Quality & Quantity: Essential Frameworks for Clinical Data Operations for Payers

Stephanie Broderick:
I am Stephanie Broderick. I am SVP of Provider Solutions and I’m really excited to moderate this panel, including our partners Velox Health Metadata. I’ll have them introduce themselves individually. Today we’re going to be talking about quality and quantity. Quantity being from the Velox solution quality coming from the Clinical Architecture solution, actually quality coming from the Velox solution as well, and essential frameworks for clinical data operations for payers. And so I’m going to go ahead and we’ll start with Michael B, go ahead and have you introduce yourself.

Dr. Michael S. Barr:
Yeah, we have a bunch of Michaels, MB, MK, Michael Barr, the Chief Medical Officer for Velox Health Metadata.

Michael Klotz:
Michael Klotz, Founder and CEO Velox Health Metadata Inc.

Charlie Harp:
And I’m Charlie, AKA. Michael Harp from Clinical Architecture CEO,

Dr. Michael S. Barr:
Also known as MH.

Stephanie Broderick:
Alright. And Michael, I’m going to go ahead and turn this over to you because we’re going to start,

Dr. Michael S. Barr:
How can he gets it? I don’t

Stephanie Broderick:
If you want to go ahead and advance it. So today we’re going to start with an overview of the Velox solution. Then we’ll have Charlie talk about the PIQI Framework and the PIQXL Gateway, and then we’ll talk about how they’ll work together to address these challenges that payers have around accessing the best source of data for their operations.

Michael Klotz:
Okay, thanks Stephanie. Yeah, so as we have mentioned, we are in a partnership with Clinical Architecture. Super excited about that. Obviously stellar reputation and a lot of already technology at play. At Clinical Architecture. Velox is fairly new, but my co-founders and I, we’ve been around, we’ve been around in interoperability, we’ve been around in quality, digital quality specifically the last few years. And we’ve seen a need for what we call clinical data operations to evolve into something that deals with what we call structured realtime standard space clinical data. And that’s what we’re all about. And while you probably know something about clinical architecture, and Charlie’s going to talk more about the PIQI Framework today, we’re all about assessing in this case what a payer’s clinical data operations are look like today. We scored out, we standardize it, we benchmark it, and then we do another thing which we call, we identify opportunities.

In other words, now that we know what you have, we also scan the ecosystem and figure out what else can be useful to your clinical data needs and basically serve that up on our platform. That’s at the very high level what Velox does at this point. And like I said, we call it the payer enablement platform because we service payers at this point, but more to come in the next few years. So we have a pretty extensive product roadmap on that. Now though, to frame up this conversation today, we obviously introduced this idea of quality and quantity, the two dimensions to clinical data and on the quality side, and I’m speaking for clinical architecture here, if you don’t mind, feel free to obviously chime in here. But quality is how complete completeness is a term we like a lot. And you hear me talk about completeness a lot in the Velox content, but I think it also applies to quality.

How complete is the data on a per record basis? And that has a dimension of what we call an absolute scoring. And again, borrowing heavily from the PIQI framework here. And then the other thing that we’re doing, we have a heavy focus on not individual use cases, but how many use cases can a set of data service because the more use cases the data can be used for, the more your ROI and the investment and the money you spend on the data on acquiring the data in the first place. So fit for purpose by use case, which is sort of a binary, it’s either good enough or it’s not good enough. Those are sort of the dimensions that we look for. And obviously PIQI can do very well on the quantity side. Quantity is a very crude way of saying completeness. You get all the data that you actually need for your use cases.

So do you cover all your membership or your population? Can you attribute, do you have all the data to attribute to what you need for your use cases? And we’ve come up with this term, I think we came up with it, I don’t want to take credit here, but I think clinical events is a good way to sort of have a broader frame around the types of data that we’re looking to find for the use cases for payers specifically. But a lot of those obviously apply to other stakeholders. So we have another concept we call data channel, which is effectively, there’s sources where the data comes from and we like the idea of the data ideally coming from the authoritative source. So from right where the data gets generated and then that data can come to the requester or in this case the plan in different formats.

So a data channel is, the data comes from a health system and it happens to be a FHIR API. So the two components together make a channel. An alternate channel might be, there’s a non-standard flat file that gets generated quarterly, but it comes from the same source. So that will be a second channel and we score that individually in the channel level, but we also score it overall. So we have a bunch of KPIs, and we’re not going to get into a lot of detail today, but obviously “KPI”ing and standardizing and scoring, all that is very important component of assessing where clinical data operations are at and where they could be. So no surprise here where the quality dimension, we primarily talk about clinical architecture and the quantity dimension, that’s where we operate with phlox primarily.

