0:00
If you can address the issues from the outset of the revenue cycle, it makes everything so much easier, as well as addresses the fundamental problem of increased denials. You

0:20
Austin, welcome to off the chart, a business of medicine podcast featuring lively and informative conversations with healthcare experts, opinion leaders and practicing physicians about the challenges facing doctors and medical practices. My name is Austin Latrell. I'm the Assistant Editor of medical economics, and I'd like to thank you for joining us today. In today's episode medical economics, managing editor Todd Shryock sat down with Clarissa Riggins, Chief Product Officer at Experian health, to talk about what's happening in the world of health care claims denied claims are on the rise, and for many practices, it's becoming a full blown crisis. Briggins breaks down what the latest state of claims report reveals, from staffing shortages and bad data intake to the growing role of artificial intelligence and stopping denials before they happen. She also shares what practices can do right now to protect their revenue and reduce administrative burnout. Clarissa, thank you for joining us, and now let's get into the episode.

1:22
You Experian Health recently released its 2025 state of claims survey, and I'm here with Clarissa Riggins, experience Chief Product Officer, to discuss some of the findings. Clarissa, thanks for joining me.

1:44
Thank you for for having me. Todd. Appreciate it.

1:47
So 41% of providers now face denial rates of 10% or higher up each year since 2022 what's driving this upward trend?

1:59
Yeah, no, I think that's a great question. I would say the biggest reason right now is the missing and your inaccurate data. That was the number one cause of claim denials, followed often by off incomplete or inaccurate patient registration data, which is very similar to what we've been hearing over the last couple of years, interestingly, so a lot of the survey is really validating some things that we've already known, as well as potentially some some new slight surprises, especially as it relates to adoption of new technology, including AI.

2:32
So more than half of providers reported an increase in Claim Errors. 68% say submitting clean claims is harder than a year ago. Why do you think these numbers are moving in the wrong direction?

2:45
I think part of the reason that we see some of this happening is a couple of it is also a bit regulatory, and this hasn't fully taken an effect yet, but I think that we are entering into a new landscape with some of the passing of the one big, beautiful Bill Act, which is introducing obviously significant administrative and documentation requirements. So I think some of it is also a bit of preparation for that, in anticipation of that. But also, I do believe that a lot of it is related to figuring out ways to identify how they can reduce some of the friction in their workflow, to address some of the issues that we are seeing and continue to see around the missing or inaccurate data, as well as incomplete or inaccurate registration information, Things really at the very beginning or point of entry of the revenue cycle process. And so some of those issues and some of that friction just hasn't been fully addressed yet, despite providers best efforts to really address through training and through really incentivization of their staff.

4:01
So staffing shortages were cited by 43% of providers. How much do workforce challenges directly contribute to denials compared with data or technology issues?

4:13
I think it's a combination of both, if you think about staffing shortages and continued staff turnover, providers are often challenged with training and retraining, and then also knowing that a lot of providers are dealing with multiple technology so it's a combination of both. Oftentimes providers are dealing with multiple technology vendors and partners that they're trying to not only navigate themselves through the decisioning of that, but also constant training that, coupled with workforce turnover, is really a recipe for friction across the board, in really trying to get their staff operating at the top of their license and actually spending more time on on patients. So if you, as you can imagine, with with kind of the con. Constant, I guess, influx of regulatory changes, payer, policy plans, etc, trying to keep up with that. On top of really navigating the technology they have available to them, it makes it very difficult. So I would say it's a combination of workforce friction, if you will, as well as technology and understanding their use of how automation can also help with addressing the problem at the outset, with the goal of reducing denials,

5:34
the survey highlights missing or inaccurate data, prior authorizations and incomplete registrations, the top three reasons for denials is there one of those areas that is more urgent than another for providers to address?

5:51
Yeah, I would say, Well, I'd say it's a combination of the missing or inaccurate data as well as the incomplete or inaccurate patient registration data, because I think a lot of it, as I mentioned, if you can address the issues from the outset of the revenue cycle, it makes everything so much easier, as well as addresses the fundamental problem of increased denials. And it's, it's your typical process of, you know, again, fix problems where they start, and then that should hopefully address the claim situation towards the end, when you're actually looking to submit the claims.

6:32
62% of providers say they're knowledgeable about AI, but only 14% are actually using it. What explains this gap between awareness and adoption?

6:43
I think the big thing so what that data told me is that the aspiration is still there, but the adoption is still adoption is still low, which tells me that providers are still fairly wary of the adopting transformational tech like AI and so, in that, in that, you know, taking that into consideration, a lot of it is really probably, I would call it still a bit of cautious optimism on what AI and technology can do for providers as it relates to rev cycle. So it's really trying to understand what practical steps they can take. What are the first couple of simple steps they can take to move from awareness to actual implementation? And it's it's really starting with a couple of simple steps, like identifying potentially high pain areas, like claim edits or eligibility checks and piloting a solution from there. So some of it is just knowing where to start. But again, the optimism is there. The adoption hasn't started. And I think part of it is that that revenue cycle leaders are also trying to understand where, where should they start? And it's it's really starting with the simple steps.

