Published, Not Perfect: What’s Lurking in Your Provider Directory?

Hello and welcome to today’s webinar, Published, Not Perfect! What’s Lurking in Your Provider Directory? Before we get started, I’d like to review some housekeeping details. Today’s webinar is being recorded and an online archive of today’s event will be available within one to two business days. If you have trouble seeing the slides at any time during the presentation, please press F5 to refresh your screen on a PC or Command R if you’re using a Mac. You may ask a question at any time during the presentation by typing your question into the Q and A box that you see located on the right side of your screen and pressing enter there. And finally, I’d like to remind you of AHIP’s antitrust statement, which is located in the link just below the slide viewer. We will, as always, comply with that statement. Among other things, the antitrust statement prohibits us from discussing competitively sensitive information. We’re very fortunate to have with us today Chris Reddich, Co Founder and Managing Partner, and Katie Harvey, Senior Vice President of Commercial, both with ProviderTrust, along with Ron Erwong, Co Founder of De Facto Health. So at this time, I’d like to turn the floor over to our speakers. Katie? Good morning or good afternoon, everyone. My name is Katie Harvey. I am the Senior Vice President of Commercial here at ProviderTrust. I’m joining today’s conversation with over fifteen years of health care technology experience, and I lead our strategy and go to market for both the provider and payer markets. Ron, I’ll hand it over to you. Thanks, Katie. I’m Ron Erwong. I’m one of the co founders of De Facto Health. I’ll tell you more about it in just a second, but we manage the largest data set of healthcare providers and the insurance they accept. And I’m Chris Reddich, co founder here at ProviderTrust, and just really excited to spend some time with Katie and Ron and just share a little bit about, know, de facto’s data set and our data set and the power of what happens when you put those together and the insights that we’re able to glean from it. So in terms of the agenda today, we’re going to spend a little bit of time, Ron’s going to go over what is de facto health and why and the data. We’re going go over the three findings really about how you think about risk in your in your directory. And then we’re gonna talk about why it matters and what regulations are are already doing. And then we’ll we’ll spend a little bit of time on like how do you fix it? What are some ideas around how you fix it? And then we’ll spend some time on the Q and A. So I’m gonna go ahead and turn this over to Ron to give us why this data and why the facto. All right. Thank you, Chris. You see some numbers here. I’m going to get into the numbers, but let me just give you a little bit of context about de facto, who we are, where we get our data from, what we use our data for. And then that’s going to dovetail nicely with the work that ProviderTrust has done in terms of detecting things within payers provider networks and the important work that they’re doing there. So just a couple of words around de facto. We’ve been around since twenty twenty one, and the reason we emerged in twenty twenty one is that that was the time when a couple of CMS rules dropped and started requiring payers to publish information about their provider networks and provider directories. So there was CMS nine thousand one fifteen, which required many payers to publish their Medicare Advantage, Medicaid, MCO, and also ACA provider networks in machine readable formats or FHIR APIs. And then there was a transparency and coverage rule that dropped around the same time that required commercial payers to publish information about their commercial networks along with their negotiated rates. So what does de facto do with that data? Well, we source that from several payers. We put it together into a large data set of health care providers and the insurances they accept, or said another way, the composition of each payer’s networks. And then we license that out to other companies that use it to help with provider search or plan shopping or network benchmarking or directory cleanup or life sciences market intelligence. So lots of different use cases used by lots of different organizations and people. De facto has done some research and published in ‘twenty four and ‘twenty five some national reports. So in ‘twenty four, we did some analysis and did a report on the level of compliance among payers relative to their FHIR APIs and machine readable files. And then in ‘twenty five, last year, we actually took a step deeper and assessed the accuracy of payers’ directories across the board, across different lines of business, across different states, different specialties and different type health systems and groups. And so that was super informative. And today, we’re going to be talking about the work that de facto has done with provider trust and the insights they’re able to produce relative to their primary source data, exclusion list data, disciplinary action data, to see the level of compliance in terms of those types of scenarios occurring within provider networks. So back to the numbers. So the numbers here really tell a story about the coverage that de facto brings in terms of the number of payers that we cover and the type of data that we were able to work with ProviderTrust to bump up against their data set. So this rightmost number here, twenty two million, that’s the number of provider plan relationships that are constituted within our data set. So it’s a very big chunk, a majority of the provider plan relationships that occur within the industry. So I’m going to hand it off to the Provider Trust team to take it a