Does Healthcare leverage public health data effectively? On this episode of the Informonster Podcast, Dr. Don Rucker, former National Coordinator for the ONC, joins Charlie Harp to discuss how Health Information Exchanges are in a unique position to re-imagine and pioneer broad-based public health approaches to data.

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Hi, I’m Charlie Harp, and this is the Informonster Podcast. Today on the Informonster Podcast, I have Dr. Donald Rucker, the former National Coordinator of ONC, here to talk about the potential future role of healthcare information exchanges in helping us across healthcare leverage aggregated clinical patient data. Good morning, Don.

Good morning, Charlie. Thanks!

First, I wanted to kick this thing off by saying thank you for your service. I know that doing, uh, stepping into a role like the National Coordinator for ONC was a non-trivial blip in your life. So I just wanted to say thank you for all the work, the good work, that you did.

My pleasure. Government service is incredibly complicated on a personal financial level, but yes, it was my pleasure.

I’m sure it was. So when you and I were talking, because you and I have known each other for a while now, and you and I were chatting a few weeks ago and talking about what we could talk about on the Informonster podcast. And one of the things that you brought up, which I thought was really interesting, was this notion of how we in healthcare could leverage healthcare information exchanges for different uses when it comes to accessing and understanding what’s happening with patients in our populations. You want to expand on kind of what you were thinking around that?

确定。健康信息交流已经由于d, well, they’ve been around probably a bit longer in some cases like Indiana. But on the national scene, they were funded with a $500 million series of grants, uh, from the HITECH Act relatively early in the Obama Biden administration, which got a lot of them started. Some of them have had different trajectories since then. At ONC, we were involved in that initial funding round and there wasn’t really follow-up funding on that, and so it was a bit of a quiescent thing as we were working on interoperability, and information blocking, and API access for smartphones to really make the healthcare economy much more consumer-facing. But one of the things that came up with COVID was really thinking about public health data and then more broadly other data available. As everyone knows, you know, (we) had lots of challenges when finding out the nature of the disease in COVID, you know, and it took literally several months. Now you can obviously argue about if some of the folks who should have told us had told us earlier, but you know, that’s sort of water under the bridge for this pandemic, but we had a lot of challenges finding out about what patients had it, and how to think about it, what the outcomes were. And the public health authorities, it turned out, really had some very big limitations on the reporting infrastructure that we’ve built out, and it’s worth understanding what those limitations were. So I think one limitation was simply that a lot of it was focused on very specific diseases or about roughly a hundred reportable diseases. And, you know, you know many of them from childhood textbooks, right? So tuberculosis, small pox, cholera, other diseases. And then there was syndromic surveillance that was focused on sort of respiratory presentations often in emergency departments. But we haven’t really funded or architected sort of a broad-based public health approach that can be a sentry, if you will, for let’s say a pandemic where you don’t know what the symptoms are, right? So where you don’t know a priori, oh (sic), it’s going to be an upper respiratory type symptom. And I think that that certainly got lots of folks thinking about how do we change that, and thinking about this, I was very pleasantly surprised by quite a few of the state and some of the local health information exchanges already had extraordinary data on COVID that was very rich for a couple of very interesting reasons that I think we’ll be able to get into today. So that’s what got this whole thing started, which is how do we get better data for public health? And when you think about that, gee, why don’t we think about extending some of these concepts to other federal agencies that have massive health data needs that you could argue are imperfectly served today?

So part of what you’re talking about is the way the system works today in public health is, it’s almost like, “Tell me when you found something.” And the onus is on someone who would have already identified something as opposed to a system that can actively help you look for something.

Precisely, yeah. You essentially have, um, it’s like you’re fighting the last war –

Yeah.

– as opposed to the next war.

好吧,我发现,你知道的,我花了很多my career in clinical decision support, which is, um, you’re almost trying to stop something. And when I first got involved with quality measures, and working with folks on quality measures, it’s a very different point of view. And even when it comes to some of the things we’re doing with reporting, it’s much more of a rear-view mirror perspective than clinical decision support, which is a much more, you know, “Try to swerve not to get the person in the street,” type of mentality.

