Latest / Home Health Revealed (+Palliative and Hospice) / The Truth About AI in Home Health: What’s Hype, What’s Real, and What to Do Next

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The Truth About AI in Home Health: What’s Hype, What’s Real, and What to Do Next · Home Health Revealed (+Palliative and Hospice)

• 0:03
  Speaker A: Welcome to Home Health Revealed, the podcast for home health and hospice leaders who want to stay connected to the industry and ahead of what's next. Listeners who might not be familiar with Flychain, can you share a little bit about what you do and how maybe AI fits into that picture?
• 0:20
  Speaker B: Yeah, absolutely. And again, Anna, thanks for having me back. I know it's been a little while, but we've been good partners and friends for a while, so appreciate the opportunity to chat. But yeah, we're Flychain. We are the kind of the financial infrastructure for healthcare agencies with a focus on home health, hospice, home care, a little behavioral health here and there. And again, the whole purpose for us is bringing clarity to your financials via our platform. We do funding, financial kind of visibility, KPIs and also our AI CFO. And the whole AI CFO actually gives our owners kind of real time view of their financial health and helps them understand what's going on in their business. But again, the reason I'm excited to be here today isn't to talk about flighting. It's because AI again is the topic that kind of everyone's hearing about. And it's not that no one knows what to do with it. I think, you know, it's, it's really just a, it's a murky universe and there are real players and there are not such real players. And so I think I hear all the time and I think we need to be like honest about instead of like dismissing it because it is here to stay. Like there is a fear that is associated with AI that's both on the clinical as well as the operational side of the fence. And I think the lion's share of our conversation today will be much more focused on the operational side here. But no, I'm really excited to jump in here. We're going to talk about a bunch of different topics as it relates to AI. Some of the basics as well as some of the trends that we're seeing not just among our customers and partners, but also from the payer universe as well. So yeah, I'm ready to dig in here.
• 2:05
  Speaker A: Yeah, let's do it. And I mean that's fair. Definitely what I'm hearing, seeing in the industry. And you know what this made me think of? It's not in our show notes, but do you remember when the World Wide Web was a brand new thing? Are you, were you there? Were you there for it?
• 2:21
  Speaker B: I think, you know, I, I think I was maybe in like elementary school where I like first, like first like had A Google in my library.
• 2:29
  Speaker A: Yeah, I was too. We bought our first computer when I was in fifth grade. Like our first home computer. We just had them at school. And I'm only bringing this up because this feels a little bit like those conversations then right when it was like, oh, are we going to have to use it? Is it here to stay? What is www? And I remember my parents having email.
• 2:52
  Speaker B: Right?
• 2:53
  Speaker A: Yes. And now we're so far beyond it that it doesn't feel intrusive or, I mean, it feels normal. I think we're going to get there with AI, but it is a little more, a little more detailed. There's just so much more that I feel like has come to the surface with conversations about maybe it's taking our jobs, maybe it's doing some of these. So let's, let's talk about some of that too, because it's a fear component. And I mean, I have team members who are genuinely concerned about their jobs. What do you say to that?
• 3:24
  Speaker B: Yeah, I hear that all the time. And I like to think about it like, you should not be worried about your job specifically. If you are actually embracing some of the AI tooling that we can talk about today, it's not necessarily coming for your job. What we see it doing is actually freeing up a lot more time for our customers either to do other things. Depending on the size of your business, your role, you might be wearing a ton of hats. Well, maybe that enables you to either either wear like more hats or take a few hats off and focus on some others. And so I think, I think about it as leveraging it to improve margin, save time, and actually using it as an augmentation tool. The other cool thing is, let's say you're maybe earlier on or you're growing. For those that are growing, you can actually scale your organization now without adding headcount linearly on the operational side because you can just do so much more with so much less on that operational employee side of the fence. And so when you think about that, you can scale, your margin will improve because you don't have as many operational fixed costs. But that's how I think about it. I think in our universe, like AI isn't necessarily replacing in other industries. Yeah, I would be worried. But for health care, home health, we're seeing this being used more as a margin improvement time optimization tool and we'll give some examples of that. But that's how I like to frame it. It's scary, especially when you read headlines, but in our world it's here to stay. But we can use this to our advantage.
• 5:06
  Speaker A: Definitely. And the way I view it in healthcare, in home health especially is if I'm an agency and I'm looking at it, how can I use my people to do things that only people can do to truly maximize the human interaction element and then take away all of the automation, take away the boring stuff that the humans don't necessarily want to do anyways and really turn my people to be patient facing as much as possible.
• 5:33
  Speaker B: Incredibly important point where humans can be sort of replaced when it's like fingers on keyboards and just automating like redundant tasks. What AI doesn't have is the context of the human brain, of you being in that organization. Maybe it'll get there someday, but that's entering different territory, not for this purpose.
