292 00:10:17,309 --> 00:10:18,909 records and and submit them to the payer 293 00:10:18,909 --> 00:10:21,225 before the denial ever even needs to be 294 00:10:21,225 --> 00:10:23,965 contended with. And so, you know, being proactive, 295 00:10:24,105 --> 00:10:25,625 I guess, is the is the second best 296 00:10:25,625 --> 00:10:27,945 practice. I would say number one is staying 297 00:10:27,945 --> 00:10:29,785 on top of what the payer is doing 298 00:10:29,785 --> 00:10:31,465 and how they're changing their policies. But number 299 00:10:31,465 --> 00:10:34,504 two, implementing as much as many proactive measures 300 00:10:34,504 --> 00:10:36,125 as you possibly can to avoid 301 00:10:36,639 --> 00:10:37,699 pain that is avoidable. 302 00:10:38,399 --> 00:10:39,759 Yeah. It really sounds like so much of 303 00:10:39,759 --> 00:10:42,320 this is about going upstream. And as you 304 00:10:42,320 --> 00:10:44,879 mentioned with the staying on top of payer 305 00:10:44,879 --> 00:10:47,120 policies, like, how do we help those orgs 306 00:10:47,120 --> 00:10:49,199 try to almost have a sense of that 307 00:10:49,199 --> 00:10:51,595 in real time and and work and move 308 00:10:51,595 --> 00:10:52,095 accordingly. 309 00:10:53,274 --> 00:10:55,115 Looking ahead, how do you see AI and 310 00:10:55,115 --> 00:10:55,615 automation 311 00:10:56,075 --> 00:10:58,554 affecting the ASC space in the next three 312 00:10:58,554 --> 00:11:00,554 to five years, let's say? What are the 313 00:11:00,554 --> 00:11:02,954 some of the innovations that you're really excited 314 00:11:02,954 --> 00:11:05,399 about? Yeah. And I'll I'll actually touch on 315 00:11:05,399 --> 00:11:07,100 the question you asked in in the previous 316 00:11:07,559 --> 00:11:10,039 question as I respond. So, you know, one 317 00:11:10,039 --> 00:11:12,459 of the biggest common misconceptions is that automation 318 00:11:12,600 --> 00:11:14,220 needs to be a zero sum game. 319 00:11:14,759 --> 00:11:17,159 And what I mean by that is it 320 00:11:17,159 --> 00:11:18,779 does not need to be true that 321 00:11:19,095 --> 00:11:21,334 a piece of automation needs to eliminate the 322 00:11:21,334 --> 00:11:22,714 human from the loop entirely, 323 00:11:23,654 --> 00:11:25,814 especially so in in kind of the world 324 00:11:25,814 --> 00:11:28,054 that we're moving into where there are certain 325 00:11:28,054 --> 00:11:30,454 actions that AI is really good at performing 326 00:11:30,454 --> 00:11:32,214 on behalf of teams, and there's other actions 327 00:11:32,214 --> 00:11:34,860 that required decision making or judgment or some 328 00:11:34,860 --> 00:11:37,600 sort of taste, if you will, or analysis 329 00:11:37,740 --> 00:11:40,299 that maybe the AI is not fully equipped 330 00:11:40,299 --> 00:11:41,600 to perform quite yet. 331 00:11:41,980 --> 00:11:43,039 And so as it relates 332 00:11:43,659 --> 00:11:45,759 to revenue cycle within ASC specifically, 333 00:11:46,220 --> 00:11:48,159 I think the shift we're gonna see is, 334 00:11:48,845 --> 00:11:51,004 not in the elimination of of teams, but 335 00:11:51,004 --> 00:11:52,865 really the upscaling of teams where 336 00:11:53,644 --> 00:11:56,204 revenue cycle personnel specifically will be tasked with 337 00:11:56,204 --> 00:11:58,304 working what we like to call exceptions, where 338 00:11:58,524 --> 00:11:59,745 the AI is not 339 00:12:00,284 --> 00:12:02,450 suited or does not have an expertise in 340 00:12:02,450 --> 00:12:04,389 a particular subject matter. And therefore, 341 00:12:04,769 --> 00:12:05,910 they're doing the, 342 00:12:06,370 --> 00:12:08,210 let's say, the first three steps of the 