step deeper and to reveal some of the findings that they were able to uncover as part of their analysis. Thanks, Ron. So we’ll talk about the methodology because these are big numbers and this is a lot of data. And it helps and it will help hopefully for how you read the numbers that we’re getting ready to show. So we worked from two data sets. The first you saw highlighted on this last slide, which was a blinded set of one hundred and forty four payer networks, which encompasses about twenty two point three million providers. This had no organization names or business lines tags attached. That’s the dataset behind the most, the majority of what you’re about to see. The second is a smaller set of forty two organizations, which we have featured in our Provider Trust Public Risk Reports, but we don’t cite any of them individually here. As you heard, we don’t want to break any of the antitrust rules. And again, the five lines of business that Ron highlighted that are important, the payer segments are commercial, Medicare Advantage, ACA plans, Medicaid managed care. So across both data sets, we ran five layers of analysis federal and state exclusions, NPI status accuracy, which cross references the National Provider Identifier, NPI, against the NPPES website, the Medicare opt out list, license board actions, license status issues, controlled substance registration issues, and board certification status. So if we’re thinking through this, we’re encompassing not the demographic information like address, phone number, and in network status, which is really important to your members as well, but the good standing metrics. Are these providers in good standing? Should they be who your members go to see? A definitional note, license board actions are confirmed disciplinary actions that the board posts. License status issues are a broader category to include suspended, revoked, or expired license. That distinction really matters because of what we’re gonna show you next. And then because network size in this data set ranges from under twenty thousand providers to more than one point seven million providers, Everything we show you is a normalized rate. It’s not a raw account. So this is where provider trust comes in. This is our real time provider verification platform, Dynamic NPI, which is running de facto’s data through these five layers. Before we turn the results before we share the results, I do want to ask our first poll question because this really helps us understand you as the attendees, how you see your provider directory provider data. So the first poll question, how confident are you that your provider directory doesn’t currently include an excluded provider? Very confident, not confident, somewhat confident, or I have no idea. That’s why I’m here today. All answers are great. We’ll let that run, and then I’m gonna excited to start sharing some of the data. Katie, I am really looking forward to to seeing, people’s answers here because the the the data is is pretty telling. But also to mention, like, it’s a hard job. This stuff is changing all the time. Right? And we say that, you know, all the time, you know, across payers and providers is like, this is what’s challenging about this job. I agree. And and there are no wrong answers. It wasn’t a trick question. But, you know, what we what we saw from the poll is about fifty fifty. Fifty percent of attendees are at least somewhat confident or very confident that their networks do not, their provider directories do not include an excluded provider. And about fifty percent either have no idea or, you know, they are not confident. So when we think about that, there’s, again, a lot of data that was a part of this analysis. But what stood out that we wanted to share is all one hundred and forty four networks. So every single network carried at least one license status issue, license board action, or controlled substance registration issue. Every single network. Then only sixteen networks, so eleven percent came back with zero excluded providers. Eighty nine percent of the networks that we looked at had at least one excluded provider. And Chris, you set it up really well. This is hard. This data is always changing, always evolving, and so it’s really difficult. And I think when we feel confident, likely, it’s because we have a process in place. And if we don’t feel confident or we don’t know, it might be time to look through at what processes we use, because waiting for the next credentialing cycle, waiting to do this once a year, when we talk about the risk for our reputation and the risk for our ability to bill CMS and follow the rules and regulations that they have. Well, Katie, I also just think like we’re living in a world, an instant world, right? Where you could order a Domino’s pizza and it could be there in five minutes, right? So just this idea of, within healthcare, being able to service healthcare, but then the compliance side of things. And then this is what makes it tricky is the data on the backside is really hard. And we’ll talk a little bit about that. But I would not I I I would I would not be obviously, it’s a hundred percent, right? No network is gonna be perfect. They’re published and just like the title said, published, not perfect. Right? What we’re trying to do at DeFacto and at ProviderTrust is how do we make it easier to be more perfect? And I think that’s the encouragement we want people to hear is that we know that this is a hard job. And so we’re trying to do a lot of things on the back side to make it easier for our customers. Yeah, Chris, I’ll have you kind of walk through the Yeah, so finding. So before I get into this, I’ll give a little bit of history. People always ask, and I’m sure Ron gets this as a founder, it’s like, did you come up with this