• 5:57
  Speaker A: Right. And I think a lot of people do think of AI as like only ChatGPT. But can you kind of break down what AI actually looks like for an agency outside of just ChatGPT?
• 6:09
  Speaker B: Yeah, I'll first say like everyone should use ChatGPT and or Claude it is, it's an incredible piece of technology so you should be using it. And that is a version of AI. You can actually use those systems in much more like proactive, actionable ways versus I think, you know, early days when I was using it, I treated that just like, almost like a better version of Google, if that makes sense.
• 6:35
  Speaker A: Yeah.
• 6:36
  Speaker B: But now we're seeing this, you know, kind of AI revolution hit our, our industry in certain very like purpose built ways. So the first place I would always look is sort of like transforming operations and not just broad based operations like operations that are specific to, as an example a home health onboarding, patient workflow or a, you know, medical coding submission tracking down denials. There are operational components that are very time intensive that AI is actually quite like really well designed to handle. And so while ChatGPT is not going to do that for you, there are companies that are leveraging, you know, these AI models that are then kind of not retrofitting but fitting them into the specific workflows that are unique to our world. You know, honestly like, the other thing I would watch out for though is just because it says AI on like the company's label, it doesn't always mean it's AI. And some of it is like, you know, simple, like if then logic.
• 7:40
  Speaker A: Yes. Just expert systems which are, which are valuable. Like yeah, use them all day.
• 7:46
  Speaker B: Yeah, 100%. And I think about it, it's almost like people set up if then logic in Excel Right. And these are things that have been in existence for decades with like a little bit of marketing. You know, you could actually kind of pitch yourself as an AI team, but that doesn't always mean that. So I would say, like, it's important to ask questions if you're like, you know, thinking about a vendor that you'd want to work with, like, what is the data being trained on, you know, how is this improved roi? Like, what exactly is the value? And that can kind of give you an insight to weed out those that are good or kind of fake, if you will. I'd say the other like, important distinction is that there were kind of two worlds of AI in home health as we alluded. There's like the clinical side of the fence, like AI scribe, note taking is a big one. Not having to go at the end of your busy day and kind of put all these notes in there. That's, I think, one of the low hanging fruit sort of AI products that we've seen being kind of widely adopted. There's like coding review, OASIS analysis, there's definitely operational components and then certainly on the financial side as well, being able to leverage AI to predict like denials, using that for cash flow forecasting, margin tracking, things like that are actually like really well suited for AI today. And that's what Health Rev and Flychain are kind of really leveraging within our products today.
• 9:14
  Speaker A: Right. And I think, you know, we're still in a place where clinical judgment and human in the loop is very, very valuable. We're not outside of that by any means. And lot of this information, it's not the fact that you can get the information, it's what do you do with the information once you have it, that's going to really propel you into that next level of running your business.
• 9:36
  Speaker B: Yeah. And definitely important, like we're not clinicians, Hannah. Like clinical, clinical judgment is like see it, hear it, touch it, feel it kind of thing. And AI can't really replace that. And so it's, it's, I think using AI to get to the answer faster, more efficiently. That's how we want to think about AI as you're perhaps implementing it into your, your business.
• 10:01
  Speaker A: Yeah, exactly. And a side note, for any parents, you can tell a lot about your kids by their chat history. Just saying I looked, I looked at my, my son's in the next room. So I'm hearing cfs, hearing me, my daughter's chat GPT history, and it said act as if you are whatever and tell me step by step how to get my super strict mom to let me have social media. I was like, all right, that's coming.
• 10:33
  Speaker B: And then how do I restrict my, how can I respond to my daughter to restrict her? Yeah, it's a wild world we're living in right now.
• 10:41
  Speaker A: Well, heck no, that's not happening. Thanks so much. Oh gosh. Well anyways, back to this. How can agencies align their tech stack? So when I think about a tech stack it is really just that, right? You're adding on pieces of technology from EMRs to billing platforms, all of these things. How can we be ready for AI with our tech stack?
• 11:06
  Speaker B: Yeah, I think that the question that perhaps should come before like what tool should I buy? Because in our world like AI is only as good as like the data that you feed it and the operational tasks.
• 11:21
  Speaker A: Tasks.