Okay, so I’m going to introduce at a high level what we call our Velox Clinical Data Exchange Scoring Model. The top is just sort of a cutout of what our KPIs look like or can look like at a very high level from a health plan. But we have this idea or this concept called a complexity score. So a complexity score from zero to 10 basically tells you on a standardized scale, how good or bad is a data channel or a whole group of data channels. So there’s a few input factors or a number of input factors that we score and that makes up the score. And obviously they’re all important. They tell us a lot about the data, where it comes from, how long it takes to get to the requester, how many times the data gets moved around before it gets to you, how many times the data gets transformed before it gets to you.

And then in the extreme cases, is there manual abstraction involved? Is there an aggregator involved, right? That’s not necessarily a bad thing or a good thing, but it’s good to know that. And then there’s also internal only components. So there’s data that once it gets to you, there’s still best practices that may or may not have been implemented. So clinical data integration, which is basically a centralized, standardized data repository for a clinical data, that’s a best practice. And sometimes it exists, but not all the data goes through, sometimes it doesn’t exist. And there’s all kinds of permutations on that. And another one that we’re scoring because we fully anticipate and see that a lot of use cases are moving towards FHIR, and generally our clinical data exchanges are moving towards F is do you have a just in time FHIR capability, which basically means you may not source all your data as FHIR, but you may need to transform it just in time for your use cases.

And digital quality is obviously a great example for that, right? So those are some of the components that go into that. Now the other side of that is, so the complexity score tells you how bad is it? What you actually have the opportunities score tells us or tells a plan, what else is out there in the aggregator or on a channel by channel basis that might actually help you be more complete with your data and help speed things up, help make things much more efficient, may reduce the cost of actually acquiring that data. Because the other thing we’re seeing a lot now is that we’re going to need a lot more volume, but at the same time we want to bring the cost down. So the per record cost or the per clinical event cost needs to come down dramatically. And again, that can happen if we tap into structured standards based clinical data sources, especially if they’re covered by certain fee rules like the one from ONC as it originated in 2020.

So that’s the potential to improve on what you have. And then, whoops, the other concept that I want to talk about really quickly is each channel has a weighted score. The idea there is that if you have a population of a hundred thousand members and the data channel only has a hundred members at it, improving on that doesn’t really move the needle a lot. But if it’s 20% of your population, it moves the needle a lot and therefore it should be treated with your appropriate priority. So the weighted score is basically saying on the average number of members is sort of like your unweighted score, but if you have more members than the average, then your weighted score is higher and therefore the OI should be higher and therefore the priority in implementing that data channel or that opportunity should be higher. So that sort of a high level overview of our CD scoring model.

Stephanie Broderick:
Great. Yes. Next I think we’re going to have Charlie take you through then the PIQI Framework and the PIQXL Gateway and talk about how that works and then we’ll talk about the two solutions together.

Charlie Harp:
So some of you might’ve heard of the PIQI Framework. I’ve been talking about it a lot at himss, and just in a nutshell, the idea of the PIQI Framework is to have an objective way of measuring the quality of data that’s moving through a pipe. So getting data from a data source, scoring it against a standard rubric, and I’m not going to walk through this in detail, but the idea is at the end of the day, you know that the data coming from a particular source is a certain quality or is not, because to go back to what Michael’s saying, when you look at the data and you look at all the metadata about the coverage and about the complexity and about the latency, that’s all really important metadata. But the quality of what you’re getting and whether you can actually act on it, leverage it, consume it, is something that is very important to clinical architecture. We’re in the data quality business and whether the use case is payer, whether it’s provider, whether it’s life sciences, our objective is to raise the quality of patient data the way we exchange it and store it across the entire ecosystem because a rising tide raises all boats and we all really need data to be high quality so that we can make use of it to improve patient outcomes and how we understand the universe around us.

Stephanie Broderick:
So the outputs of the PIQXL gateway is a score, but can you also talk about some of the ways in which it slices and dices the data, things that you can glean from the data at a more granular level?

