Alright. Well, again, we are thrilled to have you here. This, office hours is sponsored by HRSA and, financed under our contract with the agency. There's the contract number there, but, of course, we have a disclaimer, that HRSA has asked us to include that basically, makes it clear that they are there are nonfederal resources, specifically NCQA's work product, that haven't been approved or endorsed, specifically by HRSA or HHS. So just wanted to make that very clear. If you would like to, ask questions, please send those through the q and a. I've got those open in a window over here, which is why I keep cleansing up here. I've also got the chat open there. Please feel free to use the chat to provide any anecdotes, responses if there's any issues with the the audio or visual. Make sure you do chat to everyone, if you are looking for something if you want the the information or the the question you're asking or the feedback you're giving to go to everyone in the room. Otherwise, you can just if you send a host and panelist, I'll be the only one that'll be that will be able to see that. If you have questions about standards, process, things like that, please send those through the q and a. We do save those and share them with HRSA. And you can also raise your hand if you want to ask a question verbally or provide some, feedback verbally, and I can elevate you up to a speaker, and then you can, chat with us, directly. So just a little bit of background for those who are not necessarily, familiar. We, among the many things that NCQA does, we manage and are the governors as it were of the, health care effectiveness data information set, which has been under several different names, but always, given the same acronym, HIE does. Over the years, it actually started as specifically a health plan, set of measures, has since been expanded, in terms of its usefulness and its use, around the country. As you can see, we've got two thirds of the of the population basically covered by plans that are reporting HEDIS, and that includes all the Medicare plans. Seventy three percent of Americans have insurance through one of the plans that we accredit, which means that, of course, they are reporting HEDIS. And then we also have a large number of physicians working somewhere in various NCQA recognized practices, which includes the PCMH program, but also PCSP and some of our other, programs as well with over thirteen thousand entities actually earning recognition from NCQA in our various programs. That's accreditation, certification, and recognition. Those are terms that that, we use somewhat interchangeably. But in general, accreditation is for organizations. And, in accreditation, the big difference is if you are, approved, that's publicly reported. But if you're denied accreditation, that's also publicly reported. It's not the case with certification programs or recognition. And certification tends to be a little bit more limited looking at specific processes, things like that, and recognition is what we use, for all of our practice based programs. So a few years back and by a few years back, I mean, twenty five now. It's a little shocking to think because I've been with the organization with NCQA since ninety seven, and I started on the health plan side of things back then. And we I was part of the the the workforce that had to implement the first integration of HEDIS measures into our health plan accreditation, which was a very big deal. But we have had those integrated now for a quarter century. We do not we're not anywhere near that, level of sophistication with our recognition programs, with our practice based programs. But I think it's it gives an indication as to where we'd like to go someday because our mission is to improve the quality of health care, in general. And to do that, you have to have data. And as much as we stand by our our programs, and standards that are based on process and and, make sure operations are appropriate, the real question is, can you show it through the data that that you're actually being effective? My old boss, Maya Harkins used to say, I don't really care if you're wearing shoes if you can win the race. Alright? But if you can't, if I can't tell if you're gonna win the race, then I might wanna check out your footwear. You know, that's sort of, the the idea here a little bit. So what we are doing here in NCQA is we are moving toward the use of digital quality measures, and this is something that, I'll talk a little bit about in future slides. We'll get a little bit, high level too, in a minute. But the idea is to move toward a system of measures that are based on digital information as a way of having consistent measurement across different, types of organizations that are that are coming from systems in a way that hopefully are not necessarily, as labor intensive as some of the the quality measures have been. So we're looking at standards based data exchange. In other words, the the, FHIR standards being able to actually, use electronic systems to exchange data. Of course, interoperability is still a big deal among EHRs, things like that. We're also looking at measures, though, that are machine are written in a machine interpretable language. So something like CQL, something like that, or I guess SQL is is actually how you would refer to that, and incorporate the sort of the specifications of the measure that actually are required to calculate the measure appropriately. So we'll talk a little bit about how that works, and I'll I'll extrapolate from the slides into PCMH as we get toward, those sections of the of the program. And the reason that we're we're moving the whole organization towards digital quality measures is because, obviously, if you can pull data directly from health IT systems, that's going to be, far easier to deploy measures. We'll talk a little bit about the the the the background of of measurement in the next