idea to do exclusion monitoring or license monitoring? I always say like very few people in my world, in my sphere when they were like in high school or elementary school would say, hey, I wanna run a big large healthcare data anti fraud company. In fact, no one says that, maybe Ron did, right? But I think what most entrepreneurs and most people do is they want to solve a problem, and it’s a really hard problem and make a big impact. And so what you’re looking at here and maybe if we go back to this next slide, this last slide, I’m gonna go back this eighty nine percent right before we get into that. The reason why this number is so high is because we’re doing things on the backside with our data to make it easier for you to find it. So a really good example, we’re in Nashville, Tennessee, and when you ask for a physician’s social security number, they’re not going to give it to you, but they will give you the NPI. And so what we’ve been able to do is we’ve been able with dynamic MPIs, we’ve been able to augment everything on the backside to an NPI. And so when we get NPIs, we could very confidently a hundred percent say like, hey, this person’s excluded or this person’s not. So there’s no, like, matches or potentials. Like, it’s only a match. And so this is what what what is is we’re able to find about twenty seven percent or twenty seven percent of the exclusions, no one else would have found, because they’re not augmenting the data on the backside. And we have some proprietary efforts and strategies on how we do that. But that’s how we could roll out a dynamic MPI product and get almost instant results that are higher than the industry’s average. And we’re going to find things that other people aren’t going to find. And so with this, you know, that this is what’s what’s what is that people who are fraudsters are going to do things to hide. And you got to recognize it’s it’s not necessarily the entity, it’s the individual behind the entity. And so that’s what we’re trying to to attack is find these people who have committed a fraud and get them out of healthcare in a healthy way before it impacts you all, your jobs. Yeah, and the other thing I’ll add about just exclusion data and the number of exclusion sources, right? There are forty four state Medicaid sources, and then there’s the federal sources as well. None of them do things the same way. And so there isn’t a one size fits all way to check a list and see if your provider may be excluded. Unique identifiers vary dependent upon the list. But what’s really challenging for organizations is the balance of federal exclusions and state exclusions is seventy seven percent of exclusions are state Medicaid exclusions, and those take over four hundred days to reach the OIG. So every state has a requirement to publish their exclusions at the OIG, but it takes over a year. And so if you are not consistently looking at every single state and you’re sticking to the federal sources, or you’re maybe only looking at the states at which your immediate members or your immediate providers live, it’s highly likely that this is where some of the gaps come in. And so we really just can’t rely on only the federal list. And that’s what Provider Trust really prides ourself on, is being able to ensure that at any moment in time, this ever changing information across close to fifty different sources for exclusion and sanction information is up to date within our network. Amy, I think what makes it challenging, you know, within this AI world is, oh, I’ll just go out and get that data. But with AI, you need them to make very deterministic decisions. It can infer because very quickly it’s going to start inferring that this person is not excluded or this person is this person and they’re not that person. And so with AI, you need to be able to provide very deterministic yes or no. And then AI could then open up the workflows, you know, do a lot of the things that it promises that it can do, but without that deterministic. And that’s what’s challenging around all these state lists is state of Alabama will have a name and that’s it, right? Whereas the state of Tennessee might publish a name and a birthday. Well, then the federal government, you know, might not have that information. So it does take four hundred plus days for it to even if it gets up there. Right? And on a on a annual basis, we’re constantly telling whether it’s state of California or another state that they have the wrong social security number attached to exclusion, right? And so if you start to think about like how AI or how this gets become a really, really hard job, That’s how it is because the data is not accurate. It’s different in different data sets. And then you have to be able to make those relationships outside of AI, you know, really trying to solve that problem. All right. So this was the slide where we talked a little bit about just how long it takes four twenty days. Katie kind of dropped that bomb a little bit before this slide. But if you think about it, that’s the challenge, right? You have all these state exclusions, seventy seven percent state, twenty three percent federal. Even within that state, you have permissive and mandatory, which gets a little confusing, permissive or mandatory. Do they go to the state? Do they not to go to the state? Even if they do get reported, do they actually make it up there? So that’s why you have to look at all your state and federal. And then it gets even more challenging around some of the things and we won’t even go into that kind of things. But the timeframe and then ultimately fifty eight percent of state exclusions never actually make it to the LEI, right? That could be because they’re permissive versus mandatory or all those things. But at the end of the day, if you’re excluded