• 11:21
  Speaker B: It's very specifically designed. It can't like come to its own conclusions at this stage. So it's really about like having the data that process and where to implement AI. So a good example would be like, you know, if your billing platform says one thing and your financial accounting platform says another, AI is going to give you a pretty darn messy answer there because it's, it's going to be apples to oranges and therefore it can't deduce or come to any conclusions if those two data sets aren't communicating with one another. I would say like the good news is you don't really need to rip and replace a lot of the existing systems. You know, there's, there's like, you don't need like one specific giant platform that will do everything. And what I mean by that is like you've got your EMR, you've got your revenue cycle management. A lot of EMRs in our CM are implementing AI as kind of point solutions in their EMR. So that's great. You can work with your existing systems and benefit on some of the AI tooling that they're building for you. And ideally they're building it again. A home health specific EMR is going to be building like specific AI automation tools like prior authorization, scheduling, things like that, that you might not have to go and find yourself because it's their job to go find the right ones or build it themselves and implement that. The other thing I'd say too is like, it's really about the layer that connects things, right? And so you can still have it be in these disparate systems, but as long as you kind of can bring all this data into one location, that's where I think more of the, instead of like workflow AI. That's where the AI can tell you really important things and surface really big problems. Forecast a problem that might not have popped its head in your eyes just yet. So I think so much of this is like, you don't need to rip and replace payroll, accounting, emr, rcm, all these things. It's just understanding what current setup you have and how they can communicate with one another. And as I think a pretty. The holy grail then is getting it all together and then having AI be able to be your superhero, super cfo, super CEO sidekick, if you will.
• 13:34
  Speaker A: Yes.
• 13:35
  Speaker B: Yeah. And maybe I'll just double click on that a little bit from like the flychain lens. So yeah, you know, I kind of mentioned like the AI is only as good as the underlying data. So let's think about like mimicking a CFO via AI. So you know, we have this AICFO model and that is tethered to our customers chart of accounts. And so now we built that accounting platform to get the data accurate. We also bring those bookkeepers to ensure the accuracy. So now all that data is accurate. It's also granular. So each revenue and expense transaction is broken down to kind of every dollar in and out. It's standardized for home health as an example. And then the last piece of the puzzle here is also context. So I'll give, you know, example. If we were looking at a chart of accounts that was accurate but maybe not super granular and you threw like an AI CFO on that, it's not going to really be that valuable because it might just be like, hey, like, yeah, this is your gross profit, net profit. Here's maybe one of the drivers. It's really like the peeling of the in and then benchmarking that data contextually to the specific like home health industry that you're working in, that is where you get a lot more context because we know what good means. As an example. If, if you. I'll just give it another example. When we launched our AI cfo, we were saying, hey, like, let's just see if we can pull a customer's QuickBooks data into our, our platform and have the AI, you know, CFO rest on top of that. And it kind of broke down even like a few little like, you know, miscategorizations here is going to throw that off. In some cases it'll just say like revenue, insurance, expenses, payroll. Well, like you need to break that down. Each individual payer, Medicaid, any commercial payers, any patient, you know, pet Private payments, salaries, clinical, operational. It's really just that peeling of the onion. That is where AI really shines is being able to go and interpret all these granular things and then kind of benchmark that. So if we were just throwing it onto the books that we had historically been seeing, it would either tell you the wrong answers or not tell you anything, like meaningful, if that makes sense.
• 15:42
  Speaker A: Yeah, no, that makes perfect sense. It is that data in, data out. And you know, you talked about several different pieces of the tech stack. I just want to know in your mind, is EMR kind of like that foundational technology piece that I feel like an agency when you're going into business. And I hear agencies say things to me often like, well, a startup. So we're going to start with X emr, right? It's cheaper, it's easier, it's whatever. In my mind that is like a decision that you have to start thinking about in long term, especially if you're going to grow with where they're going with their tech stack, with what they're capable of doing today and what's on their roadmap for tomorrow. Because once you start building in that data and a lot of the data that you're putting in is human entered data, right. Sometimes it's scanned in sheets, it's typing things in, it's copies of insurance cards and whatnot. But the data that you're putting into that EMR becomes really a source of truth overall. And so how do you see maybe even the idea of should I start with this EMR save money today or should I look to the future? What is the cost analysis of changing versus paying now?
• 17:07
  Speaker B: Yeah, yeah, that like it's a personal decision. Because also like every business owner is going to start theirs or might have a very different capital situation, right? They might be bootstrapping or they might have taken a lot of investment. So that's kind of the first thing. Like you, you can only pick what you can afford first and foremost. Like that. That's just something I will say up front when you're thinking about like an EMR investment, I would interrogate, you know, the EMR on a few fronts. So, hey, I'm here. What is available today? What is available for me as I grow? What is valuable? Like they might have, they might have so much tooling built, but if you're super small, that might, a lot of that tooling might not actually be super relevant to you at this moment in time. So I think that's like another thing is maybe you don't need to like buy every single piece if they're like kind of, you know, add on, add on services there. The other thing that I would just be weary of or conscious of, not weary necessarily is you know, are the emrs building that it themselves or are they plugging in third party point solutions to solve something? So I'll give an example. Like is the EMR building really good like scheduling or are they partnering with another scheduling entity? And the reason why like I get a little weary when you kind of have a Frankenstein like tech stack here with like six different companies. Like what happens if you know, like one of the companies leaves like now you might be relying on that as being like a really consistent part of your workflow. And if that EMR doesn't have it built themselves, that product might go away for you. So that's another thing is like what are be be very like tactical around what type of these AI tools solutions is that EMR building themselves in house because then you can rely that it will be there versus are they piecing a bunch of things together? I'm not saying there's necessarily anything wrong with piecing things together because maybe those point solutions, because that's their singular focus, can do it really, really, really well. But that's just something I always be, I would always be like very conscious of is who owns that specific part of the platform. And you also mentioned like EMRs are in many ways the source of truth. And if you think about that, it is the source of truth, but in some cases it's also not. And what I mean by that is think about who's using like the EMR on a day to day basis. It's generally speaking perhaps not like the CEO of that business. They're not, they're not seeing, right, you
• 19:41
  Speaker A: have data entry, somebody's right, an end user somewhere. They're humans and they're fallible 100%.