7:59
So for providers who have implemented AI solutions, 69% report improved denial prevention or resubmission. You know, what lessons can other organizations take from their their experience? Yeah,

8:14
no, I think, as I was starting to say, it's, it's knowing where to start. And you know, in my mind, the probably the first, most practical step that they could start so in terms of lessons, is identifying a high pain area and then building muscle from there. And building muscle from there means showing, or being able to demonstrate, as a partner, as a vendor, measurable ROI quickly that can create the framework for scaling across the rest of the revenue cycle. So I think that's the biggest thing. Is, where do you start? And where you start, typically is at the very beginning of the rev cycle, which is being able to identify any missing data, any inaccurate data, and having technology partners or solutions that can help you find these issues from the outset,

9:01
concerns about AI, accuracy, HIPAA compliance and payer specific rules are holding some providers back. How realistic are those concerns, and how can vendors help address them?

9:16
Yeah, no, I think they are real. I think they're real, and this is why the building trust and showing measurable AI out of the gate with any technology solution or any partnership is very important. And I think as much evidencing as any technology partner can show and prove to providers as they're I'm going to call it navigating and tiptoeing their way into the AI, or the technology is going to be very important. So what we've seen is the most powerful use of AI, especially in claims, as I said before, is preventing arrows errors before they can even reach the payer. And that means that embedding. Technology or embedding AI at the point of entry in the revenue cycle is going to be really important, where it can flag any missing information, any missing documentation, any payer specific requirements in real time. And that's where the confidence inspiration is going to be very important. Solutions have to be transparent, and as I mentioned before, showing and providing evidence of why of two providers on why a claim might need to be adjusted or edited, as well as obviously keeping in line with being HIPAA compliant, that's going to continue to be really important when providers see that the AI is working accurately and securely and is also tailored and is knowledgeable of the payer changes in the payer rules, it's going to become more, I guess, adopted, and there's going to be increased confidence in the solutions, rather than a black box.

11:12
Hey there. Keith Reynolds here and welcome to the p2 management minute in just 60 seconds, we deliver proven, real world tactics you can plug in your practice today, whether that means speeding up check in, lifting staff morale or nudging patient satisfaction north. No theory, no fluff, just the kind of guidance that fits between appointments and moves the needle before lunch. But the best ideas don't all come from our newsroom. They come from you got a clever workflow, hack an employee engagement win or a lesson learned the hard way, I want to feature it. Shoot me an email at K Reynolds, at MGH life sciences.com, with your topic, quick outline or even a smartphone clip. We'll handle the rest and get your insights in front of your peers nationwide. Let's make every minute count together. Thanks for watching, and I'll see you in the next p2 management minute.

12:01
The survey also found that confidence in claims management technology is falling. Only 56% say their systems are sufficient, compared with 77% back in 2022 what's behind that loss of confidence?

12:18
I think it's just that what they're seeing is they're continuing to see denials, denials, denials. And you know, part of that is, it's, it's the fact that the denial rate continues to go up when some of the adoption of, some of the tech is, some of the efforts that they've had are not bearing fruit. I think that's that's probably some of the feelings, or the emotions that are being conveyed in the survey results.

12:43
So do you think payer provider collaboration needs to change alongside technology adoption to truly reduce denials, or is this mainly a provider side challenge? I

12:56
think payer provider collaboration continues to be very important in addition to adoption of technology and understanding, really both sides of what, because, if you think about it, there's technology vendors that serve both the payers and the providers. And so while providers might be looking to technology partners to to be able to reduce some of the friction and ultimately reduce the denials payers are doing the same thing. So I think there's, there's an element of leveling the playing field on the technology. But of course, the collaboration is going to continue to be important in in coming to the table, and that's where solutions and understanding even kind of the, let's call it the payer contracting process. The contract management process is going to continue to be important. So you have to look at solutions and vendors that cover the gamut, or really the range of all those different types of solutions and technology.

13:59
You touched on this a little bit earlier, but I want to really emphasize this point. If providers wants to leverage AI or automation but feels unprepared or unsure where to start, what are some practical first steps they should take?

14:18
Yeah, as I said before, if you know, if you think about the steps of the revenue cycle, there's, it's a pretty, it's a pretty vast end to end set of steps. And so I think the first step, really, for provider who's looking to get started and adopting technology and AI is finding the highest pain points, perhaps, where they've got lots of staff deployed, but they're seeing continued or they're able to attribute, perhaps, where there are high errors, high manual work, and if there's attributable data to that part of the workflow and rev cycle to perhaps what's causing a denial. I think it's identifying some of those high pain point areas. And again, it doesn't have to be attributable. It could just be. It's very manual, very human intensive, lots of staff to it. You know, some areas that I mentioned is really at the very beginning of the process, really eligibility, checking, or even claim editing, claim submission, authorizations, etc. And from there, I'm going to call it pilot, an AI solution, to see how it's going to fare. And that's going to be really important in building comfort level, in building confidence and trust. And that's where the measurable ROI is going to be also important. So for providers who are looking to get started, it's partnering really with vendors who can show the ROI from the outset, and that's just part of part of the value proposition. So the key is really to find those partners, find those vendors who are not only bringing the revenue cycle and healthcare expertise, but who can also ensure that they're integrating into existing workflows with minimal disruption to staff, and that's going to be really important. I said that from the beginning is, you know, with a lot of friction, continued friction, really, with staffing shortages and staff turnover, so the integration with the existing workflows will be very tantamount to ensuring adoption, not only of the end users, but ultimately results for the revenue cycle leaders

