1 00:00:00,000 --> 00:00:02,000 Hi, everyone. This is Brian Zimmerman with Becker's 2 00:00:02,000 --> 00:00:04,240 Healthcare. Thank you so much for tuning in 3 00:00:04,240 --> 00:00:06,259 to the Becker's Healthcare podcast series. 4 00:00:06,639 --> 00:00:09,039 Today, we're on-site at Becker's thirty first annual 5 00:00:09,039 --> 00:00:11,219 meeting, the business and operations of ASCs, 6 00:00:11,519 --> 00:00:14,080 and we're going to discuss predictive scheduling and 7 00:00:14,080 --> 00:00:16,504 the future of AI in health care. Joining 8 00:00:16,504 --> 00:00:17,565 me for this conversation 9 00:00:17,945 --> 00:00:21,324 is Waleed Nasr, cofounder and CEO of Medley. 10 00:00:21,464 --> 00:00:22,824 Thanks for having me. Great to see you 11 00:00:22,824 --> 00:00:23,564 again, Waleed. 12 00:00:24,344 --> 00:00:26,364 Let's get in here in into the conversation 13 00:00:26,425 --> 00:00:29,219 with an introduction. Just, you know, give us 14 00:00:29,300 --> 00:00:31,779 some details about your your background and and 15 00:00:31,779 --> 00:00:34,100 the work you're doing now. Yep. Medley started 16 00:00:34,100 --> 00:00:36,739 as a marketplace that connected health care professionals 17 00:00:36,739 --> 00:00:39,559 with facilities that needed short term, staffing. 18 00:00:40,420 --> 00:00:42,759 And since then, we've evolved into a workforce 19 00:00:43,245 --> 00:00:47,325 optimization platform and orchestration platform that centralizes both 20 00:00:47,325 --> 00:00:49,885 core staff, full time staff, and flexible labor 21 00:00:49,885 --> 00:00:52,844 into one platform. And then applying AI and 22 00:00:52,844 --> 00:00:54,304 ML to be able to optimize 23 00:00:54,765 --> 00:00:56,865 that staffing across your facilities 24 00:00:57,480 --> 00:00:57,800 and, 25 00:00:58,440 --> 00:00:58,940 increasing 26 00:00:59,480 --> 00:01:02,399 retention and also increasing patient throughput. Yeah. It 27 00:01:02,520 --> 00:01:04,200 it's cool to hear about how how you've 28 00:01:04,200 --> 00:01:06,200 evolved over over the years in the AI 29 00:01:06,200 --> 00:01:08,599 component. Right? I mean, let's get right after 30 00:01:08,599 --> 00:01:10,680 it because, of course, that's what everyone's talking 31 00:01:10,680 --> 00:01:12,504 about probably for the for the last couple 32 00:01:12,504 --> 00:01:14,984 of years now and, into the foreseeable future. 33 00:01:14,984 --> 00:01:15,484 But, 34 00:01:15,784 --> 00:01:17,304 the the stat I have here is nearly 35 00:01:17,304 --> 00:01:19,144 half of medical practices in The US have 36 00:01:19,144 --> 00:01:21,465 been using AI to work at work for 37 00:01:21,465 --> 00:01:23,864 over a year. So from your perspective, where 38 00:01:23,864 --> 00:01:26,219 has AI made the biggest impact so far? 39 00:01:26,380 --> 00:01:28,299 And how have you seen it evolve just 40 00:01:28,299 --> 00:01:31,180 at I mean, it's clearly we're we're past 41 00:01:31,180 --> 00:01:33,020 the state of just pure hype. People are 42 00:01:33,020 --> 00:01:34,799 using this technology now. So, 43 00:01:35,420 --> 00:01:37,099 from your POV, where are we at? Yeah. 44 00:01:37,099 --> 00:01:38,954 That's it's it's it's definitely not hype, 45 00:01:39,435 --> 00:01:40,795 but it definitely has to have a well 46 00:01:40,795 --> 00:01:43,275 thought out applications. I think two of them 47 00:01:43,275 --> 00:01:44,814 from our perspective is 48 00:01:45,275 --> 00:01:48,314 medical scribing, where I think it's provided an 49 00:01:48,314 --> 00:01:51,130 unlock for doctors being able to just not, 50 00:01:51,130 --> 00:01:53,130 you know, have a natural conversation with their 51 00:01:53,130 --> 00:01:55,049 patients and have that data go right into 52 00:01:55,049 --> 00:01:58,250 the EHR system or EMR system. I think 53 00:01:58,250 --> 00:02:00,189 that's been a very interesting application. 54 00:02:00,569 --> 00:02:03,564 Another one, of course, is, medical coding, and 55 00:02:03,564 --> 00:02:06,165 it's been, a great unlock being able to, 56 00:02:06,444 --> 00:02:08,944 you know, be able to get, this information 57 00:02:09,164 --> 00:02:11,504 to insurers much faster to get payment. 