• 19:47
  Speaker B: And so like when you think about source of truth, sure, the user of those systems will be the folks keeping the lights on generating your revenue like they're the lifeblood of your organization. So yeah, it's a source of truth, but it's not the complete picture of the source of truth because what EMRs don't have is like the expenses, right? We don't know what the payroll, what are you paying all of your vendors? This is a clinical stack. And so again it's kind of what we alluded to earlier. It's having that bridge between every data element here to bring it all into one location so that the owner, the C suite, doesn't have to go into payroll, doesn't have to go into emr, doesn't have to go into all these disparate systems just to try and make sense, sense of it. Because you kind of have sources of truth in various spots, if that makes sense.
• 20:35
  Speaker A: Yeah, yeah. And one of the things that I, by the way, that was not on our list of questions, that was just my own little rabbit trail of thinking. But you know, in thinking about what flows through the emr, one of the things that I did want to bring up and talk about was the fact that some of these payers are already using AI, which really kind of changes the conversation in some ways since payers are already using it. How do we keep up with payers? Even like specific provider side tools?
• 21:07
  Speaker B: Yeah, this is probably like the most important part of the conversation in my opinion. And if you look at any like research or data, like pretty much all payers are now leveraging AI in a pretty material way. And so you as a, you know, an owner needs to be aware of that. And why are payers and how are payers using like AI today? So I'll give like some, some examples. Like they're using it not to speed up your reimbursement. They're using it to reduce what they pay you, for lack of a better term. There was a cool like industry study that came out, I think it was this week actually. It was from medical billers and coders like warning that the payer side AI is like kind of quietly eroding provider revenue through things like algorithmic down coding. And that's where like AI automatically reduces the level of your EM claims and modifier stripping, where AI removes modifiers that would have qualified for separate reimbursement. Like industry reports are basically saying at this moment it can be impacting. It does depend on the state and XYZ. There's caveats, but between like 6 to 9% of collectible revenue. And the tricky part is often it kind of falls below the standard audit threshold. So agencies don't even necessarily know that that's happening. Not to like scare anyone, it's actually, I think we can flip that on its head and make the positive case. So this is why providers should leverage AI on this side of the fence. The same AI capabilities that payers are using today to deny claims and downgrade providers can actually use AI to catch those problems before they wind up in that payer's hands and you get rejected. So you want to use that to flag anomalies you know, appeal things faster, you know, go after, you know, aged AR claims. Anything that you can do to kind of like meet them where they are. It's like fighting fire with fire a little bit. And, like, not only are they just to be also crystal clear. And this is something that I think is going to impact our entire country pretty heavily. Like, they're not just using AI to deny claims based on, like, billing errors as an example. They're now, they can now do this at like, an insanely large scale that they never could have before. And I think historically, like, maybe if you were a small provider in a rural area, maybe you were able to, like, fly under the radar a little bit more because they would only audit maybe like 5%. I'm just spitballing here, but now they can kind of analyze everything because they don't need humans to go and look at every single claim as an example. And so this is something that we're kind of seeing a bunch of states really start rolling out. And it's just something I want everyone to be, like, very aware of that where you might have been able to fly on the radar, you kind of. Now everyone has a bit of a target on their back. So I just wanted to highlight that this is a big thing that's happening and it's only going to continue. So it's all the more important to, like, really button up your internal processes. And Hannah, like, yeah, I'd be curious on your end, obviously, at Health Thread. Right. Like, you're, you're in the weeds all day, every day there. I'd love to learn a little bit more about some of the things that you're doing on your side to kind of like, combat's an aggressive word, but to deal with the, the new reality of like, payer AI implementation and the trends that we're just very obviously seeing there.