Charlie Harp:
Absolutely. Because the PIQI framework is designed around this concept of measuring things at an entity level and measuring it against a usability rubric like USCDI V3 V2, or really any other rubric that you designed to measure usability and applicability of content. When you look at the data, not only can you see down to the attribute level and the data class, are my meds good? Are my labs good? Is my immunization data good? My problems, my procedures? You can tell whether or not those things are usable according to an agreed upon rubric. But if it’s not usable, there’s also enough information to tell you how to get it usable. I really think that in the world we live in today, people share data and I honestly think they don’t know if it’s good or not because we are not measuring it. We’re not measuring it in a standard holistic way. I kind of believe that if we have something akin to a credit score that says, what is the score of your data, that if nothing else social pressure is going to make us want to make it better, if we don’t have a financial reason to do it, there’s a social reason to use it. Because if your data’s bad, then who’s going to want to try to use it?

Stephanie Broderick:
Before we go into the joint solution MB, I wanted to give you a chance to just kind of talk about our journey together and your thoughts about the partnership.

Dr. Michael S. Barr:
Sure. First of all, very excited about the partnership and in my mind, I’m a physician, these guys can talk all day about the frameworks and then all the, what’s the word you use Charlie about the canonical, all that kind of stuff. Like, okay, put that aside. When I think about this partnership, I think about what we do, what they do as the following. We help find data, clinical architecture helps refine data. So find, refine, I mean that’s where you get the quantity and quality dynamic. We’re better together because we do that together and the opportunities, although MK was talking about the health plan and the provider side, that’s very exciting. I’m waiting for us to get to the clinician, ACL health system side and everything that he spoke about in terms of what we do on the plan side is going to translate and very well to the clinician and system side, and we’re just on the roadmap to get there. So I’m excited about this with Clinical Architecture, I think again, better together.

Stephanie Broderick:
Great. So let’s go ahead and give the clicker back to Michael so he can take us through the joint solution.

Michael Klotz:
Alright, so what you’re looking at is, I apologize, it’s going to be hard to ease you into the whole platform, but effectively what you’re looking at is what we call an opportunity screen. So each row effectively represents an opportunity which could become a data channel. So we’ve identified in this case a number of endpoints or whatever the format might be, and therefore the transport accordingly. And we’ve scored that and say that the more blue, the more opportunity to improve. Of course we have in this context, we have new opportunities, which is basically you’re getting data from a source where previously you never get structured data before. You may have abstracted records, but you’ve never actually sourced structured data from them. So those typically score high because we basically score them 10 minus whatever the minimal complexity of that opportunity would be and that you see that also.

Then eventually we’ll do ROI projections, and obviously those have high ROI as well. But the idea is that each opportunity will then if existing also have a quality scores. The second to last column on the right there, we put the PIQXL logo in there because that’s where we’ll pull through PIQXL scores or PIQI scores to immediately show what these different channels or different potential channels or opportunities, how they score. And I dunno why it does that, but this is effectively a detail screen when you click through on any specific opportunity. So there’s a lot of information there. The green box is ultimately what it comes down to, right? This is where we see at the rank. We’ll also we will eventually say implement in this order, right? In order of absolute ROI, we will rank 1, 2, 3, 4, 5 how the opportunities should be implemented in order how many members are attached to which again, the more important, the more also the ranking, the higher the ranking typically.

But in this case, the opportunity score is 5.1, so that’s a good score. On a scale from zero to 10, ROI dollars, ROI multiples will be populated in the future version. We also will inventory more and more will be called certification. So is it USCDI version three for example, or four or five and others along those lines. But again, the quality score you see on the individual rows would come through there. What we’re also looking to do, or more or less, what I think clinical architecture will potentially be able to do here is not just give us a score of the existing data channel but also say, Hey, if you then do certain things with that data, which in this case for example, the pivot product could do, your score may be 74, but you can take it to 88. That’s all the stuff we could pull through and then bring basically right to your fingertips as a plan and show you that.

So you don’t only know what you’re getting, but you also know what you could potentially improve on that. Right. And then we talked a little bit about why use cases are so important, and again, PQ will help us, or an overlay on PQ will help us basically say which use cases can this data satisfy or this particular opportunity or channel? And the more the better of course, because we have this concept called ROI stacking where if data satisfied one use case, you may not have a good ROI or ROI at all, but then you do the quality reporting, you do gap closure, you do risk adjustment, you do fraud, waste abuse. All of a sudden you have a very compelling ROI and the same set of data. And again, we can basically do the math on that if we know which use cases the data satisfies. So that is effectively how we look at this from a Vox perspective, from an opportunity perspective.

Stephanie Broderick:
Can you advance to the next slide?

Michael Klotz:
Yes.

Stephanie Broderick:
Alright.