这是一种把它。我没有想到it described quite that way (laughter). Now that you point it out, there is a lot of overlap. You know, I think we’re in a newly enabled era, right? So the public health thing we talked about, with mandated reporting and the huge limitations of that, you know, which that goes back 150 years easily, if you look at quality measures, those go back, I think in a sort of a rich sense, roughly 20 years, uh, when the laws were changed so that Medicare could differentially pay based on a measurement of value. So it wasn’t just… Before that, as folks may know, Medicare basically had to pay everybody who submitted a bill, right? Sort of any willing provider. None of us in our own personal lives would ever engage in shopping activities where we didn’t actually shop; where we just bought, you know, whatever the price was without even looking. Right? That’s what CMS was doing, and on a vast scale with tax dollars. So quality measures were a sense of, “Let’s try to be a smarter shopper,” really, and for what was the state of the art in 2000? So, you know, 2000: Limited internet bandwidth, limited really internet anything when you get right down to it, limited electronic medical records, um, not something that you could rely on, and therefore very limited available electronic data. So the quality measures basically had to be things that could be sort of, essentially, manually, on some level depending, collated and reported. And of course because of the money involved, they also became heavily politicized. Those data measures have turned out to be very narrow. I don’t think they represent a broad notion of value. It’s like you’re buying a car based on the left hubcap or something. I don’t know if cars still have hubcaps, but you know, you’re basing it on a very, very narrow set of value. With health information exchanges collecting very rich data sets on entire populations of patients and, you know, roughly two thirds or more of the country now, in all those places, I think instead of going through the burden of the quality reporting system and the gaming of the quality reporting system, you could have the payers who have legal access to that data under HIPAA anyway, right? So under the treatment payment operations provisions of HIPAA, they have access to that data. So using that data, either through the HIE or even through the new ONC interoperability rule, the HL7 Version Four Bulk FHIR API that we instituted over the last couple of years, and that’s going to be required of electronic records end of 2022, those types of approach give us the chance to use pretty much any modern big data tools to measure quality. And, you know, that’s, you know, it’s sort of modern. You know, what is big data? But if you look at what the goal of the quality programs is, which brutally put is to pay better providers more and not so good providers a bit less, we’re going to be able to make those distinctions far more reliably, far more accurately with much richer data sets than we have now. So that really is the opportunity, which is for the American public, to get a much better deal on their healthcare, (and that clearly is needed, it goes without saying,) and use that and get that deal in sort of a modern kind of way, you know, but somewhat analogous to how we do shopping for any number of other things, using modern tools.

Now, one of the things I would imagine, especially when you consider the financial impact on providers, and plans, and everyone else, when it comes to quality measures, there might be some resistance to losing control of that. So if I’m reporting my quality measurement to you, as opposed to what could be considered kind of an objective third party, being the HIE, which is responsible for providing that data, you know, do you suspect there would be some resistance to people letting that go for that reason? I think there are other reasons that I’m going to throw out a minute, but just for the losing control, the fact that I’m not really reporting the data, somebody else’s reporting on my data, my patient population. Do you think there’ll be resistance to that?

Look, people have optimized their current business models. No doubt about that, right? And so, you know, it’s a little bit of a “who moved my cheese” type of thing. You know, I think there’s obviously going to be potentially resistance based on, you know, “It’s a new business model. I don’t feel like, you know, changing what I’m doing because this is what I always do.” However, couple of points I think make this sort of a different set of incentives. So first of all, these reporting things are relatively transparent. It’s not like an HIE is morphing the data. Now some of them sell quality measurement services, but let’s say to a payer, the data would go unvarnished. And there’s absolutely no reason in the world that providers couldn’t get the exact data stream that the payers have received, for example. So we’re not talking about the type of elaborate analytic framework that needs to be built piece by piece for every single quality measure today, right? That’s the whole point of big data. (It) is to incorporate all of it. You know, so it’s the difference almost between, you know, very classic small scale statistics and all the modern computing techniques. You know, you don’t need those bespoke calculations, you know, that in fact are inherently problematic and suspect. That’s one big consideration. The other big consideration is providing all of this data is a very expensive activity. This is something that erodes value, and in a world where healthcare will, one way or the other, become accountable. And in that world, you want to be providing as much value for the dollar. That is what ultimately gives you protection. It’s what gives you protection in any commercial enterprise is that you’re providing good value for the dollar. Patients aren’t particularly served, certainly not directly, if you spent hundreds of thousands of dollars on, you know, quality of measure collection. And on the public side, vast sums of money are spent on these things. You know, it’s hard to show outcomes that are proportionate to the expenditure. So I think just from a efficiency point of view, I think there’s a lot to potentially say here, right? Because again, you’re using… You’re reusing infrastructure rather than building new or maintaining a totally separate out-of-process infrastructure.