in one state, you’re excluded in all, and you need to be making sure that you’re paying attention to that information. Katie, anything else here on this Yeah, I was just gonna say, I do think this really makes the case for ensuring that you’re continuously monitoring your network instead of doing it at point in time at appropriate events. And really, within Dynamic NPI, we are reverifying license and credential board actions. We are reverifying that on a monthly basis. And our exclusion database of all the excluded individuals is being checked every day. And so this allows us to say, like, if someone’s clear today, it doesn’t mean in two weeks that that’s still gonna be the case. And there’s just no timeline that’s consistent to be able to tell you how to do it except to do it continuously. Hi again. Thank you so much, Katie and Chris, for going through the numbers and for providing a little bit of a peek into how complex the work is on provider trust side. But of course, you guys make it easy for everybody else because you have the dynamic NPI to be able to just plug in. You take all that complexity and all those unnormalized sources, and then you make it easier for others that you work with. Let me just uncover some of the numbers here and why this matters. We’re going to zoom out a little bit. So in terms of percentage of providers within any given payers network that has an exclusion or has some type of disciplinary action. It seems like a small percentage, but if you take a look at the absolute numbers and then what that means, it’s not small. So for Medicaid MCOs, you see it’s less than one percent. But less than one percent over a provider network of tens of thousands of providers or one hundred thousand providers or five hundred thousand providers, there are some payers that have very large networks. It’s in the hundreds of providers. And we also see that there are some payers, their networks, they’re not performing as good as the median or the average. And you can see that they can have even up to 18x the number of exclusions than the median. So it is a big problem, especially if you take into consideration things like civil monetary penalties. Everybody wants to talk about ROI and assigning some type of dollar value. When these types of cases show up, the civil monetary penalties can be twenty thousand dollars fifty thousand dollars on a per case basis. So this isn’t on a per provider basis. But if members see these providers, it can add up. If you have ten members who have seen that particular provider, that starts stacking up. And we know that federal and state agencies have taken some of these actions. You can see some of those fines and those penalties logged in government documentation reports. So this is a real thing with real ROI, with real risk exposure. What I’ll say about Medicaid specifically is, and we’ll go into some more of the regulatory context in a second, But Medicaid, the prevalence of some of these exclusions are higher within some Medicaid networks versus the average. And that’s important because the federal government is taking a little bit closer look at some of the Medicaid networks. Doctor. Oz sent a letter to all the state Medicaid directors just a couple of months ago saying that swift revalidation needs to happen. So just wanted to mention that we’ll go into some more detail. This is not a directory problem. It’s not just a credentialing problem. We talk about the silos, but this is a data mastering problem. And it’s important on any given payer, the problem could be in different places. It could be all the way upstream. You’re just not doing enough monitoring, or you’re not doing it frequently enough, or you’re not doing comprehensively enough. And that’s where provider trust could provide the right solution way, way upstream. Or it could be in the middle. Maybe you’re doing all of that, but you’re not loading that data into your directory. And so there may be some incongruency between your directory and your monitoring efforts. And that still needs to be fixed because you’re still being exposed on this end. So these issues can look a little bit different, and it’s important to take a real fine grained diagnostic approach to your particular situation. We’re going to talk about the regulatory context a little bit more. So I mentioned Medicaid. Let me just mention a couple more things about the Medicaid situation before we get into Medicare. So I mentioned Doctor. Oz’s letter to all fifty state Medicaid directors. Let me just double click into and I wrote a piece about this about a month ago, so if you’re interested in getting deeper into it, feel free. But there’s an opportunity on the government side, because they’re mandating so many transparency files and APIs, for them to scale up monitoring enforcement. I mean, you’ve seen when ProviderTrust was able to take the directory data from de facto, The work that they’re doing on their end is very hard. But in terms of the bump up, once they have the NPIs on their end, it’s easy to bump up against payer directory data that de facto provides. The government could do similar things. So they may not be doing it yet, but they’ve expressed an interest in it. They’ve talked about it in RFIs and rulemaking, that it’s not just about cleaning up addresses and phone numbers, but it is cleaning up providers who shouldn’t be in network and providing that constructive feedback to the state Medicaids themselves, but also to the MCOs. So every state Medicaid has to publish their FHIR APIs. Every Medicaid MCO also has to publish their FHIR APIs. And the state Medicaids, especially ones like Texas and North Dakota, there will be others, are more often now mandating NPI enumeration for atypical providers. So behavioral analysts and home renovation and