Michael Klotz:
Oh yeah, my conceptual slide at a very high, I’ve seen this before.

Dr. Michael S. Barr:
I hope you’ve seen it before.

Michael Klotz:
Yeah. At a very conceptual level, what you see in the center there, a gray box which we call the payer enablement platform. That’s a current existing payer platform. We currently have about 80 plans on there, approaching a hundred and we inventory and we do opportunity sourcing for all these plans. So our datasets getting interesting at this point, let’s put it that way. But that is the cloud platform that manages data channels, sources, opportunities, so all the metadata we talk about. So we named the company very deliberately Velox Health metadata because we don’t touch PHI, we don’t touch the data itself. We touch all the data that describes the clinical data, what we’re going to deploy in phase two, probably Q2 is what we call FHIR enablement components where the requester, again, in initially the payer will be able to deploy this component and feed on all the metadata we have in the platform and effectively very little or nothing about FHIR and still take advantage of it.

That’s the goal here. And then PK can score the source, which is going to be very helpful also for the source because again, it goes back to the credit score analogy. If you have good data and you can prove it, you probably can have people make good use of it and potentially monetize that within the allowable rules. The other way data would certainly need to be scored is once it actually enters the control of the health plan, so in this direct model, they should be the same, but not all data comes direct today, the primary way is it gets transformed. A vendor is in between or an aggregator and HIE or Teka going forward more and more. So it’s in the center. So what comes out of the source may not exactly be what you’re getting once the data gets to you, but again, the idea is score it there and then potentially upcycle that to use a term that we’ve all known from before with pivot, right? And that’s effectively how we create value together in this concept of how we look at the clinical data ecosystem.

Stephanie Broderick:
Fantastic. Can you go to the next slide? All right, we’re going to switch over to Charlie and talk about,

Charlie Harp:
I’ve never seen this slide before.

Dr. Michael S. Barr:
What

Charlie Harp:
Is this?

Dr. Michael S. Barr:
Charlie? Would you like me to explain it? Sure.

Stephanie Broderick:
We’re going to switch over to Charlie and show the granularity of once data has been scored, the type of information that can be surfaced about that data source so that the data can be improved.

Charlie Harp:
So the idea here is imagine you’re in a platform like Velox and you see the PIQI score and you say, I want to drill into that score and see the specifics. This is an example and I’m going to stand up. This is an example where you can see for a source. So we have two sources in this particular channel. Ideally, if you were going and looking at one source, you would just see the one source. And what you’re seeing down here is you’re seeing a message volume, you’re seeing the rate of critical failures for the use case you care about. You’re seeing the number of records that make it through clean the number of messages that are a hundred percent clean, and you’re able to see kind of the total aggregate score for that channel against the use case, which in this case is USCDI version too.

So this kind of gives you a high level picture. You might look at that and say, that’s not great. There’s a lot of critical failures. You can look over here and see kind of a high level picture of where the problems are, and then you can drill into it and say, well, if I start looking at the individual fields and data classes, what is going on with each one of these? And this is kind of in the weeds view, but you can go in and see, for example, here the medication drug is 77% passes the test. And if I look at the reasons why it fails, here are the reasons it fails. 66% of my failures, of the 33% or the 23% are because the concept’s invalid. It came in and I can’t validate the concept. And in this case, my conformance is RX norm or NDC.

If I look at, I can say less than one percent’s unpopulated 4%, it’s an invalid medication and 30% I have an incomplete code, the medication’s not populated, so I have 77%, is that good enough? Yes or no? That’s really up to you to decide. The other thing you can do is at a data class level, this goes back to the question about use case. You can say, well, I really care about immunization data and from this source, the immunization data is great. It’s 94%, actually 94% clean, 97% on the quality scale. I can see for the labs I’ve got 13% critical failure, 49% quality score, but nothing is making it through 100% clean. Because the important distinction here is a critical failure is something that you said if you fail this test, that’s a showstopper for me. So in this scenario, if the lab data is my use case, I might say I’m not going to use that data and probably, or I’m not going to pay full price for that data. I’m going to have to work to make it usable.

And then here’s just one more example where for these sources, I can compare them and I can see how they rate for different use cases. So this is doing a USCDI v3, and here’s v2. So I can see version two, your quality score is 76%, but if version three is what I want you to be compliant with, you’re at 51%. And if I were to create a rubric for version four, these guys would probably be at like 30%. So it really does allow you to dial into the use case and see what you’re going to do.