I think you’re right. I think also that, you know, I’m a big proponent of not everybody toiling away in their silo to solve the exact same problem. So theoretically, if you have the HIEs focusing on the challenges of doing, you know, measurement and analytics against clinical data coming from all these places, they could get really good at it. And that means that, you know, you generally raise the bar of quality, and your ability to do things with that data by having these, let’s call them, “Centers of Excellence,” when it comes to doing that kind of thing, as opposed to everybody interpreting things their own way, everybody building things in their own silo. You know, you have this mechanism that you’ve constructed a thousand different ways. And when you go to make use of that, you have the same problems you have when you’re trying to interoperate with the data in the first place, right?

嗯,我看有点不同,Charlie. I would say that, you know, for example, the HIEs are really sort of, you know, the last mile of the public utility, you know? The wire, or cable, or fiber to connect to the providers and, you know, get the data, and they’re not doing the transformation or the analysis per se. I mean, they may, in some circumstances, but it’s really the payers, right? That’s part of the payer value proposition is to figure out what to pay for. So I see the HIEs in this in just making the process potentially much more efficient, much more real time, much more interactive, but ultimately it’s the payers adding the value. The ability of the payers to then, you know, figure out how to change healthcare, where to invest resources, what thing, you know, what clinical activities to support, that’s all in their ballpark. I look at the HIEs really as a utility model. So we’re not, to your point, not rebuilding all the APIs, recollecting all the data, and doing it in, you know, little bits here, right? So there’s sort of quality measure reporting, which has multiple different ways of doing it. There are, you know, probably thousands and thousands of public health reports. Now they’re not all going to go away. Some of them have very specific needs that HIEs wouldn’t get, but you know, it’s moving the whole thing to reuse the vast amount of data that is already out there, rather than starting over with much smaller bits of data for specific purposes. That’s, I think, the heart of the modern opportunity.

Okay. So the other challenge that I think we might have is just the monetization of patient data that we’re seeing in healthcare today. Obviously, if we’re moving all that aggregated data into the HIEs, I would imagine that we’d have to have some kind of protections in place to make sure that somebody can’t take that data and sell it.

So obviously you’ve hit on a huge topic, um, no doubt by intent. Um, that would be, that’d be 10, five minutes. We could do one, or different aspect of that. But, sort of a couple of high-level points: So first of all, the state local HIEs that I’m really talking about here are non-profits with public governance, right? So, um, this is not the, “We’re going to tell you one thing and then sort of, you know, sneakily do something else,” kind of model of things. These are public entities, so that, you know, they’re just not in the business of selling certainly identified data. Now, the public health agencies and the quality measure activities we’ve talked about, all those people already either get, or have a right to, pieces of identified data by law, right?

Yeah.

So if you have a sexually transmitted disease, the public health has a right to know that information. If you’re a Medicare beneficiary, they have a right to assess the quality of your care, right? So all of these things we’re talking about are already legally established. An interesting thing is with modern machine learning privacy protected transformations, how, you know, there may be all kinds of – there will in fact be all kinds of discovery things on data that may come from this. But again, as public entities, those types of things would then lead to lower transaction costs and, you know, public goods, I believe because they are, you know, non-profits and accountable and aren’t, don’t have, you know, they’re not trying to sell an EHR or, you know, lock in a business model. The HIEs also potentially touch everybody who is providing care. So not just doctors and hospitals have an electronic health record product that supports HIV today, but community clinics, jails, shelters, schools. As an ER doc for 30 years, I assure you I’ve taking care of thousands of patients who’ve been sent to the ER. We typically know who sent them, but we know nothing about their prior trajectory, right? So There are many opportunities here in medical care at large to be smarter about the data. So all of that data, I think, will have very strong protections and public uses, far more than is happening currently today, in data selling deals, which are for the most part, very non-transparent. The other thing to point out for the audience is almost none of the inference about your health that is available in the modern internet actually requires HIPAA covered data generated by clinicians, right? There’s far more inference available about your health that can be economically acted on by things like the geolocation on your phone. Are you at the liquor store, the McDonald’s, or, you know, running a four minute mile? That accelerometer: Are you moving? Are you just sitting in your chair? Your search data, you know, as we well know, all of those things have vastly more implication for things that economically matter to us as individuals. So that just gives a little hint of the data privacy issues and data transparency issues, but hopefully we’ll get folks thinking about it richly.

