Bringing Clarity to Workers' Compensation Through AI with Jamie Lapaglia

0:01John Lee Dumas:Light that spark fire nation, JLD here and welcome to Entrepreneurs on Fire brought to you by High Level, the all-in-one sales and marketing platform. Today we'll be breaking down how to bring clarity to workers' conversation through AI. And to drop these vibe bombs, I brought Jamie Lapaglia into EO Fire Studios. Jamie is the founder and CEO of ClaimClarity and applies over 25 years of clinical and workers' conversation leadership to create pioneering AI-driven decision support solutions that ensure quality healthcare and eliminates delays and denials. And today we talk about the workers' compensation gap, about AI and data-driven decisions, bringing expertise to tech and oh, so much more.

0:40John Lee Dumas:And a big thank you for sponsoring today's episode goes to Jamie and our sponsors. Fire Nation, is your business stuck? Are you looking to learn the proven processes and success systems that have been used to create thousands of millionaire success stories? schedule a free consultation with America's number one business coach, Clay Clark, by visiting thrivetimeshow.com slash EO fire. Again, request life changing tickets today at thrivetimeshow.com slash EO fire. If you are building a real business, you need real infrastructure. High level gives you website hosting, funnels, email marketing, automation, calendar booking, payments, and course hosting all on one platform.

1:17John Lee Dumas:Plus award-winning 24 seven support. Get a 30 day free trial and my full bonus stack that includes a 50 minute private call with me and much more at highlevelfire.com, highlevelfire.com. Jamie, say what's up to Fire Nation and share something that you believe about becoming successful that most people disagree with. Hi, Fire Nation. And I guess what most people disagree with about being successful is I think being successful comes with a lot of responsibility. A lot of people, I would think, they think probably once they're successful, they don't have to worry as much anymore. And I would argue it's probably the opposite.

1:57John Lee Dumas:Fire Nation, it is the opposite for a lot of reasons, I can tell you that for sure. And that's why we're talking about bringing clarity to workers' compensation through AI because this is a new world we're entering when it comes to AI, when it comes to all things agentics. We'll be talking a lot of things about this and more. I do want to start, Jamie, by talking about the workers' compensation gap. And I think a lot of listeners right now, they'll find it fascinating about how the traditional workers' compensation system, I mean, it's failing right now to meet modern expectations. Why is that?

2:32Workers' compensation was built on a legacy foundation, similar to mainframes, a banking industry. A lot of these sort of big, giant industries were developed over centuries. And what I'm trying to say is this, you have a you have a complicated, you know, fragmented system built on siloed infrastructure with with no single single source of truth, no real consistency throughout the different systems. So when you take that and you try to integrate into it, stuff like AI, machine learning, all these sort of newer technologies that take that data and scale it infinitely. It's just it's night and day.

3:09It's just it's incompatible.

3:10John Lee Dumas:I want to talk about AI and data-driven decisions specifically because, man, artificial intelligence, it is transforming claim management. You know it. I know it. You know it more intimately than most. Tell us how. How is it doing so? This is a great question. And I hear it all the time. And I see all these great AI solutions come into market. And as you dig deeper into it, you really realize that there really isn't a problem. The data is not the problem per se. It's not that we don't have enough data. It's that the data we do have is consistent, right? It's unstructured. It's disconnected. And there is no really clean or consistent way to bring it all together.

3:48We like to call the harmonization layer. So if you think about AI and what it really does is sort of take data and bring it to scale and make sort of predictions and decisions based on historical data, based on patterns. Well, if you don't have the context, you don't have the understanding what that data represents, sort of the underlying influences and levers. Well, then the insights that you draw from it can be widely misleading. And that's, I think, what we're seeing right now. We're starting to see, you know, the initial age of AI, even though we know this isn't the initial. So AI has been around for a long time, but we seem to have this sort of coming to to coming to enlightenment, if you will.

4:26Right. Like everyone's starting to see the A.I. is this amazing tool. And it is. But it's that it's just a tool. It's how you use it. And I think when we look at data and A.I., particularly in these highly specialized verticals like workers compensation, we often miss the critical piece. And that is that sort of deep domain expertise, the sort of that knowledge that's needed to sort of apply the context to link the appropriate pieces together. So I think that's going to be the biggest challenge as we sort of move forward. It's going to be how do we really use data in a better way instead of just throwing more data at the system.