pest control. And oftentimes that’s the type of fraud that CMS is looking for. So when that’s enumerated more and provider trust is able to get their NPIs and bump that up against OIG lists, the government’s going to be able to do similar things. So it’s important to get in front of those types of regulatory actions and efforts. I’ll just say a couple of things about Medicare, and then we’ll dig into some of the other pieces. The Real Health Providers Act, which is legislation that was passed, it’s a law that was passed in February, requires that Medicare Advantage plans publish accuracy scores about their directories starting in twenty twenty nine. Now there’s a lot of stuff that goes on between now and twenty twenty nine. One of them is that CMS will dictate or define some type of measure of accuracy. And of course, it’s address, of course, it’s phone number, of course, it’s accepting new patients. But it may very well also include providers who should not be within their directories. If they’re a Medicare opt out and they’re showing up in a Medicare Advantage directory, what’s going on there? If it’s a provider who’s lost their license and they’re showing up in a Medicare Advantage directory, they shouldn’t show up there. The other thing that the last thing I’ll mention on this slide is Medicare Plan Finder. So this is even sooner. CMS will start taking Medicare Advantage plans directory data starting in October, October of this year. So that’s just a couple of months from now and load it within their Medicare Plan Finder tool for open enrollment such that Medicare beneficiaries who are looking for the Medicare Advantage plan they want can punch in their favorite doctor. This is my PCP. I want him in network. This is my cardiologist. I want her in my MA plan. Do not show me any MA plans unless you show Doctor. Katie. And so, of course, a lot of you all are working on that very feverishly, and that’s great. However, it just shows that CMS is using the data. They’re becoming more familiar with it. They have teams of technical people who know how to query FHIR APIs that weren’t able to do it before. And so they’re going to be taking a look at not just addresses, not just phone numbers, not just whether this provider is in this plan, but also whether these providers have exclusions, whether these providers have actions, should they be within your directory. So I’ll just pause there. I know there’s more that we need to go forward with. Ron, I I I you know, I I’m I’m excited. I know we got this poll here. So let let’s just go ahead and do the poll and then, while while people are answering. So does your organization have an existing solution for ongoing monitoring of provider directory credentials? Yes. No. We’re in the process of implementing one or I’m not sure. It’s four answers. Think what’s exciting about this time on both sides of the table is that access to data is getting faster and more available. Now the question is, is that data truthful? And is that data truthful at a point in time? Right? So the the next layer is like, are you monitoring? But was that person excluded or did they have proper credentialed when that procedure occurred? And that’s a very quick next step behind like, is it accurate, right? Because they’re gonna begin to say like, it’s not just okay to be accurate at this point in time, you need to be accurate all the time. And so this is where this new AI revolution, all this stuff kind of comes into play. And the underlying thing both Ron, Katie and I know that is, is the data accurate? And can you track it and track the changes across time? And so that’s going be exciting. I think we’ll close out this poll pull up the results a little bit later. So from findings to fix, we got a little bit into this conversation, but I’d be interested, Ron, just kind of in your thought process. Obviously, like, the the fix is an ongoing layer of primary source verification, right, throughout the credential cycle, not just once every three years, not just a point in time solution, but it’s the ability to query that data on an ongoing basis. And so you don’t have to, you know, set up your entire, you know, credentialing process, you just need to add another layer to determine the truthfulness of your information, and then that feedback loop to be able to correct it in a very timely manner. I think that’s right. I’m sorry, go ahead, Chris. No, go ahead. I was just turning it over to you. Yeah. I think that feedback loop is so important because I think understanding because the directory, if you will, is kind of the finish line. That’s where you’re pushing all that work is funneling through. Finally, the members have access to something. This is how they view a payer’s network. It’s also how regulators view a payer’s network just because it’s the public exposure endpoint to it. So if you can detect issues there, having that feedback loop, asking those hard questions about where is it breaking, is it breaking all the way at the top, Is there just not enough ongoing monitoring going on? Is only a federal source and it’s not crossing all of the state sources? Are there issues with provider matching that it looks like ProviderTrust has done a lot of work to do that very detailed, very complicated provider matching? Or is it also a downstream issue of the data not loading properly within the directory? And I think there have been, over the past ten years, many efforts and their fits and starts within payer organizations. If you’ve been around your organization long enough, you can probably recall the CIO bringing up some type of, or a provider data management team, sending a business case to you guys and saying, hey, we need to finally rationalize all of these five different PDMs and bring it all into one place, into one