slide. This also reduces the sort of the interpretation or recoding of human error issues. When, back in the in the in the bad old days of of, paper records, which is where, HEDIS began, there had to be a lot of interpret interpretation and interpolation of things, you know, in terms of, were certain activities being done? Sometimes that was very clear, but sometimes it wasn't something that could be coded on a claim form. So when you're looking at medical records, particularly handwritten records, which is what we were looking at back in the day, there can be a lot of interpretation, needed for that. And that can lead to, to issues with, with actual accuracy of measures, things like that. We also wanna have standardized measures. Right? We wanna be able to compare apples to apples, and that really does require us to be able to have the same measures with the same specifications across organizations. And you can see how we've been doing that in PCMH with, the standardized quality measures that we're asking you to use now or that we're requiring you to use. That's the sort of the first step to having that apples to apples comparison. In health plan, of course, we're we're auditing those measures, and we're doing all this kind of, back end work to make sure that they are being done accurately. We're nowhere near that with PCMH, but and we're not releasing, you know, actual measures yet. We are literally at the baby steps that we want you to use these specifications and provide us with data that we can then use to compare, and contrast. So sort of, you know, where did we begin? I'm actually gonna look over here at this slide because it's a little larger text for me. But the old, sort of, HIE DIS system, where we, well, as I as I mentioned, in the beginning, it was all what we would call administrative data, which is actual record data, diving into records, actually having to pull paper records for patients that had certain specifications and then determining if if they met the the the requirements. Then we moved we also moved to claims data, and there was a there was a hybrid measure where you could sort of use claims data to to pull and then look into medical records for patients who were, who were who were listed as not having a service to see if it it actually happened. So for instance, very common problem, right, with, our community health center clients, the inability to to code both medical and behavioral health care at the same visit. Right? That that's a problem in a in a lot of areas that can you should lead you to what you're gonna pull in a claim form is going to be, less than the full suite of services that the patient received that day. Well, in a hybrid method, you could say, well, this patient has not you know, there's been no follow-up on depression, medication after medication, prescription. So I can go into the record, though, and see, did that actually happen but wasn't billed on the claim. So that's the hybrid method, which a lot of folks have been using for quite a while. Then we actually wanna move toward sort of the eCQM, which is where we are in terms of, where we wanted to be with with PCMH. Certainly, we're we're getting clinical data directly from EHRs. As we all know, though, there's another sort of, challenge with a lot of the EHR systems as to how do you document something so it's actually measured. We've talked with this, about this with, HRSA on many occasions. For instance, we require, depression screening, as one of our, knowing and managing your patient requirements in the in in the PCMH program, but yet depression screening rates seem to be sort of stuck at a very low level for UDS measures. And a lot of that was inaccessibility of data. It wasn't that it wasn't happening. It's that it was happening in a way that wasn't being recorded correctly to be pulled into data reporting. So all of that can can cause issues as well. Moving from there, we get to, what they're calling the electronic clinical data source method, which is basically using data from all the various electronic sources, that we have. So administrative and claims data, electronic clinical data from EHRs, HIE information, registry information, case management system. So, basically, it's the hybrid method, but using all electronic data. Right? Trying to find, is there any evidence for this in any aspect of the patient's record keeping, to be able to count it toward a measure? What we are aiming for, we are nowhere near there yet, is this idea of digital quality measures where we're going to have standardized data models and then using the same reference engine in terms of how to pull the data, and specify things appropriately so you're getting the right patients and you're getting the right information, and that any source system that is following the FHIR requirements could in fact be used to to pull those data. So that's where we're sort of removing. We're we're moving away from that traditional HEDIS measures into this digital quality framework. So what is our goal? Well, we have been using sort of traditional HEDIS measures since the nineteen nineties. As I mentioned, we did, we we sort of did away with the all manual, all chart based, measurements quite a while back. So everyone is at least doing hybrid method, if they are reporting data. And it's been a while actually since I've worked in HIEDA, so I'm not as sure what the percentage was sort of all electronic versus or all claims data versus, hybrid is at this point, any longer. But we also have, of course, local health data environments that are nonstandard. We don't know necessarily what systems are being used or how things are being recorded. And we are still working with paper based technical specs. Right? We still produce a book. It's now downloadable electronic format rather than literally printed, but we still have a book of HIEDA specs where we talk through how we're going to measure specify each of