你知道的,通常我同意uniq麻疹uely positioned to do some really cool things. And I think that some of these cool things, even though they’re nonprofit public entities, I know for, based upon my interactions with his over the last decade and a half, that they also, they struggle sometimes having a model that allows them to provide a lot of these services from an economic perspective. So, you know, it also opens the door for them to have some additional value add that can contribute to their longevity and their viability. But when you think about the data moving through the HIEs, if we can solve some of the other issues that I’ll throw out in a minute, you know, there are things like, for example, my father a few years ago was diagnosed with a glioblastoma and when he was going through that process, um, and he ultimately succumb to it, I was looking all over the place trying to find any kind of clinical trial or any kind of thing where he was a candidate and maybe I could look for something that could help him. And it was a huge challenge for me to kind of navigate all that. I think things like, you know, clinical trial recruitment, identifying people that are seeking opioids, you know, which we already kind of do in some of these environments, you know, if we had access to high quality interoperable data in an environment like an HIE, I think there’s a lot of really good purposes we could put it to. So I, I agree with you.

Yeah. Yeah. I mean, those are all points. Spot on.

So, that brings me to the other challenge. I live in the interoperability space. That’s one of the things that we do at Clinical Architecture. So one of the things that I think is also a struggle is, you know, we can provide, you know, FHIR to me was a great innovation, and it’s a great level-setting technology when it comes to the canonical models we use in healthcare. And in some cases it helps with the semantics of healthcare as well because of the value sets. It doesn’t completely solve the problem because there’s still a certain amount of uncalibrated uncertainty between the terminologies that we’re using in our EHR silos and our different data platforms and how we make that available through FHIR. And I think there’s a lot more uncertainty and transformation of meaning in that process than a lot of people fully appreciate. So I think that that’s a challenge. And I think we see that, and I think the other big challenge about moving data and being able to act upon data is the fact that we still have so much data that is kind of bound up in unstructured text, that it just doesn’t make it to a place where we can apply a lot of the type of analytical technologies we have to do what want them to do. Do you have any thoughts on that?

你知道,在医学的意义的问题data is… Has been there forever. And, um, obviously there is, I think in our broader lives, right, in our broader technology lives, there is a, um, has been a rich tension increasingly as we get more data between which of that data is quote unquote structured, and which of it is unstructured. I think that’s just a broad, broad thing. And you could even see this economically, right? I mean, ultimately the battle between Yahoo, for folks old enough to remember Yahoo, and Google was the battle between structured and unstructured data. I mean, it was, and they have sort of different uses. The first thing I like to remind people of, you know, when I was at ONC, obviously we would get a whole class of requests that were of the order, um, “Make somebody give me structured data,” as a request class in policy. We met with, uh, over 200 stakeholders in doing the interoperability world. The thing you have to remember is, uh, the laws of information physics definitely apply to data. If you want structure, right, If you want to resolve entropy, you have to put energy into the system, right? That was true if you were, you know, that was true when they built the pyramids, and it is true with structured data today. And of course the people we typically forced to do this, most notoriously, are the doctors, right, who are spending three, four hours a day in front of their computer screen. And, you know, the case of primary care docs, you know, really is that, I mean, what other industry uses computers to make more work? So I think when you look at structured data, you know, it’s a very, very, you know, you have to have, you have to have a very purpose-built approach. And obviously you’ve been, you know, an expert in doing that now for the decade, or for decades (certainly that I’ve known you). Because it requires energy, AKA investment, to do that, and tooling to do that, and process to do that, it’s very expensive. And we just have to keep that in mind, as we decide what to buy and structure. The unstructured data, which, you know, so that would be, for example, you know, clinical notes would be maybe the classic clinical unstructured data. Um, that data is increasingly amenable to, you know, modern, natural language processing and machine learning tools. So I think if you look at the world, there has been a gradual shift from, you know, structured to unstructured data, in terms of generating economic value from data. And you’re seeing that with all of the Silicon Valley and, you know, national international approaches to big data, right? They all essentially come at it with the assumption that much of this is going to be unstructured. If you look at structured versus unstructured data as a horse race, you got to believe the unstructured horse is winning. Now from a policy point of view, the Cures Act, which was the law that required the interoperability rules that we did at ONC, actually required all data be made available for application programming interfaces. As this audience knows, all data is not structured and even vaguely structured. So we, um, at ONC continued, you know, with sort of the US core data for interoperability instead of data that has varying degrees of reliable structure, and then for the rest of the data, which is, uh, those APIs are required in December, 2023, all we can do there is ask that those things have a vocabulary tag, you know, a little bit of a very loose, I don’t even want to use the word terminology in front of you, Charlie because you’d beat me over the head with a stick. “Don, that’s not a terminology. That’s absolutely counter of a terminology it’s anti-terminology.” (Charlie laughs)Um, so acknowledging that, Charlie, acknowledging that fact, it’s, you know, that data is really going to be available for NLP approaches. But there will be NLP approaches and they’re getting better by, um, you know, rapidly.