• 24:45
  Speaker A: Yeah, no, it's something that we're talking a lot about and really in. In the conversation, it always comes back to operations. What is happening? What are we doing to defend ourselves against audit cracks? Right. I think there's a couple of different avenues. You can think about it. It's preventative initially. Right. What are we doing? Because the other thing you have is, yes, these payers are doing everything they can to not pay you. You kind of have to bolster your workforce to fight back against some of this. So we're seeing an increase in the need to look back at contracts, for example, and go to bat and prove that these things were billed according to the contract. These things should be paid. We're seeing an increase in ADRs, there's definitely been an increase in audits. And so you really have to go about operationally running your agency with defense in mind. And I know that doesn't sound like a flashy and fun, but I think the message really is we have to do the right thing and document the right thing from start to finish. So, you know, with coding, we've not found an AI coding company that can really do what we need to. But if you can at least get yourself to a place where you can point back to the document documentation and see, okay, without having to go through 300 pages of documentation, we can point to specific areas where documentation has holes. Right. So we're utilizing some of those kinds of things and then all the way through to denial, trend tracking. That's where I see. And it is, you know, AI maybe. And I would say this is a little bit more of an expert system, but we're starting to track and trend utilizing some of those systems. Why are these things being denied? And then the goal of that is one, education for what's happening upstream. But then two, you know, when you go back to a payer and you're having to renegotiate and you're seeing that you're consistently having trouble getting these things paid, these payers, what they don't want is to have re hospitalizations. These payers and you know, they, they pay out on a patient. And so home health actually saves money in the long run. And so if we can prove that the care we're giving is the best and most cost effective method, then we're going to be winning and we're going to start to see an increase. Because since COVID has happened, and I was actually just talking to somebody from the alliance yesterday about some of this research that's being done. But you know, since COVID we're seeing hospital systems utilize home health agencies, buy up home health agencies because they know it saves them money from mitigating those rehospitalizations. So all in all, all of this data, we're using it in kind of a circular fashion to influence what's being done in the home, to influence what's going out on a claim, what's coming back paid and or not paid, and then using it to fight and make sure that as an industry we can continue to grow because we are a very valuable part of care that takes place in this country. And the more we can prove that, the more we can influence regulations and things that down the line are going to help us increase margin and become a more profitable and sustainable line of work.
• 28:21
  Speaker B: Yeah, I couldn't agree more. And it also feels like so much of what's going on right now AI payer driven, you know. Yeah, a lot of that is coming kind of from the top and it does feel like a, maybe a bit of an overreaction, like, you know, solving this problem with a blunt object of just like we're reducing rates. But you know, the whole goal here, and I think what we're trying to communicate is like if we have the right tooling in place that will make sure like clean passes, everything is buttoned up entirely. You know, hopefully that that pendulum will swing back in a little bit more a favorable direction because the data has proved, proved that out. Right? The data has proved that like we're delivering care really well. It's valuable for XYZ reasons that you just described. So I think we're in a very interesting time right now where the pendulum might have swung and it probably is going to continue to swing for a little while longer. But I think when perhaps maybe some of the bad actors, if you want to call it that, kind of get out of the system, I really do feel optimistic that it will switch in the right way because you know, you're left with those, those folks that are doing things by the book and can demonstrate like the material value you're having preventing hospital readmission. Readmissions being like a very, very clear example of that.
• 29:30
  Speaker A: Yep, for sure. So on the operations side, Ethan, what does AI look like financially?
• 29:38
  Speaker B: Yeah, I think like if you want to take it, I think there's both the operational blocking in tech and that's what we're talking about that can be done like literally scheduling you're even. We even see things where it's like, you know, someone will put like an AI voice agent in their office so that it can answer phone calls like 247 and just. Oh, you know, no, no patient ever kind of gets left behind, so to speak. Like a lot of what you're talking about on like, you know, the actual claims in our revenue cycle management process, that's where we're seeing it very much on the operational side when we think about it on the financial side that is a. We use a lot of AI internally to like categorize transactions and really like assign like more granular attributes to each dollar in and out. And then that's just so more like behind the scenes automation that we're doing. But then the outcome is that AI cfo. So once you get all that data perfectly accurate, that's where like the magic of insights come from. And that's where you don't necessarily need to hire a fractional CFO or a full time CFO that will tell you things that the system can now tell you. There is this element of context that I, you know, described before where you can't always just like, like brain dump someone that has all of that like qualitative information around a business. But certainly from like a numbers standpoint it will tell you what these numbers mean and then it's left for, you know, I think like an actual human to then what do we do with that data? That to me is how we see rubber meeting the road. It's a lot of operational workflows on sort of that above the line, you know, EMR practice management, scheduling, prior auth credentialing, rcm. Yeah, that's where the lion's share of like AI operations live. And then we're piggybacking off a lot of that work because the data is cleaner, it's more accurate and then we can launch our, our CFO on top of that. The last thing I'd say too is, you know, a lot of our folks are kind of like flying blind and by leveraging tooling that is now a, it's streamlined things but the data becomes more accurate and so it's just also much more reliable. You're not making decisions based off of data that was perhaps inputted by a human there where there wasn't like any sort of like quality control or AI checks and balances and things that historically we're always done manually. And so it just helps us close books faster, get to the answer faster and also get to the answer in my opinion more accurately. And also like, yeah, this is where it also gets like pretty exciting like between our two companies too, because so much of what we've talked about is you've got revenue data here, expense data and not super granular revenue data there. And if all of the data is flowing through the right processes and systems now, you're left with a pretty like unequivocally accurate data set and picture of your business here.