these measures and what to do in terms of, various, exclusion criteria. Right? So if somebody qualifies for a mammogram measure but has had bilateral mastectomy due to breast cancer, how do you identify them so you know that they're they're no longer supposed to be in that measure? Right? It's no longer appropriate for them to be. That kind of thing is is still all in paper based technical specs. And, of course, it's also retrospective. So HEDIS is gathered basically once a year and reported. But we all know that quality data to be really actionable has to be upfront and available. And the world that we sort of, I think, all thought we were gonna go into with electronic records is this idea that you would have dashboards and and feedback so that in real time, providers can see what's going on and and can actually, you know, not just get their their quality numbers up as it were, but actually provide the care that patients need, right, and that those actionable, clinical quality data would be there to facilitate that. Obviously, we're not quite there, to to to state, the obvious, But, certainly, that's what we're trying to move to. And and by twenty thirty, what we wanna see in terms of our HEDIS measures and, again, these are coming out of the health plan world, which also a little bit easier to assign. It still have the the problem of assigning responsibility to a specific provider. But in the health plan, at least, you know, they're a member of the health plan, itself, and so that does make the reporting a little bit easier. But we are looking again to use claims, but also EHR and other electronic sources. We wanna have, improper interoperability and automation so that the specifications are automated and, the systems can actually use those specifications without having to be interpreted, manually by a human. We wanna use FHIR based, data sources and have these digital quality measures. And, again, we want them to be perspective and focused on quality improvement. So it's not so much that you're reporting your HEDIS data. That's the sort of the secondary side of impact of having real actionable quality data within your systems that you're able to use. So that's what we're trying to inform by twenty thirty. Still think that's fairly aggressive given where we are. And, again, I'm gonna use this screen over here because the camera screen is far too small. So if you sort of, can see where where where we are in terms of transitioning, We have the the the measure years on top, sort of how we're providing the information, when digital measures will be available, the the use cases so that folks can understand how to, program and, use those measures, the certification logic and validation, so that we know that systems are working correctly, an actual SQL engine and then, hybrid data collection where you're actually going into charts, hopefully, replaced by all electronics. So digital introduction came in, two twenty twenty three. We are still providing paper specs. We have, some, measures that are have digital delivery. So we're sort of testing the waters for for that. We're still using sort of excuse me. Still sort of in that traditional quality improvement mindset where you're sort of getting data and looking backwards in retrospective, that sort of thing. Where we're trying to get to in this current time period is digitally enabled measures. So, again, we're still using paper specs, and so we're still also trying to get more, digital delivery of measures. We're trying to get the the administrative components of of measures, so sort of the eligibility requirements, that sort of thing, fully digital using the the quality improvement and population management plus health plan reporting to, as the use cases, and then also having different, ways to have that validation. And then we're trying to move to by twenty twenty nine and twenty thirty is having fully digital measures so that we have everything specified digitally even though all data reporting won't be digital. So you can still see there's there's options in terms of the logic and validation, object, in terms of SQL, use that kind of thing. But, also, we're trying to move away from the hybrid data collection where we're going into the medical record and having to have someone read it. So we're still sort of using hybrid data in that. We still wanna be able to pull from different electronic data systems to find the information we're looking for, but it won't be somebody having to read a chart to see if there's a note which manually, can be recorded as meeting. It's more if you can't find it in a claim, then you're gonna look in the records to see if something's been coded correctly, that sort of hybrid method. So it's a little bit different. I we're probably not gonna call it a hybrid method either, but it's still using all available data sources. And what we would like to do is get to a digital only, setup, where everything is is done fully digitally. We're getting, data in, from health plans, things like that completely. The the the measures are automated, all that kind of wonderful world. Again, will we get there? I think it's still an open question. Excuse me. And there was something I was gonna mention about oh, yes. You noticed in, the twenty twenty six standards, in fact, for PCMH, we've since added something to, CC twenty one, the unicorn standard, where we have the ability to get multiple, sort of multiple points. Now it's up to four points. It used to be three because one of the the the, things you can get credit for now in CC twenty one is being able to digitally provide data to a health plan. So as part of this this moving to digital, he does measures. We wanna get the data from the the practices to the health plans in a digital format. Again, standardized, automated, all those great things so that you're you're reporting the same information to all the health plans in terms of the the