I mean, one of the things that, that we’ve been playing around with here, because I think that, I think you’re right. I think that structured data, you are, you are fighting information entropy and you’re forcing people to do something that is kind of awkward. And you know, when you’re dealing with structured data, in my opinion, the biggest limitation you have is you’re dealing with pre coordinated terminology. So you’re taking somebody who’s a free-thinking human being and you’re saying, “Hey, I need you to tell me something, but you can’t say it in your words, you have to go look it up and you have to count on the fact that some other human at some point in time thought about what you’re thinking about and put a code to it.” And so you immediately introduce this notion of uncertainty because the person who’s thinking something has to find a pre coordinated thing to be able to share that in a structured way. And I think that on the flip side, when you deal with unstructured data, you know, I always look at unstructured data and NLP as us creating a puzzle that we have to solve to get at the valuable thing that we’ve locked inside this puzzle. And so one of the things we’ve been experimenting with is this notion of, you know, something in between this pre coordinated terminology and canonical structure and this totally unstructured stream of consciousness, which is really a way to represent information that is designed to self-assemble structure based upon the natural expression of information. That doesn’t… It’s kind of a post coordinated expression type reality, where you… In healthcare, we people talk about things using patterns and their expected patterns. And that’s what we use in NLP, or, you know, unnatural language processing in terms of healthcare, to try to unravel this, this note that somebody dictated or somebody keyed in. So I know it’s something that’s… It’s kind of this futuristic silver clothing flying car idea, but having, having an information system in healthcare that isn’t just totally unstructured, but it’s aware enough of the way we talk about things and the nomenclature we use in healthcare, to when we’re naturally interacting with a system to describe what’s happening with a patient, it is assembling something that is structured, even though it doesn’t feel structured, so that we go to do analytics against it, we’re making it a lot easier, so we don’t have to solve that puzzle. So that’s one of the things that we’ve been focusing a lot of attention on. And I kind of think of it as high resolution healthcare because I think of structured content in healthcare as low resolution. And I think of unstructured notes as almost analog. And we’ve got to find something I think, in the middle, so we inject less work generally into what we’re doing in healthcare. That’s my soapbox moment. Thank you for enduring it.

I mean, I, I think that’s the kind of innovation folks are looking for. It’s worth understanding also with structured data that when you force structure, there’s sort of two fundamental issues that you have to deal with clinically. One is folks may not actually have the information available. I’m an ER doc, and I may not know what your chest pain is from, right? Or your whatever. Um, I may have a guess. But when you say, “Well, okay, give me the code,” you’re, you know, you’re making assumptions about what’s known that are likely to be highly inprecise and maybe just plain inaccurate. The other thing, which is very interesting with structured data, is what’s the granularity of this, right? So if you say, I don’t know, pick a disease, well, if I’m a generalist, my level of a disease might be very different than, let’s say I’m an orthopedist. Um, you know, my description of a broken bone would be, you know, very different than an orthopod’s description of it. And so, you know, that granularity, and of course, neither I, nor the orthopod is going to describe it at the molecular level, which really determines maybe its course over time. Yeah. There’s, there’s plenty of work left to do here.

