Using Data to Optimize Costs in Healthcare with Travis Dalton and Jocelyn Jiang President/CEO & VP of Data & Decision Science at MultiPlan

sn't inside of the network or out of the network where care is sought. And that's a very different path with a very different cost structure around it. So I can see where it's confusing. No doubt about it. But as I said, you know, what we're trying to do is know, demystify the extent we can and also control and contain costs to the extent that we can as well.

Richie Cotton: Okay, so, primarily insurance based and then you've got this different string in network where it's like specific providers and then out of network where it's like, oh, you want treatment from someone else and maybe you have to pay a bit more. All right, so it sounds like a fairly elaborate system.

What are the problems with this? Jocelyn, do you want to explain the, the downsides of the system?

Travis Dalton: sure. Like 

Jocelyn Jiang: Travis mentioned it's fairly complicated. A few, I would say main problems with the U. S. system for the average consumers. Number one, I would say the price is not transparent at the current level. it's not very easy to know how much you're facing out of your pocket. prior to your getting the care in a lot of the situations.

And also, secondly, the price can be drastically different from provider to provider, even for the same exact procedure that you're seeking. Just simple example, like getting an x ray, getting an x ray in the hospital, comparing to getting x ray in the imaging center, that price can be different by 10 times or even more and then third patient with the insurance, they typically only care about their portion of the out of pocket amount and have no idea of that main risk barrier, whether it's the payer, which is the insurance company, or their employers, who is actually the risk barrier, are actually paying for the service.

So, a lot of the times, it's hard to educate , the consumers or the patients to shop for the service that are shoppable. And then lastly, the patient decision making on the provider selections oftentimes is also less about quality or cost and more about How soon I could get that next appointment and how easy for me to get to that doctor.

So that I think that's the area with the recent government requirement on price transparency. We are trying to help filling that gap is to help bring that transparency, helping to bring that cost information and quality information to the consumers to the payers and providers. So they can provide that information to the people for them to make a wiser decision prior or before 

Travis Dalton: consuming the care.

Richie Cotton: Okay, so yeah with that lack of transparency, it's very difficult to know how much you've been charged and where you're going to get the best price. I guess there's no like price comparison service like you get for shopping sites no equivalent for healthcare in general. Okay, alright Travis, did you have anything to add to that?

Are there any other problems you see with the system?

Travis Dalton: Yeah, I mean, it's interesting. I mean, there's, you know, the price for the same service can be different. Yeah. So if you're going through a Medicare channel, it's a price. If it's in network negotiated, it's a price. If it's out of network, it can be a price. So part of what we're trying to do collectively with all the transparency data and with machine learning and other capabilities is to essentially, bring transparency to the table, but also evaluate those prices.

Against some fair and reasonable basis and determine what an appropriate cost for something might be and bring that to the industry in a way that helps contain costs over time. And so, there's a lot of challenges with that. I also served the provider and patient community for 22 years before I joined the CEO of Multiplan.

And, you know, there's a lot of disparate data out there. taking that data and normalizing it, making it useful is something that's really important as it relates to health care, and particularly in terms of bringing value, but also on the, patient provider side as well.

We could certainly have a little discussion about that for my past life, too, if it's useful to your listeners.

Richie Cotton: Okay I love that you mentioned data and I really want to get into the details of what the data is and how it's used. Maybe just to begin with at a high level how can data be used to help solve this problem with transparency around costs?

Travis Dalton: there's several ways. So we offer a number of services. So one of the things that we do is we do offer a network Discounts. We have 1. 4 million providers that have a discount with us, and we offer that network as a primary error app. Beyond that, we also offer services where we look at claims, and we evaluate those claims against some rational basis, like a Medicare as a basis.

And then we essentially reprice those claims. So when you look at an out of network claim, sometimes it can be drastically higher Then you might see the same service in network. So we use data, we use information, we use machine learning, we evaluate those claims, we look at them, we peg them to some basis, and then we have a market clearing or what we think would be a fair process by which we work between payer and provider to essentially resolve that claim and keep costs down for employers, employees, and patients.