4:59John Lee Dumas:So I want to talk about bringing expertise into tech, Jamie, because I want to know how you specifically are combining medical knowledge with technology to create better patient care. Or how are you seeing people that are doing that? What are they doing right now, day to day, week to week, month to month? No, I appreciate you saying that. So just so I built Claim Clarity. It's been about three years now. And that's built based on, you know, over 25 years of clinical and industry experience. So I wanted to put that in context. Right. So I wasn't able just to come in and open up my computer and and in a day develop, you know, this this product.

5:36I say that because it requires more than just a development background, a technology background, an AI background. It really requires that deep domain expertise. And I can say with firsthand experience that throughout my journey here, I partner with different developers. and I've learned the hard way that even the most sophisticated AI engineer who, you know, invented a lot of the techniques that a lot of the AI engineers use still struggle with some of the work we need done because, again, they don't have that clinical background. And without that context, you'll go down rabbit holes, right? You'll start to really start developing and you won't know it at first because if you don't have the technology background, you don't know.

6:19So bridging that gap, having the ability to sort of kind of speak both languages, if you will, The clinical side, the technology side is critical and be able to do what we're doing. And so I think that being able to combine those expertise, whether, you know, in our particular use case, it's going to be medical or clinical, if you will, you know, property, casualty or insurance, if you will, and AI technology. And having someone that has at least a general understanding of all three areas is critical to develop these sort of vertical solutions to solve real problems. Otherwise, you just have neat looking tools or sort of these catchy gadgets, if you will, but they don't really scale.

6:56So I think that's probably the biggest thing to look at when you look at these types of solutions.

7:01John Lee Dumas:Fire Nation, these solutions, they're there and they're being utilized right now by companies. And I think the question that we're going to be diving into is the innovation, the clarity, and then the future. And we're going to be talking about all this and more when we get back from thanking our sponsors. If you've ever tried to secure a traditional bank loan for your business, then you know the process is slow, complicated, and it rarely moves at the speed you need. And between increasingly strict lending requirements and a growing number of obstacles, you're often stuck waiting right when you need capital now.

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10:10John Lee Dumas:Visit highlevelfire.com and start building your something today. Jamie, we're back. And I want to talk about going from clinician to innovator. Can you actually share your journey of going from clinical leadership to creating a tech driven solution that actually solves the industry pain points that you were having to deal with prior? I started my career a little over 25 years ago. I was actually, I started as an ER trauma nurse in a, in a level one trauma center, which just tells you my, my, my background, right? I'm passionate about sort of diving in deep in first, right? Seeing firsthand the sort of the trouble, the profound impact that this having, making the right clinical decisions can have on patients and their families.

10:53And then I graduated, right? So I went from trauma to flight nursing, worked in various critical care areas, but I was still seeing over and over again that I, I wanted to try to make a bigger impact. So I sort of migrated from what we call clinical or bedside nursing into the non-clinical or managed care arena. So in that space, I started in managed care, started in utilization review. And then I did this prior authorization, retrospective reviews. It's really making sure that that treatment is safe and effective, that we're we're providing patients with the best quality care possible. in doing that though I really started to see behind the curtain I started to see all the the nuances all the inconsistencies if you will all the challenges that people have and so I started my journey out throughout different leadership positions I worked in various aspects of claims clinical data analytics and throughout that I really saw for again I worked with a lot of the the executives of the largest you know stakeholders within the industry And the pattern was the same over and over and over again.

11:53Everyone had all this data, but they didn't really know what it meant. So they would give it to their data teams. Their data teams would give them back all these reports. And they weren't able to drive any meaningful change, and they couldn't figure out why. And so I really think that going from my journey, particularly going from sort of bedside clinical hands-on care to sort of behind-the-scenes technical innovation was out of a need for developing a solution that really solved a pain point. Because otherwise you get more just, you know, you get either meaningful information or actionable. And I knew to be effective, we needed something that provided both meaningful and actionable.

12:30And so that's where I landed today. So what I can do now is only possible because of my journey. I couldn't have just started this 25 years ago and done this. And I couldn't have done this had I not had gone both through that, the sort of clinical and non-clinical work experience, I really required all that background to be able to develop the type of solution that we've developed today.