• 32:39
  Speaker A: Yeah, and not just a picture of it for the sake of having a pretty picture of it. I think one of the things that I didn't talk about earlier that I probably should have is the predictability of revenue into the future. I think that's where a lot of the AI models and these systems are really helping agencies look into the future and have good decision making data. Because then when you're looking at can I add on a clinician, if I'm able to influence marketing, how can I leverage and move around some of these little pockets of money for growth, whether that's marketing, clinicians adding on visits, looking at tech tools so that visits take place quicker and documentation takes place quicker. I think we have so many options for the way we can move around the pieces within the business and that becomes the data that influences the decision making. And unless we have those things in place, we can't see that picture of where we want to go, where we can go.
• 33:40
  Speaker B: Yeah, totally. The analogy is like we're all kind of like every system here is like painting a portrait with our own paintbrush and like it requires all of us to paint on the same canvas for that actually to make sense. Otherwise you're just going to get very disparate, you know, portraits hanging around your room that don't, that don't make any sense. It'll look like abstract art.
• 34:01
  Speaker A: Yes. Which unfortunately is a lot of what we see. And you know, I think explain to people because obviously in revenue cycle management, people don't usually come to us because they're like, hey, we're doing this really awesome. We're getting paid in a really timely manner. So would you take this over for us? Right. Normally we get the, oh my gosh, this payer is driving me crazy. Something's not set up in my emr. Right. I don't know why I'm not getting paid. Here is my tangled ball of yarn and I would like a sweater back in 30 days. And then you start, you know, unraveling the yarn to even get it into a place where you can weave the sweater. Right. And sometimes that means looking at contracts and figuring out forms and setups and operations and eligibility and off and intake forms and how things are working. Right. We see a sliver of the puzzle, but it's so much bigger. And a lot of times these conversations just mean I need to explain these other pieces and how they work upstream to downstream so that we're all on the same page. That's also why I think having partners who are willing to look at the big picture are so valuable in this world of home health and hospice because there are many nuances. This is not just you just click a button and it just works, especially with all of the new AI pieces that are in place. So I think, you know, we're looking at profitability. Yes. But ultimately what I always come back to and I think our companies share, share this value is these are people who are fulfilling their mission in their marketplace, their boots on the ground, inpatient homes, come rain, come snow, all the things, I mean, I've seen it with my own eyes and been able to go on ride alongs that I've really had my eyes open in a, in a fresh way even just recently. And it, it always brings me back to yes, it's numbers, it's revenue, but at the end of the day it is people, it is lives. And AI is not taking the people jobs. It's really not.
• 36:04
  Speaker B: No, no it's not. And it, and it won't hopefully ever. I, I don't want a robot, Karen, for my grandmother as an example.
• 36:13
  Speaker A: Nope. Please don't send me one.
• 36:15
  Speaker B: Yeah, yeah. And maybe like that's kind of an interesting thing around, you know, the, the marriage of a revenue cycle management platform and an entity like a flychain or any accounting system. And we kind of view this and we've talked about this between us, it's sort of like that full picture, it's 1 plus 1 equals 3. And without kind of combining these data sets again, the data sets are now accurate because we can rely on them probably. Hopefully you've been implementing some of this tooling that is double, triple checking everything. So now you get that actual perfect picture of the puzzle that is your business. And again, that's why it's been so fun working together. You have one side of the equation, we have the other and we know how to, you know, get those two pieces of the puzzle to fit.
• 37:06
  Speaker A: Exactly. Yeah. So I'm just curious, what role do you really think automation. I know you said you don't want a robot caring for you, neither do I, but what role do you really think automation and data integration is going to play in reshaping the healthcare finance ecosystem over the next few years? Even five to ten.