58 00:02:11,965 --> 00:02:14,064 And those feel like really good applications 59 00:02:14,685 --> 00:02:17,245 for AI as it is today with, natural 60 00:02:17,245 --> 00:02:19,780 language. But we're we're really excited about is, 61 00:02:19,939 --> 00:02:20,919 workforce orchestration. 62 00:02:21,620 --> 00:02:24,740 And that is the intersection of machine learning 63 00:02:24,740 --> 00:02:25,479 and AI, 64 00:02:26,020 --> 00:02:28,180 and being able to orchestrate your workforce both 65 00:02:28,180 --> 00:02:32,040 full time and, flexible labor under one, platform, 66 00:02:32,435 --> 00:02:34,854 being able to, scheduling credentialing, 67 00:02:35,555 --> 00:02:37,794 cross training opportunities. So you can amplify that 68 00:02:37,794 --> 00:02:41,235 workforce from, internally before you even ever 69 00:02:41,875 --> 00:02:43,634 you put a job out to an agency. 70 00:02:43,634 --> 00:02:45,394 And I think that's a really interesting unlock 71 00:02:45,394 --> 00:02:47,550 for the future of AI. And can you 72 00:02:47,550 --> 00:02:49,409 talk a little bit too about just how 73 00:02:49,550 --> 00:02:52,129 workforce orchestration is the the phrase you used? 74 00:02:52,189 --> 00:02:54,129 Because why has that not been possible 75 00:02:54,750 --> 00:02:57,229 until now, until with this kind of technology? 76 00:02:57,229 --> 00:02:58,909 Can that's the way I'll phrase the question. 77 00:02:58,909 --> 00:03:00,935 Yeah. I think it's a intersection of two 78 00:03:00,935 --> 00:03:03,094 technologies. One, you have to have a platform 79 00:03:03,094 --> 00:03:04,534 to be able to do that. And that 80 00:03:04,534 --> 00:03:05,034 platform 81 00:03:05,334 --> 00:03:08,534 has to has to have, multiple features that 82 00:03:08,694 --> 00:03:10,394 that's like scheduling a credentialing, 83 00:03:11,094 --> 00:03:13,709 full pool management, on call management. You know, 84 00:03:13,709 --> 00:03:16,449 you gotta have integrations with EHR systems, 85 00:03:17,469 --> 00:03:20,430 with HR systems. So it's the aggregator of 86 00:03:20,430 --> 00:03:21,169 that data 87 00:03:21,469 --> 00:03:23,870 and then the application of machine learning and 88 00:03:23,870 --> 00:03:25,810 AI to be able to actually orchestrate 89 00:03:26,110 --> 00:03:28,415 that staff according to that. And I think 90 00:03:28,415 --> 00:03:29,955 that has been possible because, 91 00:03:30,415 --> 00:03:32,094 while you, you know, you have just a 92 00:03:32,094 --> 00:03:34,415 lot of point solutions, but you don't have 93 00:03:34,415 --> 00:03:36,495 true platforms with AI to be able to 94 00:03:36,495 --> 00:03:38,655 to provide that service. And and staffing and 95 00:03:38,655 --> 00:03:39,794 health care is inherently 96 00:03:40,175 --> 00:03:42,639 complicated. Right? Probably more so it's fair to 97 00:03:42,639 --> 00:03:44,879 say more so than most other industries. I 98 00:03:44,879 --> 00:03:45,860 would agree because, 99 00:03:46,400 --> 00:03:48,719 you just have different work preferences for health 100 00:03:48,719 --> 00:03:51,159 care professionals. You have credentialing. You have Right. 101 00:03:51,360 --> 00:03:53,919 Learning modules. You have, you know, you have, 102 00:03:54,240 --> 00:03:56,319 each unit in the hospital operates very differently 103 00:03:56,319 --> 00:03:58,284 from each other. The OR schedules are very 104 00:03:58,284 --> 00:03:59,344 different from infusion, 105 00:03:59,724 --> 00:04:00,224 scheduling, 106 00:04:00,764 --> 00:04:01,805 which is different from, 107 00:04:02,364 --> 00:04:04,604 ICU. And so it's just it's not a 108 00:04:04,604 --> 00:04:06,444 one size fits all, and, you have to 109 00:04:06,444 --> 00:04:09,025 have a platform that can accommodate each setting. 110 00:04:09,164 --> 00:04:10,500 Yep. Well, let's 111 00:04:10,960 --> 00:04:12,819 get into, I guess because I know 112 00:04:13,760 --> 00:04:16,000 many leaders across different types of health care 113 00:04:16,000 --> 00:04:18,639 organizations are turning to this technology to, you 114 00:04:18,639 --> 00:04:19,459 know, reduce 115 00:04:20,240 --> 00:04:22,660 administrative burden, workload burden. 