PDM. This might be the time to consider that just because it’s just so much easier for not only members to see the data, but also regulators. And it’s so much easier to detect these issues. And as Chris said, have that feedback loop. So take advantage of that feedback loop before anybody else does. Ron, I think one thing before we get to this next slide, Slide eleven, if you just go back, let’s go back to that. And I just think it’s such a good there we go. Just just why why this matters, where you have con contracting, enrollment, credentialing, and directory management. And just like each of these sections in the life cycle, they’re a beast in their own. Right? But this feedback loop has to be going on throughout the entire thing because data changes every day. You know, people say, well, I’m checking exclusions once a month or quarterly or once a year. The reality is like those exclusion lists are being updated on an ongoing basis. I know that because I see that and we’re pulling in PDFs, we’re pulling in Excel spreadsheets, we’re going in querying or crawling a specific website, they’re all different. And then we have to go through the whole augmentation process, right? So we’ve built that over the course of the fifteen years. All that is going into each of those processes. And so that’s where I say, Ron, I think you mentioned is like, you really should sit down and really just draft out like, where’s the data coming in and at what points and then what’s the feedback loop on each of these, knowing that it’s gonna change the next day. So you just have to have that continuous cycle. Yeah. And I’ll be brief about this metaphor analogy, but if you’ve got some type of leak in the house, you got to figure out where it is and you have to trace it all the way through all of the pipes. Provider data is the same way. If you can see that something bad is happening at the very end, something bad is happening either in the middle or upstream. And for any given type of error, it could be one or both. I have an example. There’s a payer that you guys know and that I know, I won’t mention their name, but we were finding directory errors within their directory API, but also their web directory. And it was clustered around a particular health system, a few health systems. And they were able to find two flavors of errors. So one was they had some business logic issues loading the data into their directory. And so once we found that feedback, they were able to fix that load business rule. So that’s some internal IT work. But then they were able to find issues all the way up top from the providers entering the data into, I guess they’re called DataSpring now, but they used to be called CAQH, their portal, where the enrollment folks who were clicking on the dropdowns were selecting the wrong dropdown. They just needed some education on which dropdown to pick. So they provided that constructive feedback all the way up top and good data started flowing through. As you guys have described, this is a complicated problem and we’ve got to fix it organization by organization. But when you have scalable tools like ProviderTrust, you’re able to do that in a more scalable, efficient way. Yeah. And Ron, I think you about, you didn’t mention it specifically, but it really is a team effort. I I grew up playing collegiate professional sports teams that win and win championships. It’s not that they don’t have conflict, it’s that they actually move through conflict faster, right? Where they’re able to have direct conversations, they’re able to own that, but also they recognize the big goal. And so I share that because it’s on both sides. It’s not just the payers, it’s also the provider orgs, right? And so how do we move into an atmosphere into a culture where we’re all working together to try to solve this problem? Because in some ways we’re creating problems for each of the groups. And so how do we have those? And those are some of the conversations a month and a half ago, had the state of Tennessee, we’re blessed to have a lot of public hospital companies here in Tennessee. And we had some really good conversations with both payers and providers talking about how do we solve this? What does this look like? And what are some ideas on how we can leverage AI? And so that’s where it’s like this collaboration of like, we’re trying to solve something, it becomes really powerful and you can move really, really fast when everyone’s kind of moving in that same direction. So I moved us off course back to slide eleven. Let’s jump here to slide fifteen, where Katie and Ron, you guys were summing up some of this stuff and then we’ll get into some Q and A. Yes. There are some questions I’m excited to answer. But I think a couple of key takeaways as we kind of wrap this conversation. In looking at the poll results, we asked two poll questions. I cannot see who answered, so this could be completely, happenstance. But the, the number of people who are confident that their directory does not include an excluded provider almost directly correlated with the number of organizations that already have continuous monitoring in place. So the fact of the matter is this continuous monitoring should improve your confidence. However, I think we all know this, but it’s important to highlight that it’s likely that you’re using different systems for your directory and for your continuous monitoring. It’s likely that possibly those systems don’t talk to each other. And so as we think about our role within an organization, it’s important to think about who is who’s communicating with you internally to ensure that this data makes it from upstream, like Ron just, really nicely, articulated for us, all the way downstream. Because at the end of the day, your directory is a public promise to your