• 37:24
  Speaker B: Yeah. First and foremost, we're already seeing a lot of this happen today where businesses are able, with the right systems in place, are able to scale with margin improvement over time. And that's kind of going back to that notion of growing your business and not just again, not replacing tasks, but growing your business more efficiently without adding like human headcount. That's something we're already seeing as we go into the future. I really think, you know, whether that's flychain or a combination of our services, you know, with the amount of data, granular data context, like I actually believe a business owner won't really have to make their own decisions, candidly. And what I mean by that is like we'll have every single data element, piece of the puzzle. What state are you in? What is the reimbursement rate of every single one of your payers? What is the average salary for all of your clinicians versus the market you're in? You know, are you overpaying on your telecom bills? Like, from our lens, it's all about just augmenting all the data and then you actually kind of get to like the actual answer as to what to do. So a good example of this, and we don't have this like fully built today, but when we go into a customer's, you know, call it financials, we'll look at like gross profit and net profit. I'm just giving a simple example.
• 38:49
  Speaker A: Yeah.
• 38:49
  Speaker B: But we'll say, hey, like based on, you're in Texas, you're doing $2.5 million a year in revenue. You have Medicare as 40% of your revenue or met revenue, and you've got commercial payers and you even have a private duty element of your business here. So now we're going to look and see, like, what are the rates, what are the reimbursement rates within that market? Where do you stack up for your Blue Cross? Right. Okay, you're in the bottom 20%. Like we need to get that up. That's going to be a margin lever here. What are you, what are you charging your patients versus the competitive region that you're operating in. So it's, it's kind of like peeling the onion so that there's nothing left up to necessarily like interpretation. There's still going to be like some qualitative decisions made along the line. But I really do believe like you, at some point, once all these systems are perfectly connected, granular, benchmarked, there will actually be a pretty much like a North Star truth here for business owners. I don't want to opine on the clinical side because I just don't know about it. And it's also not my, my place, but I think from like an actual running a home health business as an example, I think we'll get to a point in the next couple of years where there will never be a decision, there will never be a wrong decision made. And maybe you may like, might hire the wrong person. Like, I don't think I'm going to be able to like interview someone and, and make sure they're the right fit for you. But I think with all the data, the financial data in your, in your system, there is the correct answer. And so every decision you can make, again right now we think about it as a decision aid. Right. It's a tool that helps you make those decisions and gets to the, the answers you're looking for faster. But I really do believe in the next couple of years you'll log into a system and it'll say, yep, do you need to do this, this and this? Click a button and then it does it for you.
• 40:43
  Speaker A: Well, and what you're saying is there's going to be some exposing of some of these different payers, maybe rate discrepancies amongst, you know, various home health agencies. And the more we get all of this data in one pool and can start to compare and negotiate, right. It's going to bring a standard up to a certain level. It's going to give agencies a view into what's going on. You maybe working with Flychain and you have several clients in the same region, you're able to say, hey, they're getting something that you're not. Let's, let's go back and look at that. So it's going to expose some of those kinds of things as well.
• 41:21
  Speaker B: Absolutely. It's already something we do today. It's not like completely embedded in our system, but we think like commercial payer rates for our customers in those regions. That's something we're doing kind of like an ad hoc manual basis. But there's no reason why that shouldn't live in perpetuity. And therefore you would know exactly how you stack up. You know, it's the interesting thing with health care versus I think a lot of other industries, like call E commerce for example, like so much data is really readily available in most other industries. Whereas in health care we've talked about the disparate systems all that live, all this data lives in and we still haven't gotten to a point where everything is super fluid. Once we kind of get to that. And what I mean by fluid, data is just data interoperability between all these systems in a, in a buttoned up, accurate, standardized way that is sort of that like the singularity of like running a health care business. And we're chipping away at it. You're chipping away at it. All these other people are chipping away at it to the point where, you know, you'll get this point of like complete transparency and enlightenment, if you will. But yeah, it's, it's a really interesting time to be building these types of businesses. Candidly.
• 42:32
  Speaker A: Yes, it is. And another thought on that, I think that's even more so if you're a home health agency. I'm a home health agency. We're in the same region, but we have very different rates and different kind of contractual agreements. One of the things that's going to be really important is how we're caring for patients with specific diseases with specific acuities, because that documentation is also going to become reasons, leverage, whatever you need to use to say this is why we should be at the same rate. Because a lot of it's going to come down to the outcomes you're showing for different levels of care.
• 43:10
  Speaker B: Yeah, I think this is probably something I'm like speaking completely out of my expertise on, but it's one of the things that I probably love about this job, being at this intersection of like financial technology and health care is, you know, I think we've talked like people are scared of AI taking jobs. Like, it's definitely transforming a lot of elements of our society and not always for the best. Like, I'm very much a movie nerd and I'm worried about like, you know, Terminator, like, you know, like Matrix. We're going to get killed by all this. Not to get too dark here, but on the other side, Hunger Games. Yeah, it's just like some post apocalyptic universe because robots have overthrown humanity. Right. I'm not saying that's going to happen, but it's something that does live in the back of my mind. On the opposite side of that coin though, on the health care side, clinical trials and research, like the pace at which we're now able to develop new healthcare products, drugs, solutions, et cetera, things used to take so much longer and now with the leverage of AI, you're actually able to run so many more of these trials and data sets. And so we're actually seeing just like some of the craziest stuff that's like revolutionary medicine that will actually really improve the quality of life. So again, that's sort of like a really like pie in the sky, you know, happy feeling, but it's happening too. And so it's not all, you know, I get a little doom and gloomy at times, but that is something that like really gives me a lot of hope and optimism of how people are leveraging AI for the betterment of like human health.