16:36
when you're implementing an AI solution. Can you tell me a little bit about how much training is involved. Is it a heavy lift? Is does it take a long time to get the staff trained on how to use it? You know? Give me some insights into that process?

16:51
Yeah, yeah, no. And I kind of touched on this before. I think the key is to try to be as frictionless as possible in not just the finding the partners who can integrate into the workflows, but also minimizing the work for the users of the software. So AI solutions, technology solutions that understand the workflow can integrate directly with the H is or the electronic health record system is going to be really important, and then injecting information right into their workflow, as opposed to yet another technology that they're going to have to install and or pivot to on a tab in their on their on their computers. That's that's really not going to work well, because we know how long these, especially the H is installations, take. We know how much training is involved, so it's going to be really important for these solutions to integrate right into the workflow and be part of the work queue and just really provide the data and intelligence for those users to make those decisions from the outset, especially knowing kind of the complexity of the revenue cycle process and knowing that there's different teams and staff often working different parts of the rev cycle, whether they're registrars that are verifying eligibility and or schedulers at the very outset, or those who are really putting together the prior authorization, or those that are working on the post service side, and are those preparing and editing the claim and submitting the claims,

18:28
looking at the full report? Was there anything that surprised you when you saw the numbers?

18:32
Initially? I would say, if anything surprised me, it was still that the data around 67% think AI can improve the process, but only 14% have adopted so, you know, AI is not a new concept, but it feels like it's it's taken off a lot more in terms of just awareness in the last year or so since our last survey. And again, that just still tells me that the optimism, and the aspiration is there, but the adoption is still slow, largely due to maybe not knowing where to start. And so I think it's really, how do we, how do we work with providers to understand where they can start and where they can start, to think about how the technology and the AI can help them.

19:23
Is there any other data points you wanted to highlight that we haven't discussed

19:30
at this point? I think, I think we've touched on a lot of the data points. And if you think about the date, the data and the opportunities that the survey, the survey results really show it talks a lot about the power of possibly technology and automation and AI. And I'd say the best, the biggest takeaway I want to leave you with is that AI really works best when it's starting small and starting in really practical areas of. Rep cycle. So when you're able to start with kind of high pain areas, like claim edits, it really sets that framework for where you can pilot and leverage technology and other parts of the rep cycle. And it's getting those proof points early, and that's really what providers should be looking at as they're looking to get started. The real value of technology and AI comes when you can, you know, insert the technology at at the very onset of the process, really, where AI can prevent the errors up front. And the idea is that you can guide staff, guide end users at the point of entry with any kind of AI and, you know, HIPAA compliant, and pair specific recommendations they can actually trust. So that's really what I I would want to leave you with that we we've covered, but I want to make sure gets emphasized. And as you know, ultimately, the the measuring stick is the outcomes, and what matters most is reducing the denials and ensuring that fewer days, in AR, faster reimbursement, all of the things, and ideally, what that will lead to is that you're freeing up staff to work on higher value added work, because the technology is actually working for you. And if any solutions and tools out there can deliver those measurable results and the financial impact, while also understanding and having the knowable items incorporated into the technology. As payer rules evolve, as information becomes more knowable from the patient perspective, because you're leveraging that data, it not only becomes something that can be worked in the short term, but becomes a sustainable solution that providers can stand behind.

22:12
Very good. Clarissa, thanks for joining me.

22:14
Thank you. Thank you for your time, and thanks for the opportunity to share a little bit about what our findings were showing and sharing with you. Once

22:29
again, that was a conversation between medical economics Managing Editor Todd schryock and Clarissa Riggins, Chief Product Officer at Experian health. My name is Austin Luttrell, and on behalf of the whole medical economics and physicians practice teams. I'd like to thank you for listening to the show and ask that you please subscribe so you don't miss the next episode. Be sure to check back on Monday and Thursday mornings for the latest conversations with experts, sharing strategies, stories and solutions for your practice. You can find us by searching off the chart, wherever you get your podcasts. Also, if you'd like the best stories that medical economics and physicians practice published delivered straight to your email six days of the week. Days of the week. Subscribe to our newsletters at medical economics.com and physicians practice.com off the chart, a business of medicine podcast is executive produced by Chris mazzolini and Keith Reynolds and produced by Austin lutt. Medical economics and physicians practice are both members of the MGH Life Sciences family. Thank you. You and.