Stephanie Broderick:
And to kind of go back then, Michael, to the slide that you showed with the use cases, you could see those use cases showing up with scores because what we would test might be variable across the use cases. Different data matters more for one use case than another. Yeah,

Michael Klotz:
Exactly.

Stephanie Broderick:
Alright, so I’m going to go ahead and switch to my questions. And so MK, the Velox scoring model includes CDI and just in time FHIR. Can you go into those a little bit further?

Michael Klotz:
Sure. Yeah. So like I said before we distinguish between external and internal. When we talk about clinical data operations, the idea is external is mostly you’re dependent on the sources. What they have available to you is what you’re restricted by. Internal is entirely in your control. What do you do internally that’s up to you and how you want to make investments and how diligent and disciplined you want to be about your clinical data operations. So these two components are factors that we consider to the internal score. And I think I mentioned before, just in time FHIR is a capability where you may not source all your data and FHIR just yet. It’s going to take a while until we get there, but you still want to be able to feed your use cases with all FHIR because that’s the requirement in FHIR, CQL or digital quality is a prime example for that. So we basically also consider what are your internal capabilities, what you can and need to do with that data, and how well are you scoring on that. And clinical data integration is again a centralized repository where you standardize and aggregate and de-duplicate and potentially do other things with your clinical data. So this is all best practice stuff that we score. So you get a full view internal as well as external about your clinical data operations.

Stephanie Broderick:
In your dashboard example, you showed complexity and opportunity scores and there was also a donut graphic labeled data channel format. What’s that for?

Michael Klotz:
So the donut, which we plan to tell you a whole lot more about going forward, but the idea is it’s actually on a very early slide, but that’s fine. The donut is effectively a full inventory of clinical data channels and opportunities. So again, red is bad, green is good. So if somebody says, what am I doing with this platform? The short answer is your job is to make the donut green. Green is FHIR version four or higher, and red is custom formats with quarterly and it takes forever to get the data and on and on and on. And everything in between is either different standards but not quite FHIR and or opportunities that you haven’t laid up yet. That’s sort of how the donut comes together. But if you enable all your opportunities and you go all towards FHIR, the donut gets greener and greener. That’s the idea. So it’s the one KPI where you look at it and you know exactly where you stand at a high level of course.

Stephanie Broderick:
Great. MB, I’m going to switch to you. Let’s talk about gap closure. Can you elaborate on why complete data is so important for this use case and what is gap closure?

Dr. Michael S. Barr:
Sure. So we’ll go back to the use cases and I’m going to combine gap closure, risk adjustment to the really important use cases in the current value-based payment environment. And all the struggles that health plans are having currently can focus on health plans, but also you can say health systems, accountable care organizations, anybody taking a risk, they’re struggling to close the quality gaps and improve the measures that drive their performance and their payment. If you add risk adjustment, same sort of challenge. And to improve upon that, getting more data but not just more data, better data, and knowing where to find it and how to refine it is critical for them to achieve it. And that’s what the current measures we use in the current risk adjustment models as our colleagues at NCQA evolve new measures, different measures, more complicated measures, digital measures, and the need for that data only grows and the need to find that data and get it through all this wonderful technology, this platforms becomes even greater.

So it’s the smart health plan, the smart health system, the smart practice that’s going to start looking and say, okay, here are all my data sources currently, these are not satisfying our needs. Either switch, find something better or make them improve it using the levers that these tools together provide. Same thing for risk adjustment. I need the complete information to make sure not only may a risky adjustment, but a risk adjusting accurately, effectively, and without burden on the clinical systems to actually document all this. This is the way to go. There is nothing else like this that’s going to move us and move us in this direction and match the acceleration towards digital quality measures and better measures because we need those to make value-based payment models succeed. The current measures as good as they are, are not good enough to differentiate high performance and low performance. So there’s going to be a continuous effort to improve and expand those measures.

Stephanie Broderick:
You want to add anything?

Michael Klotz:
Yeah. So one of my big interests is within the gaps, I’ve always had this idea that there’s gaps that you think you have because the data just doesn’t tell you otherwise because you may not even receive the data and then there’s actual clinical gaps, but you only can tell once you have all your data. So based on very informal polling I’ve done with a number of folks at health plans, it seems the numbers 80, 20, 80% is actually data gaps and not real care clinical gaps. So if you get more complete data, a, most of your gaps go away and you can truly focus and spend money wisely on the ones that remain, which are truly gaps that need closing and not just gaps that you think you need to close because you don’t know any better. So I think that’s a very important concept as well.