12:50John Lee Dumas:I want to move into talking about claim clarity in action. I mean, how exactly does the platform improve accuracy, speed up claims and support patients in returning to work safely and very importantly, efficiently? I think all of us can have an experience either directly or indirectly. Maybe it's our friends and family, someone we know that would dealt with this, meaning, you know, they want, they got hurt or they had something happen. They needed to go get medical care. They want to, they want to see their, their healthcare provider who, who wanted them to have an injection or therapy or a surgery or something.

13:22And they hear that whole, the whole idea, well, we got to get insurance approval, right? So they say, okay, they will send it to your insurance provider and we'll get approval and you'll get it done. And then days, weeks, months go by. And all you're getting back is we don't know, or it's been denied. Or we're still, it's a, it's a very frustrating thing, endless loop. But having worked again on both sides, I can tell you it's frustrating for everyone. For the most case, I think, you know, there's a lot of really dedicated, talented professionals out there in that managed care space, the claims adjusters, the insurer.

13:51I promise you, they want what you want. Meaning if it's the right treatment, it's safe, it's effective. They wanted you to have that treatment as quickly as possible. And I know a lot of people won't agree with that. But again, I've worked on both sides. I've had firsthand experience in this. I The problem is, is here, while we all know that we all know we're working towards giving patients the best care possible, where the disconnect is, is who defines that? Who decides what is the most effective care possible? Right. So most people say, well, the doctor, right, the doctor went to medical school.

14:23And they if they say you need it, you need it. And and again, I'm a clinician. So I think being a physician is a very noble profession. God bless people that become physicians and nurses and all those health care community for that matter. but they only have as much information as they have had, if that makes sense. Meaning they're being trained, they're going to school and then they're having on the job experience, which helps mold them and helps give them the knowledge they need to effectively treat patients. What they don't have is the, you know, the the infinite knowledge base of evidence based medicine that changes every day that we're we experience.

14:56And we do more research and we get more information. That's why you don't probably don't leeching anymore. All these sort of older medical practices that aren't used anymore are born out of, again, trial and error or born out of evidence-based research. And what I'm trying to say here is claim clarity helps empower those folks without them having to go out and do that research themselves. The research is out there. The problem is having the time and the ability to know where exactly to go to get the information you need to make a better informed decision. We're not creating the literature. We're simply connecting people to the literature.

15:28And as that relates to our particular journey here at Clean Clarity, working specifically right now with workers' compensation, a lot of folks don't know this, but in workers' compensation, it's state-driven. So that means every state develops their own rules. It's state-specific. There's – out of all 50 states, there's actually 17 different sets of guidelines. And it's just depending on which state that you're in determines which set of guidelines you're going to use. And then out of those 17 different guideline sources, there's over 12 ,000 different treatment recommendations. So if you're a physician trying to get approval for a surgery or treatment or something, you're sending this to the payer and the payer is trying to determine, well, you're getting care from this state.

16:10Therefore, we got to use these guidelines. And in these guidelines, they call that treatment. This we're in those treatment guidelines, they call it that. So it becomes it's almost like a Rubik's Cube, right? You got to basically speak 17 different languages without really knowing what the language coming in is or what language you need to use going out. So from how we speed this whole thing up is we basically eliminate all that, all those barriers. We transform incoming information, incoming requests for treatments into a standardized concept name. And then we map that standardized concept name to the relevant guideline, which is also a different treatment name.

16:46Right. So we basically harmonize all that data into one layer. We create this sort of a universal clinical decision layer so that we can expedite the process, meaning I can't right now without our system send a treatment to a payer to get approval and have that approval done in seconds. Because it first has to go to someone to look at. They have to sort of take the treatment name off the off the form requesting it. They have to normalize it. They have to codify it. They have to find where in their policies that addresses that treatment. Do they even allow it? Do they not allow it? Which state wants that treatment?

17:18then once they have that information they have to say okay the state well the state uses mtus it's one of the guideline sources for workers comp so i now have to go to the mtus source which is a different location than all the other 16 other sources right so each one has its own location i have to know where they are as you can see it's a very knowledge heavy a very um it's a very steep learning curve and with the with the rate that the these knowledge workers they're exiting the industry they're retiring we can't replace them fast enough and because it's such a deep learning curve, it's exponentially harder for new folks coming in to build that knowledge base.