specifications. So that's part of what we're looking at in this, digit digitization. Why do we want digital quality benefits? Well or digital quality measurements. What are the benefits? Well, obviously, lower cost. Like, the idea is there should be much less work involved in pulling these kinds of measures, and then also less variability in terms of both, the quality of data sources that we're getting, but also in some of that interpretation because we still have people physically looking at medical records. They may be looking at electronic records now, but they're still looking at medical records. A human being is having to interfere having to intervene to gather all the data. Hopefully, we can move away from that. That's gonna be a lot lower, workload, presumably. We also wanna have architecture that can help health systems learn from their data and, again, have real actionable data to be able to to get better and provide better care. And that, of course, also means better value based care support. So as we move toward, payment systems that are value based, this kind of measure, digital measure system will be able to, be actionable, and provide the information needed for that value based care, but also promote more integrated care and reduced fragmentation as information flows more freely. You know? And why why is this sort of NCQA doing this now? Well, there's a lot of reasons. One is industry feedback. Right? The they're adding more measures is great, but adding more burden is not. So as we try to evolve the HEDIS measure set, we need to make it easier to actually provide the data. So from, question from my very basic understanding, but the goal is that the community health centers will work work through a FHIR program, and that program will be available to HRSA for real time data. Am I understanding this correctly? Yes. I think that you are understanding correctly what our overall goal is. We are very far from that from that vision. But, yes, I think that's that's but both UDS data would be going in real time, but also to any Medicaid plans you're working with, any other health plans, and also presumably at some point to NCQA for PCMH too or or the advanced primary care, which will be building on PCMH. So, yes, that that's exactly what we are, aiming for. Whether we get there or not, I think, is is is still an open question. This also allows us to to have interrupt or or we know that that we are trying to get to that interoperability environment where we actually can exchange data across different systems, and quality is one of those top use cases for for, interoperability. And then, of course, again, the value based payment arrangements, those are more common than certainly we need to have this kind of measurement in real time, not once a year, not quarterly, but in real time, measurement as well. So we have, digital, measures, available, and are in use right now. We are still implementing, in terms of some of the the use of digital quality measures. The electronic standards have certainly matured, and we have seen that happening. And so that's good. We are gonna be sunsetting our hybrid reporting in a couple of years, and then, hope to have that full, digital measure set for twenty thirty. So that is still, where we're aiming for. But how do we sort of how are we envisioning this, working in the future? So we're gonna have our measure architecture, right, which is how what the specifications of the measure are, what population we're looking at, what things we wanna see, having happened or, in this case of negative measure, not having happened. Right? So you have building blocks that are module modular. You can use the same logic, so that, through different systems so that, you can reduce burden and increase consistency. And, again, the the, improperability there, also allows us, is there to allow us to to exchange this data. We also wanna be faster in terms of measure life cycle. Right now, it's sort of an annual basis, that sort of thing. But can we, if we have digital, measures, can we create test and validate measures a lot faster and perhaps replace measures or improve them on a much, quicker basis? And that's hopefully what we'll be able to do with the HEDIS measure set. Also allowing for, update cycles that are shorter, which, again, are, you know, going to that real time data, and it leads to that continuous learning environment that we'd like to see. And so that's all important, for this digital measure. And if you think about some of the the, engineering electronics, the the the banking systems, these this kind of thing is already happening in a lot of industry. We're just a little bit behind in health care. In terms of of prioritization of measures, we're sort of, we wanna, use the digital data to be able to look at at the highest value measures again to reduce burden and improve relevance. So if you think about some of the HIEDA specs we've had in the past, I think my favorite example of a HIEDA spec that makes no sense, but it's based on the the quality of data we have, which was when we first put out our ADHD, medication management measure. Actually, being diagnosed with ADHD was not a requirement in the measure because the stigma of behavioral health, the limitation of behavioral health benefits all led to pediatricians being very reluctant to put ADHD on a claim form as a as a diagnosis. So when we did our measure specs, we found, oh, you know what? Actually, just being just being, prescribed ADHD medication is a better indicator of having ADHD than a diagnosis and a claim. So that led to sort of some of the the the data quality issues that led to specifications that looked weird, but we're we're gonna pull the best data they could. And that was one of the the the the examples I always I always use. So, obviously, if we can eliminate some of those issues and we can have better specifications, you may not need to to to specify measures or