Furthermore the team is working on transparency products. that take a massive amount of MRF files and data that has been essentially, there's been some policy around that, making that mandatory that, that cost data be provided. Ingest that, normalize it, use it in a way that you can bring that forward so you're now getting hopefully cost and quality to the table in a, reasonable, meaningful way.

Richie Cotton: In that case Justin, do you want to go into a bit more detail about like, what are all these different data sets that you have and where do they come from?

Travis Dalton: Typically, 

Jocelyn Jiang: those claims data come directly from the insurance company. And then we have a series of, you know, ingestion data processing steps to normalize and standardize those data that coming from the insurance company. Oftentimes, you know, the way the data is being laid out or sent are different companies.

and then after we normalize them, we will go ahead and enrich those data further. So, for example, the data will come in as procedure code or diagnosis code, and we enrich them with the description, give them the categorization to help them further analyze their data better. And then, furthermore like Travis mentioned with the price transparency data that's being public available, although it's very messy.

And then it's massive that we use a lot of machine learning techniques needs to sort those out and then clean those data out so that we can. Leverage the actual utilization data off the clients coming from the insurance company and then reference that with the public available data from the transparency files to find that balancing point and to compare, you know, like whether services are being paid at a reasonable.

Price point or if there is a better network, the client should be picking based on how their employees are located or how they are consumed, 

Travis Dalton: their, the cares.

Richie Cotton: Okay, it sounds like you've got an awful lot of data cleaning to do then, that might be kind of the crux of this. So, can you just tell me for like what types of data is it? Is this like a lot of just sort of text data that where some doctors scroll some notes somewhere and you're trying to work out what that actually is or what sort of data cleaning do you do?

Jocelyn Jiang: Mostly the data when it comes from the insurance company, those are post adjudicated claims data. So they are standardized and mostly like, they have been processed by the by the insurance company. So they have the diagnosis information, the procedure information, so you know what has been done to that patient and which provider.

And then what is, cost, what is the billing amount? What is the discount rates? And then you know, what is the out of pocket amount for the patients? So those are typically the information we're getting from the insurance company. and the one thing that I didn't mention earlier was after we ingest those data, we also apply prediction to it.

 want to very With the machine learning techniques, we can predict someone based on their past 12 month claim experience. What is the likelihood of this person of getting a certain disease onset probability will be like, or what is this person's future 12 month spend is likely going to be in a certain range.

So with those type of information, we can. better pinpoint the type of patient that we should focus on or if they have a point solution vendors and oftentimes we can direct them to the right targeted population for them to 

Travis Dalton: do early intervention.

Richie Cotton: That sounds pretty cool. The fact that you can make predictions about like how much people are going to have to spend over the next year, things like that to help people budget. Are you able to share any details on what those machine learning techniques are that you use?

Travis Dalton: Yeah, so, 

Jocelyn Jiang: before we were purchased by Multiplan the company was founded out of MIT spin out project led by Dr. Bert Smith. That's a pronounced professor in the optimization techniques. So we mainly uses Okay. the decision tree methodologies to finding that optimized decision for the individual based on their past care pathways that we could see in a claims and then finding those detectable signals in their claims and then determine what's Okay, based on all these signals, combination of the signals, how much spend this person is likely going to spend or what type of disease this person's likely to have and then it's not widely any disease we can predict and oftentimes is those ones that I have a certain patterns that can be traceable or trackable.

Those are the ones that 

Travis Dalton: can be detected better.

Richie Cotton: That's fascinating. And I suppose The use of decision trees means that it's quite easy to explain what's going on, which goes back to your idea of having transparency around, like, how everything works.

Travis Dalton: Correct. And I'm glad 

Jocelyn Jiang: you mentioned that interpretable because I think that's one main differentiator in our methodologies comparing to other companies that are applying AI or machine methodology to their data. Oftentimes it's a black box, like you can't understand what's going on before the results being spin out.