17:52So what I'm trying to say here is it's becoming even worse. So what our solution does is eliminates that knowledge gap. We basically empower users with that expertise, even though they don't have it, so that they don't have to know which state requires which guidelines. What the name of the treatment guideline in that source is called, we essentially have a user come in, whether it's being directly fed in through an API or they're typing in cells. They just type it in natural language. I want to RTC tear repair and our system will return back the end test guideline for a supraspinatus surgical repair.

18:27See, we're not doing keyword search. We're doing semantic modeling. So now let's get to the gold of this, right? So how does it improve accuracy, speed, and most importantly, better outcomes for patients is now instead of a user trying to decipher which guideline to use or whether the criteria they're looking at is correct for the specific treatment they need, our system does it for them. And we, in fact, we just did a demonstration of, uh, I think it was last week where, um, the lead nurse, uh, was on the call and her, her quote unquote words were, that is amazing. It literally took you guys less than three seconds.

19:01What took me over 12 minutes to do. So I think that's a prime example, a real world example of where our system just in identifying the right criteria to use, we were able to return that to the user in three seconds where it normally would have took them over 12 minutes. and that's just one example of many and if i could add one more thing an average it takes about seven days um anyway i think what the range would be safe to say between five and 15 days depending which state you're in but average seven days to get a result right to get approval if you will well with our system we return those automatically in seconds so if you think about that not only we're eliminating all the denials the the 80 billion dollars a year spent in um appeals and And in fact, I don't know if you know this, but in the$80 billion in appeals, ultimately over half of them are approved anyway.

19:47The only difference is you're now spending all the extra cost on the additional reviews that are done, the weeks and months of extra delay to get that person that care they needed. Our system eliminates all that. And more importantly, it gets the patient that care immediately. So faster care means faster recovery, means faster return to work. So it's a win-win for everyone.

20:05John Lee Dumas:I love your passion, Jamie. I mean, Fire Nation, this is powerful stuff. How can you be applying this mindset to this thought process to your business, to what you're doing today? And I want to talk about the future of claims and AI. What do you see, Jamie, as the emerging trends in healthcare data, in AI-assisted decision making, and of course, the next generation of claims management? Well, I guess that's the million-dollar question, right? And I got to tell you, it's an area for me that I talk about passion. I'm passionate particularly about this because it's challenging to see every day all these AI solutions into the market.

20:41And not because there's AI solutions into the market, but because I understand that at the core of this, it's not a matter of needing more solutions. I guess what I'm trying to say is this. In the future, I think where the shift is going to happen, where AI is going ultimately, is instead of developing a one-size-fits-all solution, we're going to start to see a lot more specialized solutions, more of these sort of solutions built for complex verticals like we're in, like workers' compensation or healthcare in general. Again, working with folks that do AI and stuff, I find that without that context, there's often a major sort of gap in deliverables.

21:20Like you have this great product and you think it works, but you don't really understand why it works or how it works. And therefore you kind of have a miss there. So I guess what I'm trying to say is I think that the future of AI is going to really be about really establishing trust, about establishing solutions that are uniquely developed for a specific use case and that that provide more than just I don't want to I don't want to say touchy or gidget but what I mean to say like for instance summarizing medical records is great it's really great but unless you really understand the context of those records and what that data represents well then that summary often it falls short and that's what we hear from folks right we hear that you know often it summarizes things but it misses the important things and it over and over emphasizes non-important things and I think that's where not only claim clarity but But the future of AI is going that you're going to start to see a lot more specialized domain specific solutions that are really developed by industry experts in partnership with with with the development and technology industry.

22:16I think that's where we're going to have the best outcomes is not by continuing to silo and find ways to develop proprietary or, you know, or isolate ourselves. I think the biggest gains are going to come from collaboration. It's going to come from blending industry experts with from different industries together in one place. And I'm pretty excited to see what comes from that. I think that once we get past the desire to sort of build our own thing, I think we're going to look towards building something that adds real value. And I guess what I'm trying to say is I encourage everyone to stop thinking about how we do things and just think about what we're doing and why we're doing it.

22:54I think if you do that, then how we do things kind of solves itself, right? Well, we'll take a better path to get to where we need to go, and I think we'll have wild success.