• 44:49
  Speaker A: Yes. Well, okay, to sum it up, because I feel like we could probably talk about this all day, solve all the problems within home health and hospice, at least from a financial standpoint. Right. But if you could give home health and hospice leaders one piece of Advice about future proofing their financial operations. What would it be?
• 45:08
  Speaker B: Yeah, few things. Like first just like look at your systems and if the numbers are out of whack and don't make sense, they're not where you think they should be. Like that's the first step is just trying to clean those things up. When you're thinking about like leveraging AI honestly there's a few ways to kind of get your feet wet. Like instead of like using again like chat GPT as a Google search, try some prompts with the AI tools with like very specific questions about your business. Like not a generic question like what should I, you know, do? Who should I hire? But like something like what is my denial rate by payer over the last 90 days and which payer should I focus on first in terms of like getting that down? And so why that is like a really cool question to ask is it's bringing in so many different elements of your business there, like what is your dso? What is your average dso? Which payers based on the DSO are the higher likelihood of actually being collected. So I would start prompting very specific questions about your business and if you're not getting the right answers, that's where there are options for you to actually get those answers. So that'd be the first piece. You know, you don't really need to like buy anything new. I think a lot of the existing providers out there or new, you know, now new providers, like you know, new EMRs as an example, they're all building this with like an AI forward notion. So talk to your existing folks and ask them what they're doing on this and then ask for help. How do I implement this? It's great that you've built this tool. I don't know how to use it. My staff doesn't know how to use it. So it's sort of like like crawl, walk, run around like identifying where you think AI could help in your business and then go to other experts that are building it themselves and they ideally should be able to tell you, hey yeah, this is actually some ROI you're leaving on the table because you don't have this implemented. Here's how to do it. We're going to do it for you. I think that's the kind of the way to start ingratiating yourself into this AI universe. And then obviously if you want to go deeper with anything on the financial side, like you know, where to, to find us and we, we eat, sleep and breathe this stuff all day and it's, yeah, it's Honestly, it's been a great benefit to our business and our customers too. So I actually was probably one of those. I was a little reluctant at first, to be honest with you.
• 47:23
  Speaker A: Were you really? That kind of surprises me.
• 47:26
  Speaker B: I know, I know. I think it's again going back to like me being afraid of like the Terminator outcome here. But now it's, it's. I'm seeing a lot of, a lot of benefit on our end and really the benefits being passed through to our customer base.
• 47:40
  Speaker A: Yeah. And I think if I could piggyback maybe a suggestion on that. I think, you know, as leaders sometimes we have our preconceived notions about what AI does or doesn't. Maybe we can be a little more on the idealistic side and think it's going to solve everything or maybe we have dug our heels in and don't want to deal with it at all. But my suggestion to agencies is to talk with your end users because truly that's where you're going to see the roi. Is it saving them time? Does it help with their job and get their perspective? Because one way or another, either the rose colored glasses or the doom and gloom, either one of those probably are not realistic and it's not truly getting the team and the person who's doing the thing involved.
• 48:27
  Speaker B: It's incredibly important. You need like buy in. You know, some people are allergic to it and you want to make sure that people that are using this stuff are comfortable using it and it works for them for. To really simplify it.
• 48:37
  Speaker A: Yes. So I know that you know, you've been a podcast guest several times, you're a repeat offender if you will. But where can people find you if they want to reach out and talk about this system that you've talked financial tools?
• 48:54
  Speaker B: Yeah, absolutely. So you can hit us up on our website, flychain us, you can also email me directly. Ethanlychain us, hit us up on anything, LinkedIn, whatever. But website book, a demo, my calendar link is kind of everywhere. I would almost give my personal phone number. But you never know with AI scribing these things. Who knows where that's going to go. But no, just would love to chat with anybody around really how we see this world. But also the tooling that we've built specifically on this financial side of the fence here.
• 49:25
  Speaker A: Yeah. Thanks so much for throwing all your ideas. It's always fun to talk with you and see kind of what's going on in your world and how our worlds collide. For sure.
• 49:34
  Speaker B: Absolutely. Hannah. Well, thanks for having me on again. And I'm excited to probably see you at the next conference, Honestly.
• 49:39
  Speaker A: Yep. Sure will. Thanks, Ethan.
• 49:41
  Speaker B: All right. You take care, Hannah.
• 49:43
  Speaker A: You, too.