116 00:04:23,205 --> 00:04:25,524 When you talk about managing the workforce, what 117 00:04:25,524 --> 00:04:27,524 kind of metrics should folks be tracking out 118 00:04:27,524 --> 00:04:29,705 there to see if, like, hey. This technology 119 00:04:29,925 --> 00:04:32,324 is yielding the efficiencies that we're we're looking 120 00:04:32,324 --> 00:04:35,225 for. Yeah. Traditional staffing is actually very reactive. 121 00:04:35,605 --> 00:04:37,899 Right? And so they lack the data. If 122 00:04:37,899 --> 00:04:41,180 you think about ASCs, they have distributed ASC 123 00:04:41,180 --> 00:04:43,839 groups, you know, in different states. And, 124 00:04:44,379 --> 00:04:44,959 I think, 125 00:04:45,500 --> 00:04:46,620 the first thing you have to do is 126 00:04:46,620 --> 00:04:48,779 centralize that data and be able to react 127 00:04:48,779 --> 00:04:51,259 on it. And so right today is everything 128 00:04:51,259 --> 00:04:52,240 is even from 129 00:04:52,595 --> 00:04:53,495 large hospital, 130 00:04:54,354 --> 00:04:56,514 systems to an ASC is is just very 131 00:04:56,514 --> 00:04:57,014 reactive. 132 00:04:57,394 --> 00:04:58,995 And so the first thing you have to 133 00:04:58,995 --> 00:05:01,314 do is have a platform where you can, 134 00:05:01,555 --> 00:05:03,794 centralize all that, and then you're able to 135 00:05:03,794 --> 00:05:06,354 apply those, that that AI and be able 136 00:05:06,354 --> 00:05:09,629 to have things like, tuning overtime, 137 00:05:09,930 --> 00:05:12,329 fill rates, workforce sentiment. Right? That's a big 138 00:05:12,329 --> 00:05:14,729 one because if you understand your workforce, are 139 00:05:14,729 --> 00:05:16,970 they gonna churn? When are they gonna churn? 140 00:05:16,970 --> 00:05:18,409 And you could start to predict these things 141 00:05:18,409 --> 00:05:19,689 so you can start to get ahead, and 142 00:05:19,689 --> 00:05:21,389 you're not reacting to using 143 00:05:21,794 --> 00:05:23,334 agency as your first choice. 144 00:05:23,794 --> 00:05:25,634 And I think that's a very important part 145 00:05:25,634 --> 00:05:28,514 of some of those key metrics. What's interesting 146 00:05:28,514 --> 00:05:31,334 though is each department in these facilities 147 00:05:31,714 --> 00:05:33,555 have different metrics that they're looking at. Like, 148 00:05:33,555 --> 00:05:35,314 the finance team is looking at different metrics 149 00:05:35,314 --> 00:05:38,980 than the CNO or, the schedulers. And so, 150 00:05:39,120 --> 00:05:41,840 again, having a platform that could, disseminate that 151 00:05:41,840 --> 00:05:43,860 part that right information to the right person 152 00:05:44,080 --> 00:05:47,300 really makes it more proactive than reactive. Mhmm. 153 00:05:47,439 --> 00:05:48,800 And and can you share a little bit 154 00:05:48,800 --> 00:05:51,460 about, so thinking about this from the ASC 155 00:05:51,520 --> 00:05:52,660 perspective specifically, 156 00:05:53,464 --> 00:05:55,245 of course, it's it's kind of known that, 157 00:05:55,384 --> 00:05:57,704 especially for independent ASCs, they don't have necessarily 158 00:05:57,704 --> 00:05:58,764 the same kind of resources 159 00:05:59,544 --> 00:06:01,865 as as hospitals or health systems. That said, 160 00:06:01,865 --> 00:06:04,024 their their ability to to bring in staff 161 00:06:04,024 --> 00:06:06,504 has often been about the experience. Right? The 162 00:06:06,504 --> 00:06:08,620 experience that they can offer staff. It seems 163 00:06:08,620 --> 00:06:10,699 like this kind of technology is very much 164 00:06:10,699 --> 00:06:13,019 in keeping with that. And if for for 165 00:06:13,019 --> 00:06:15,819 ASCs to continue to offer those folks that 166 00:06:15,819 --> 00:06:17,660 kind of experience, this is something that they 167 00:06:17,660 --> 00:06:20,060 should be really thinking about. Definitely. I think, 168 00:06:20,220 --> 00:06:22,139 what in a what ASCs need to understand 169 00:06:22,139 --> 00:06:23,955 is well, first of all, this technology 170 00:06:24,334 --> 00:06:26,175 can be applied at the ASC level as 171 