Dr. Michael S. Barr:
Actually, let me add onto that. The longest route to improve quality gaps is to try and change medical practice. So if you try to change what happens in the field and you have to do that to some degree, you’re not going to get there if you focus all your energy on it. What MK was just saying is finding the real gaps so you can focus on where you can improve those gaps by improving the quality of your data and then focus on the 20% that may actually require some tweaking in the field.

Stephanie Broderick:
Okay, great. All right. Also for mb, the shift to digital quality comes up often. How is more complete clinical data important in that context?

Dr. Michael S. Barr:
I think I anticipated your second question and answered it before because just harping on value-based payment models, we need to do better in terms of differentiating sort of who’s high performing and merits the increased reimbursement versus others. And that’s only going to get harder and that’s going to drive different measures, better measures. And to improve upon your performance on current and future, you need more complete data that meets those use cases. And the risk adjustment, you can see all the different opportunities that this analysis of data from finding it, identifying what to do and then refining it to keep using that same term through clinical architecture. That’s the model that’s going to get those performances improving. And then the ROI becomes very tangible.

Stephanie Broderick:
Well, I’m going on to ROI. So mk, a key concern for payers is the cost of interoperability or clinical data ops. Can you talk about how the Velox platform can assist with modeling and realizing ROI?

Michael Klotz:
Yeah, I think that’s something we’ve identified very early on because it is not only a big change, it’s also a big investment. And if it fails, obviously there’s not just consequences in terms of, oh, we wasted some money, but you are operationally very, very much behind the curve. So what we try to do, and again, this is where the use cases come in, we’ve come up with ROI modeling that will introduce gradually use case by use case into the platform. We’re starting with digital quality where we basically say you can model the ROI of your investments. So we have categories of like, this is what you need to spend on infrastructure, this is what you need to spend on use case specific things. This is how much it costs to source a record from a source versus from say, medical records collection abstraction, which is obviously very pricey.

And the denominator is the cost, the numerator are the different use cases. What is the benefit or what’s the return on having a higher RAF because you’re doing risk adjustment really well. What’s the benefit of doing better on value-based care arrangements? What is the benefit of getting higher HEDIS scores? Because you’re sourcing all the data, you’re sourcing the right data. So ROI think is a key for people to make informed decisions about making investments. And the other thing is, you may remember I talked about ranking opportunities, right? So this is not an all or nothing where you go, oh, okay, so we’re going to spend millions of dollars and hopefully three years later this thing actually works. The idea with the ranking is you go, oh, we’re going to spend money on implementing priority one, which is ranked by absolute dollar ROI modeled. And then you should also start seeing the return on that, right? And then you keep working down the list and the more complete you get, the more you see sort of systemic return as well because you can reduce the things that make things really, really messy. But we believe that modeling ROI is really, really key. And while we see the numbers, we know that everybody’s critical of, oh, you’re a vendor. You want to just make this look really good. The idea is obviously while if you have your own numbers, which you should plug your numbers in and see actually what the model looks like.

Stephanie Broderick:
So Clinical Architecture and Velox have been talking for over a year. We did another presentation last year actually talking about PIQI with Rick Moore, and a lot’s happened in the last year. And I have to say it took a while to get to a point where we kind of understood what you guys were doing, you guys understanding what we’re doing, and to actually see it come together, mocked up and being a real thing is really, really exciting. And Charlie, maybe you might want to just weigh in on the journey that we’ve been on and your thoughts about how these two solutions fit together.

Charlie Harp:
Sure. I think that there’s a lot of, I mean, PIQI by itself is a neat concept, and the PIQXL Gateway I think is a very cool piece of tech, but you got to have people that understand the value of it and help you get adoption. For us to have a US to see the value of scoring the data partners like Velox who have real life use cases and are pulling together other metadata to drive ROI, and it’ll allow us to kind of factor into that. And I think it’s a great opportunity for us, and I think we add something that is kind of unique and valuable into the metadata they collect, and now we’ve just got to bring it all together and make it happen. I think that the people that are going through the work that they have to go through to incorporate data into what they’re doing, I mean, these guys know it’s non-trivial to take data from somewhere else, and it’s like to make data sharing worth it, the data’s got to be worth sharing. And so having that additional piece of data and having the other metadata that knows whether or not it’s going to cover all the things you want to cover, I think it’s a really powerful combination specifically for the use case that Velox is driving.

Stephanie Broderick:
All right.