343 00:12:08,210 --> 00:12:10,049 five step process and pushing the last two 344 00:12:10,049 --> 00:12:10,549 steps 345 00:12:11,170 --> 00:12:13,090 to, you know, a smaller group of people 346 00:12:13,090 --> 00:12:15,269 that are highly specialized and can take action, 347 00:12:15,615 --> 00:12:17,054 but take action in a way where the 348 00:12:17,054 --> 00:12:19,615 technology is assisting them or enabling them and 349 00:12:19,615 --> 00:12:22,195 allowing them to avoid doing the monotonous, tedious, 350 00:12:23,134 --> 00:12:24,115 and more routine 351 00:12:24,894 --> 00:12:27,455 work that ordinarily they'd have to do soup 352 00:12:27,455 --> 00:12:29,549 to nuts. Mhmm. Yeah. Makes sense. 353 00:12:30,110 --> 00:12:31,870 Aman, is there anything else that you wanna 354 00:12:31,870 --> 00:12:33,809 share or leave listeners with on the topic? 355 00:12:34,909 --> 00:12:35,889 Absolutely. I think 356 00:12:36,669 --> 00:12:37,169 the 357 00:12:37,629 --> 00:12:40,350 winners and losers in in in this space 358 00:12:40,350 --> 00:12:41,570 or or let's say 359 00:12:42,054 --> 00:12:42,875 fast forwarding 360 00:12:43,335 --> 00:12:44,795 five years into the future, 361 00:12:45,254 --> 00:12:45,915 the groups 362 00:12:47,014 --> 00:12:49,894 that accept and adopt that AI is is 363 00:12:49,894 --> 00:12:51,115 here to really 364 00:12:51,575 --> 00:12:52,075 elevate 365 00:12:52,455 --> 00:12:54,315 the entire health care landscape, 366 00:12:54,695 --> 00:12:55,195 and 367 00:12:55,720 --> 00:12:57,879 embrace that change rather than view it as 368 00:12:57,879 --> 00:12:58,779 a as a risk, 369 00:12:59,080 --> 00:13:00,919 I think we'll really see a lot of 370 00:13:00,919 --> 00:13:03,659 benefit in in accelerating their ability to push, 371 00:13:04,200 --> 00:13:05,559 I think, a lot of value back to 372 00:13:05,559 --> 00:13:06,860 their patients and their providers. 373 00:13:07,320 --> 00:13:09,259 I think anytime you're able to do 374 00:13:09,865 --> 00:13:13,085 more with less or more with a constant 375 00:13:13,304 --> 00:13:16,105 amount of inputs, or or resourcing, anytime you're 376 00:13:16,105 --> 00:13:18,105 able to do that, you're fundamentally becoming more 377 00:13:18,105 --> 00:13:20,605 efficient as a business and therefore can either 378 00:13:21,065 --> 00:13:23,670 shift those proceeds to your consumers or 379 00:13:24,290 --> 00:13:25,889 to those that make your business work. And 380 00:13:25,889 --> 00:13:27,029 I think in both cases, 381 00:13:27,570 --> 00:13:29,490 the patients and providers will be the ultimate 382 00:13:29,490 --> 00:13:30,470 winners. And 383 00:13:31,090 --> 00:13:33,590 I think the revenue cycle teams that, 384 00:13:33,970 --> 00:13:35,509 you know, ordinarily would be 385 00:13:36,394 --> 00:13:39,035 consequential to these processes will find that their 386 00:13:39,035 --> 00:13:41,274 jobs are also much easier and and more 387 00:13:41,274 --> 00:13:43,855 intellectually stimulating as well. Yeah. Certainly. 388 00:13:44,554 --> 00:13:46,554 Oman, thank you so much for your time 389 00:13:46,554 --> 00:13:48,475 and insights today. It was a pleasure having 390 00:13:48,475 --> 00:13:50,419 you on. Likewise. Thank you. 391 00:13:50,720 --> 00:13:53,139 And we also wanna thank our podcast sponsor, 392 00:13:53,200 --> 00:13:55,759 Adonis, and listeners, you can tune in to 393 00:13:55,759 --> 00:13:56,580 more episodes 394 00:13:56,960 --> 00:13:59,679 of the Becker's Healthcare podcast by visiting the 395 00:13:59,679 --> 00:14:03,840 podcast page on our website at beckershospitalreview.com. 396 00:14:03,840 --> 00:14:04,660 Thanks, everyone.