00:06:26,175 --> 00:06:28,014 well as all the way up to, the 172 00:06:28,014 --> 00:06:31,074 hospital and and and system level. And so 173 00:06:31,214 --> 00:06:34,095 I think what's important is for ASCs to, 174 00:06:34,654 --> 00:06:35,154 embrace 175 00:06:35,490 --> 00:06:38,050 the flexible labor model. Right? And by doing 176 00:06:38,050 --> 00:06:40,370 that, it'll really give those, the the the 177 00:06:40,370 --> 00:06:43,089 ability to bring in this new workforce and 178 00:06:43,089 --> 00:06:44,849 then provide them the experience. They you know, 179 00:06:44,849 --> 00:06:47,349 this workforce is very interested in the flexibility, 180 00:06:47,729 --> 00:06:48,789 but also the flexibility 181 00:06:49,454 --> 00:06:51,634 of, being able to work in different settings. 182 00:06:51,855 --> 00:06:53,454 You know, being able to cross train and 183 00:06:53,454 --> 00:06:55,794 have someone work at PACU and pre op 184 00:06:55,855 --> 00:06:57,454 is interesting to them because that's what they're 185 00:06:57,454 --> 00:06:58,915 looking for, like you said, experiences. 186 00:06:59,294 --> 00:07:01,074 Mhmm. And and then I'll 187 00:07:01,790 --> 00:07:03,069 I'll I'll phrase this question to you then. 188 00:07:03,069 --> 00:07:05,069 For folks out there who are maybe thinking 189 00:07:05,069 --> 00:07:05,970 about doing this 190 00:07:06,350 --> 00:07:08,750 in the early stages of trying to implement 191 00:07:08,750 --> 00:07:09,410 this technology, 192 00:07:09,870 --> 00:07:11,389 what advice do you have for for those 193 00:07:11,389 --> 00:07:13,150 folks trying to bring AI into to their 194 00:07:13,150 --> 00:07:14,930 workforce management? I think 195 00:07:15,384 --> 00:07:16,205 start small. 196 00:07:16,584 --> 00:07:18,824 Right? Start with scheduling and credential. A lot 197 00:07:18,824 --> 00:07:21,225 of this is happening in spreadsheets or in 198 00:07:21,225 --> 00:07:23,084 filing cabinets. And so those 199 00:07:23,384 --> 00:07:24,205 are repetitive 200 00:07:24,584 --> 00:07:25,084 processes 201 00:07:25,785 --> 00:07:28,185 that is easily automated. So I would start 202 00:07:28,185 --> 00:07:29,939 with that. I would map out your your 203 00:07:29,939 --> 00:07:32,580 your processes on paper, and then I would, 204 00:07:32,899 --> 00:07:34,199 look for a platform 205 00:07:34,980 --> 00:07:37,459 that can scale with you, start small that 206 00:07:37,459 --> 00:07:39,399 starts small and then allows you to scale, 207 00:07:39,779 --> 00:07:41,699 much more broadly in the future when when 208 00:07:41,699 --> 00:07:43,074 you need to. Yeah. 209 00:07:43,375 --> 00:07:45,055 Anything else to add before we let you 210 00:07:45,055 --> 00:07:47,714 go? Any final final thoughts, something to reemphasize, 211 00:07:47,854 --> 00:07:49,454 or maybe something you didn't get to say? 212 00:07:49,454 --> 00:07:51,154 No. I think we're at a exciting, 213 00:07:51,935 --> 00:07:54,115 intersection in health care where both 214 00:07:54,495 --> 00:07:58,460 platforms and AI can really help amplify the 215 00:07:58,460 --> 00:07:58,960 workforce, 216 00:07:59,420 --> 00:08:02,620 can help these facilities retain their current workforce, 217 00:08:02,620 --> 00:08:03,680 and most importantly, 218 00:08:04,300 --> 00:08:04,779 can, 219 00:08:05,259 --> 00:08:05,759 provide 220 00:08:06,379 --> 00:08:09,545 the, ability for their core services to be 221 00:08:09,545 --> 00:08:11,785 profitable while providing value based care at the 222 00:08:11,785 --> 00:08:14,585 same time. So I think platforms and AI 223 00:08:14,585 --> 00:08:17,064 can definitely work to provide that service. Yeah. 224 00:08:17,064 --> 00:08:18,745 Well, Wally, thank you so much for for 225 00:08:18,745 --> 00:08:20,185 taking the time. It's a pleasure to speak 226 00:08:20,185 --> 00:08:21,791 with you again. Thank you for having me. 227 00:08:21,791 --> 00:08:24,371 We also wanna thank our podcast sponsor, Medley. 228 00:08:24,511 --> 00:08:26,271 You can tune to more podcasts from Becker's 229 00:08:26,271 --> 00:08:30,451 Healthcare by visiting our podcast page at beckershospitalreview.com.