members. And it is a promise that this person’s information is accurate. We know how challenging it is, but the minimum we can do is make sure that the provider is available, they’re licensed, and they’re in good standing. And, you know, that is something that feels easy, and I think we’ve shown today it’s not. And there are a lot of organizations that care deeply about supporting you all, through this. But, Ron, what’s what’s your key takeaway? I think I stole my thunder when Chris brought us back to the other slide. It’s the opportunity for finally to master the data. You know, I think for many payers, internal teams, they just want to make it easier for their teams to run fast. And if they’re fighting fires all the time to make sure that data in one system matches the data in another system, then they’re not going to be able to focus on the opportunities to provide a better product for their members. And we talk so much about ghost networks. We talk about compliance. We talk about monetary penalties and enforcement. But I think we need to talk about the opportunity, too, with better data, with better data coming out of providers, which is going to be possible at some point, especially if we have those good feedback loops and we work as a team, as Chris said, with the provider community. Can we get appointments faster for patients? Can we help them estimate their costs? Can we match them to the right provider better, the right high quality provider? I think all of these things are going be possible because these providers have their own share of mandates in terms of transparency and interoperability and APIs that you guys as payers can take advantage of. But we need to fix the basics first. And all we’ve been talking today about is the basics, and we just need to get that first so that we can run fast in the other direction and the positive direction. All right. So there’s some good questions over here. Are we reading these questions? I’ll stop burning do that, Chris. All right, there we go. All right. So at this time, let’s go ahead and address some of these questions that have come in during the presentation. Looks like this first one might be for Chris or Katie. What’s the biggest blind spot payers don’t realize they have, state exclusions or lapsed licenses and certifications? Katie, I’ll be receiving your answer there. In my conversations, I don’t believe that lapsed licenses and certifications is a blind spot. I think it is a very well known issue. And with with regulatory agencies like NCQA and others requiring an ongoing, license expiration tracking, ensuring that there’s no board actions against the providers in your network. I think organizations know that there’s highly likely, some some, issues there. Really, it comes down to the blind spots around exclusions. And I think today’s poll question really did show that. Almost eighty nine percent of the organizations that we look the networks that we looked at data for had an excluded, provider in their network. And and it’s highly likely based on what you shared, Chris, that that excluded provider came from a state Medicaid list, which makes it just really difficult to stay above board, on all of your providers. And, of course, we’ll just kinda go back to, especially if your systems aren’t talking to each other and you don’t have that singular data layer that really can speak to everything. So that’d be my point of view. I think there’s it’s not blind to some of the issues, but it is it’s it just shows us how challenging it is to stay compliant both to your regulatory agencies, to the government, and so on. So Katie, my answer would be D. When I was taking those, you know, all the above fill in the blank questions in high school, I’d always choose those. Probably why I wasn’t the valedictorian, but that’s a whole another webinar, right? I think what’s interesting about this is it really does depend on where the accountability lies, right? Typically, these are spread across teams. You have exclusions, you have licenses, you have certifications, and then sometimes in payers, you’re doing it across the organization with different data sets. And so I think what it kind of comes down to is like being able to get accurate data real time on an ongoing basis is going to eliminate these blind spots. So they’re all blind spots. Everyone who has that accountability thinks that’s the biggest challenge, but really the biggest challenge is how do you get accurate data on an ongoing basis. Okay, looks like the next question for Ron. Beyond compliance, how should a payer’s leadership team be thinking about this? Thank you. I teased this a little bit earlier, but around appointment availability and finding appointments for providers. But there’s been some interesting standards work that has been brewing in the interoperability community around what folks call smart scheduling links, where some health systems and practices are exposing their appointment availability in a machine readable way. And some national web based directories for provider search are using that to surface up providers who can actually take appointments within a certain time period, so within thirty days or what have you. But I’m also seeing some payers doing that too, which is really interesting. So that’s just one of the opportunities. Once you take care of the basics and making sure that you’ve got excluded providers and disciplinary action providers out of network, you can start running fast in that area. The other thing I’ll mention is the idea of being able to move fast with your network itself. So as you detect, as you remove providers from network for whatever reason, so disciplinary actions or directory inaccuracies or they’re not, they’re ghosts, How do you replace them quickly? How do you find the