have the same number of measures. So if you look at diabetes, clinical quality, you know, could we focus, with digital measures? Would it tell us that maybe just focusing on h b a one c and not a lot of the other, services might be the best way to to assess the quality of diabetes care versus all of these measures and trying to see if the whole guideline is being followed. You know, that kind of thing might be telling us. Or maybe it's gonna tell us the the the analysis that it's better to have all the diabetes measures, met in a patient and sort of counted as as, everything or nothing. You know? Who knows what's gonna happen? But we hope that the digital, environment will allow us to do those analyses and hopefully reduce burden and and focus on measures that are the most impactful. And this also, hopefully, lets us use, data in a smarter, and more adaptive way. This is where we're gonna get sort of, so here here's an idea, in terms of how we would think about specifying measures in the future. This gets a little high level again. So current measure description for mammography is a percentage of people who are between the ages of fifty two and seventy four who have enrolled in a health plan for at least two years and are not excluded for clinical reasons and who've had a mammogram to screen for breast cancer every other year. So not excluded for clinical reasons would include folks who don't have breasts for, you know, men, those who have had the bilateral bilateral mastectomy or for other reason cannot undergo the typical mammography, things like that. Doesn't so if you recently change health plans, you drop out of the measure. Right? Those folks are not being tracked. If you, have, if we're looking at this in sort of every other year, that, makes a lot of sense on an average basis. But if you have the BRAC was it the BRAC two gene, you may wanna be undergoing mammography much more frequent, and and that may be a a better measure of quality for you, but we have no way to distinguish that now. And so that may be something that we can factor on with digital, measures in the future. Does not, consider the significance of positive or negative findings. In other words, false positives, false negatives, and that factoring into, health care quality. And, of course, doesn't account for patient preferences. And so if you think about what we might want in the future, are those patient preferences, social needs, their clinical risks all being accurately represented? And so, you know, if there's a cost for for a mammogram and you don't need to have it as, frequently because of your clinical risk, is there any way to specify that or the or the reverse? Right? Are we using the intervention correctly? You know? Would it be better to have an MRI every six months versus versus a mammogram every two years? You know, that kind of clinical data might be something that we're able to tease out and then and then specify the measure that way again. What happens with an abnormal result? Is that communicated clearly to the patient? Was the evaluation and treatment delivered? So the screening mammography is great for basic prevention, and we know that if you catch breast cancer early, you're gonna have better, outcomes. But we don't have any measures right now to look at those outcomes, and that's where the value based care would be wanting, you know, how many of those are are followed up on, how many are getting the appropriate treatment, you know, all of that kind of thing. What was the experience of access, timeliness, coordination, outcomes of care, and are we getting that only through satisfaction, or are we getting in real time data as well? And what you know, given where where, was the, who was contributing to the the care, and and what, level? All those questions could be answered with digital measures. So as we're thinking about, building these measures, we also wanna think about having libraries of information. So instead of sort of having these, you know, paper manuals and PDFs and all those wonderful things, so maybe we can start with common data standards, right, and, reusable building blocks to make measures better or faster. So we start with sort of foundational, based libraries, and that's, sort of the same data building blocks and, shared, specification roles that all the measures you're relying on makes everything work. And then we go into resource specific libraries that are looking at specific types of data, and how to pull information from those types of data, and then for specific clinical areas and then actually having a a measure that's useful, through all of this, sort of data filter. That's sort of the the goal at least, into the future. And we wanna have sort of this idea of measure packages, that provide a fully structured sort of measure set, that can have a can implement, support the full implementation lifestyle. So we've got clarity, providing some clarity and consistency. So you have a single authoritative set of measure definitions and logic. Everyone's working from the same playbook, and all the the systems are programmed the same. Right? Again, we're not relying on people reading a a a record. Having a guidance on exactly what kind of data make mapping and integration faster and, less error prone, and then, again, having the, systems have appropriate, standards of of of, operations and and, and quality so that we can, get good data and then accelerate that testing and implementation. So we would have things like a a measure flow, and we'll we'll show you what this is gonna look like. But a measure specification, that's gonna be using codes, value sets, things like that, but then there's gonna be a visual flow that can actually show users how the data are being, being gathered and delivered, a guide to explain sort of what the measures mean and what you should be doing after, the measure itself, is met, and then some sample FHIR bundles so people