No, I agree. And I think that when you think about the information and data quality in general, one of the challenges we have with data quality is the energy you have to put in in the beginning of it. So at the tail end, when we’re trying to leverage all this information to make important decisions, whether it’s a quality measure, clinical decision support, you know, public health reporting, we’re totally reliant on everybody upstream from that, from the person that, you know, keyed in a lab result, from the person that picked a unit of measure, the person that created the terminology that I’m using, all those things factor into the quality of what I’m ultimately making these important decisions on. So, you know, part of the way to improve the quality of something is to make the process of creating quality data less onerous. If I have to, you know, be away from my family and be up until 9:00 PM at night putting in documentation in a structured way, you know, I’m not gonna pay… Well, I’ll give people the benefit of the doubt that they’re trying to put in meticulous quality, but at the same time, if we could come up with a way that the quality is easier, because I’m not making you jump through a bunch of hoops and figure out the way that somebody else thought about it to be able to document something, it certainly would increase the likelihood that the quality of the information you’re putting in is going to be better.

Yeah, absolutely. For sure. Reusing something that has many eyeballs on it is totally different than reworking it, you know, with just a handful of eyeballs on it at best.

So what do you think we could do to kind of move forward this idea? What do you think has to happen for… Because I went back and I did a little research on using HIEs for quality measurement and things like that, and I found references back in 2010, where people were talking about doing this, what do you think has to happen for this concept to kind of move forward realistically?

是的,我认为现实是,在某种程度上它’s simple and of course it’s politics too and it’s not simple, but basically the CMS ought to start thinking about requiring, you know, the reporting via the HIEs. You know, they did that a little bit for some of the COVID stuff, but just require that as part of what is quality. Is, you know, hooking up to an HIE both for better patient care and for their purposes? You know, should be an interoperability measure. You’re paid more when you get that data. Clem McDonald years ago, one of the fathers of informatics from your neck of the woods, said that, you know, this was, I remember talking with Clem this, Oh, I don’t know, 15, 20 years ago. He said that if CMS just paid $1 more for a lab test in, you know, coded and LOINC, it would happen pretty much overnight. Right? So if they just said, “We’re going to pay $2 more if the visit and the data is sent to the HIE, all of a sudden, and we’re not going to pay for duplicates if it’s available in the HIE,” you’re going to find that the places that don’t have HIEs will pretty much instantaneously get HIEs and you will have this; ditto public health reporting. We, you know, now have spent untold trillions of dollars on COVID. You could fund the operating expenditures of probably all the HIEs in the United States, or maybe on the order of $500 billion a year, maybe. Right? That is 500 million a year, just for the audience, is what Medic CMS pays every hour of every business day in American healthcare for one hour per year. I believe maybe they paid far more for ineffective care.

It puts into perspective.

And obviously we’ve incented a totally different system. We have incented big, um, you know, we’ve incented delivery systems to become very large and non-competitive and become so large that they can set prices to payers. That’s sort of what we’ve incented; that’s all federal policy. That’s not what a market would end up doing. Markets would have new entrants when those things happen. You know, the policies I think are very much potentially readily at hand to do that.

No, I think that makes sense. I think that, you know, you said it a second ago, this last year, COVID is terrible as it was, it’s also kind of opened a lot of people’s eyes to some of the inefficiencies we have in healthcare when it comes to understanding what’s happening. And I would think that would motivate people to look for ways to improve that. A lot of people are worried about is, you know, the next time something like this happens, God forbid, how are we going to be able to stay in front of it, stay ahead of it? And a lot of that is about having transparency into what’s moving through the healthcare system so that we can look for signs of things and then appropriately direct resources when we do see things happening in a particular location or, or region.

Yeah. Real time, real time data.

绝对的。遗憾的是,我们可以告诉铁道部e about our Twitter by watching Twitter feeds than by looking at the information we’re pushing around.

Yeah, well, that’s a whole other issue.

Well, Hey, I, uh, I really appreciate your time today, Don. And I’d love to have you on again because I think there’s like a thousand topics we could talk about at some point in the future. Hopefully this has been productive for you. Any last thoughts or questions you want to share before we wrap up?

Yeah, no, we’ve invested a lot nationally in capturing electronic medical data, and now it’s incumbent on us to be smart on how we use that no matter where we are as patients, and use that in a way that is pro competitive. There are great opportunities here with a health information exchange on all of the social equity issues as well. We didn’t get into that here, but those opportunities are massive. And I think we just have to be smart, gracious, and pro-competitive, and charitable about how we use this data, and, you know, that’s the opportunity ahead of us. So yeah. Thanks Charlie.

Thank you, Don. Well said, and thanks to everybody who’s been listening. And thank you, Dr. Rucker for joining me today for the Informonster Podcast. This is Charlie Harp, saying have a great day.