right provider with the right specialty that provides that care in that zip code? And also is ready to go from a credentialing perspective. So the same tools and techniques that Provider Trust is using for monitoring, a lot of those things will help a payer move faster in terms of primary source verification and credentialing, getting the provider into network faster. But it’s not just that use case. It’s finding the right provider. It’s making sure that they’re ready to go, loading them into network. So you’ve got to boost up every single one of those steps. It’s an IT thing. It’s a person thing. It’s a people thing, an organizational thing. But in the next five years, there’s going to be some payers that move really, really fast. And those guys have fixed the basics problem that we’ve been talking about today, and they’re already working on those things. Just food for thought. Yeah. Ron, it’s really fun when you start thinking about, like, when you get the basics done, like, how fast it speeds speed your team up. Like, just even thinking about, like, a bench for, you know, who’s next up within the network and and being able to have that ability to almost build that bench before you need it, is really kind of that next frontier. If you’re able to get that, you’ve got the basics done right. So that’s, love that, thank you. All right, this one looks like it may be for you, Chris. How does ProviderTrust’s platform close the four twenty day LEIE reporting lag specifically? Yeah, it’s simple but complex. Simple because we’re monitoring it. Complex in terms of what we have to go out and augment all that data. We have to make those relationship connections. We’ve got to go and build those data sets on the backside. So Ron talked a little bit about like where some of these payers are already have solved some of the basics. We’ve done that work to enable those organizations to be able to do that, right. There’s no way for them to build that on their own. There are these promises that AI is going to do that. I can tell you, we’ve tried to do that. It doesn’t work, but we’ll see if people are able to get the accuracy with AI without doing all the augmentation on the backside. But I think it’s just it’s being able to leverage the tools within the market to be able to speed yourself up to ultimately, and the reason why we’re in business is we want to serve patients. We want to eliminate fraud so that patients can get really, really good world class care. And so things like this matter. And so that’s ultimately for us is, you know, it’s simple, but complex. All right, that does look like our final question. So I’ll send it back to you for any closing comments. Ron, I would love to just hear kind of your vision of like where you think de facto is going to be in the next couple of years. And, you know, there’s just so much changing in the industry and what you’re hearing from customers. We think of ourselves as, I guess, the plumbers of the industry. We want to make sure there’s still work to be done in terms of completing the data set and to make sure really every payer’s network is represented. I mean, most of the payers are from a covered lives perspective, but there’s a long tail of smaller payers that are still working towards that. So we test APIs, we test machine readable files, we provide that constructive feedback to the payers and their vendors. So we want to complete the collection and just make sure that that’s ready to go. And so that everybody who needs to use the data, the payers themselves, government, consumers, the general public, that it’s all there for them to use. That’s great. I think for us and Katie, I’d be interested in your final thoughts. But I think using that plumber mentality, think we are like the filter, right? We’re looking at that data. We’re being able to help our customers filter out the bad actors, but also the people that they would want in their network. And I think that’s what I get excited about at night. If I could get excited about dreaming about data exclusions and licenses is being able to deliver that information in a timely manner at the time they need it. Because I think that’s where with AI and the technology advances, like it’s not just doing the work, but it’s being able to deliver it at the right time. Okay, Katie, are some of your thoughts? Yeah, well, what excites me about kind of, if we’re kind of sticking in the realm of the next five years, what excites me about some of the conversations we’re having is the focus on saying that we know, if we are payer organizations, we know that this data serves multiple parts of our our organization and utilizing that same data to to actually feed those systems. You know, at at in our world of technology, a lot of times, we we build something to solve one problem, and then we build something else to solve the next problem and so on and so forth. And you may have twenty systems working, on, you know, complementary issues. And so as a reminder on what we’re hoping to see with AI and others is it matters where that data is coming from, and it matters that that that data that it is pulling in is accurate. And so I’m excited to see a little bit more of the ability to say, hey, this data works in multiple places, and the technology and the resources are there to support organizations actually being able to take advantage of that. So we’ll turn it back over to our support host, Chris, and thank you all for the opportunity to share some of our insights with you today. We really appreciated it. Great. Thank you to our speakers. Thanks so much for that great presentation and for sharing your thoughts. Thank you to the audience for participating in today’s webinar, and this does conclude today’s presentation. Thanks so much again. Enjoy the rest of your day.