can see if their systems are working correctly, sample data details for that same reason, and then supporting evidence and parameters, you know, the the guidelines, things like that for the measure. So this is actually what the visual flow would look like. Right? So we again, I'm looking over here at this data. These are, patients with diabetes. So we're looking at, that glycemic status assessment. So you have start all the persons in terms of eighteen to seventy five, okay, and they have diabetes, diagnosed during the measurement period. And, again, that could be at least two diagnosis of date of diabetes on different dates of service. Again, eliminating some of those errors as someone put down the wrong code And it doesn't include necessarily, laboratory, claims, but you get to the die based dataset. Or, you can go down and look at something like an institutional claim or an EOB. So at least one diagnosis of data, of diabetes with a pharmacy claim showing that they're on some medication for, the die that would, control diabetes. Right? So this is a way that you could find a person meeting either of those, or, again, the same the same kind of oh, and medication dispenser. So I've tried I knew I I this this one always trips me up because I was trying to figure out the difference between them. So we could either have a pharmacy claim or EOP or an actual medication dispensation. So it could be through a pharmacy benefit or not. So that would be a way to to have two different ways to pull the data, for different patients based on different, data sets. And then, of course, continuously enrolled in a medical benefit, in terms of the right and then also putting them into the correct product line there. So commercial Medicare, Medicaid, etcetera. PC exchange also. And then the basic idea is you start there. Is the criteria met? If yes and it meets the initial population, then you're gonna continue on. If it's not initial population, then you're gonna stop there or if the measure isn't met. And the idea here is that we can start, on this slide, we start with quality knowledge, understanding the domains, the the topics, the data structure, the uses. We can then use that to, meet content engineering, requirements in terms of specifications, things like that. So we're getting those those, measures, and they're validated. And that can go back to the logic. Right? So we know if we know that there are ways to specify the measures that make the logic work better, that can go back to that quality knowledge. And so you see it's all a feedback that's going back and forth. And then getting to the actual digital quality solutions, which is not just reporting, but, again, those dashboards we would like, which would have, clinical data in real time, all of that. I think that's what we want to get to. But, again, this is definitely, probably into the future. And you can see how this would apply to PCMH as well. Right? This is looking at HEDIS right now, which already has those kinds of specifications. But the HEDIS measures, the UDS measures, the the standardized measures we're using in PCMH, there's a tremendous overlap. And presumably, at some point, they would all become the same measures, right, completely, and being able to pull data in the same way. So we could pull data from the practice level, as well as from the health plan level. And, hopefully, if not, reporting that to the public, which might be a little bit too much, but at least reporting it back in real time to providers and to teams in the clinical practice setting so that they know who isn't meeting, standards of care, who isn't getting, which patients are not getting the the full gamut of care that they need, and start to have real time, fixes for that. So that's the the ultimate goal. That was really what we wanted to cover in terms of the geometry strategy today. Happy to take any other questions or feedback you've gotten at this point. As I said, this is sort of a little high level and and a little bit different than what we've been doing in the past, but we think it's important to bring in sort of not just sort of the the day to day PCMH information and facts, but also what we're doing into the future as well. So I'll give you a minute to see if there are any questions. Again, if you have questions about anything PCMH related or HRSA related, please feel free to send those along as well. We don't need to limit ourselves just to the digital measurement strategy. Just a little, advertisement. We do have a series of trainings coming up this fall, both, in specific topic areas. So we've gonna we're gonna have a behavioral health integration, chronic care management, person centered outcomes, and then we're also gonna have a training on the results of our advanced primary care pilot, which included a large community health center. So we're going to to evolving, with PCMH as the basis, but then moving up above that, or moving beyond that in the advanced primary care space as we still work, on that program. Those will all be coming up. Plus, we're gonna have a five part series of technology trainings. We did start to advertise those, but we had some logistical issues that we had to reschedule. But we are gonna be doing those as well. We're looking at how technology is gonna impact, our clinical care settings in the future, both sort of technology we have now, but also stuff into the future looking at AI and that sort of thing. So, we're very excited about all those trainings, so stay tuned. Alright. I don't see any final or any questions or any feedback, so I'm happy to give folks twenty minutes back of their day. We thank you for joining us. And, again, this will this was was recorded, so we will have it up on our website in a couple of weeks. Alright. Well, we thank you again for, attending today, and we hope this was helpful. And we will hope to see you again in the future office hours or training for NCQA.