But with a decision tree, Like you can actually interpret results or prediction and have a better understanding and then like guide the model if needed to better fit prediction or results that you 

Travis Dalton: you're seeking for.

Richie Cotton: Just going back to, like, the main use case of this, which seems like it's trying to save money for people so they're not spending so much on healthcare. So, I guess, if you are paying for insurance, like, what's the sort of step one into, like, making use of all this technology and data analysis in order to save money on on your healthcare?

Travis, do you want to go with this?

Travis Dalton: yeah, happy to. I actually want to make a little comment on what Jocelyn said too, if that's okay. as an industry, you know, my view is that the BST acquisition was made because we actually believe that's the future of healthcare. Is data. You know, I take data is like the new oil.

you got to find it, refine it and distribute it. And so being able to do that in a reasonable way is gonna be really important. And I think platform products that have scale. And that offer scale are going to be one of the biggest ways that potentially to control costs and health care over time.

And so, quality and cost are two of the biggest issues that are faced in health care. And I think the use of technology is going to be the thing that ultimately helps quality go up, access go up and cost go down. And I think that will be scaled technologies. And so you'll see that from major, major technology players that are interested in the space.

Thank you very much. Because of that, and also, you know, it's moved to the cloud and other capabilities. I think it's really important for care. you know, there's lots and lots of point solutions out there. There's lots of things to do. I mean, as an individual, you have to just be proactive in your own search and your own care.

If you are part of an employer plan it's likely that someone like us or another technology provider provides capabilities where you can actually search. You can do some search where you can look at some price, quality and availability data, or it can serve up someone that's in your network that has a particular price and quality that's emerging and getting better and better over time.

As the data and the data science improves and as the transparency data, which we wildly support as a policy initiative becomes available, we can use that and we could start to map as I mentioned, cost quality to availability, and that makes the process more simple. So you can start to have choices.

But it still feels a lot like hunting and pecking. or luck or referral network or I just know somebody. I think you have to advocate on behalf don't underestimate the tools that you may have through your employer or through government websites that are actually starting to develop and do better here.

Of which, you know, we offer, some of them, but others do as well. I think that's some of the state we're in.

Richie Cotton: I suppose the other part of this is things like the provider side of things. So, like, the, the doctors and the hospitals, does having better data affect their costs and, Yeah, is it going to affect the monetary situation for these providers as well?

Travis Dalton: Yeah, I mean, I put on my, my health care hat and, you know, in total yeah, I think the thing that's going to be really fascinating, I think, with the provider and the patient side, not to take us too far off track here, but there's a couple of really interesting developments. One is the ability to use ambient technologies and listening and other techniques to where you're now collecting data in a way that's very different than the past, which is essentially.

the keyboard, so always used to say, you know, in prior life that the most dangerous thing in health care is the pen because you're writing now. It's the most dangerous thing in health care is, the keyboard. Now it's a voice to voice conversation where you can actually collect information and data in an ambient way.

I don't know if people like that word or not, but I heard An actionable way. So I would prefer to call it. That is done real time. And also you can run rules and you can use other technologies behind that to where you're taking a lot of what would be administrative work off and burden off of providers.

And caregivers and now they're able to focus on the patient and the actual conversation that's happening and then you can run prediction behind that. So there's fascinating things going on in health care. I encourage, you know, your listeners to really look at it as a pool space to go work. I mean, prediction models using data around.

I've seen it. Really interesting models around suicide prevention. Looking at social determinants combined with medical data to get early detection or early prevention technologies to people, or capabilities to people. Opiate and other abuse situations where you can look at state data, and you can look at other data, and you can determine a full view of someone's health care and what's happening, so you can intervene in a way.

Versus wait for someone to present. There's some really fascinating things that are happening with that and also around precision medicine where you're looking at the genotype and you're figuring out the best way to actually prescribe in a precise way certain therapies or procedures or protocols that yield the best result.

the next 20 years is going to be incredible. I wish I had 30 years left in my career. I probably don't. Jocelyn probably does. But I mean it's going to be like the best 30 years, because it's going to be the most exciting time because this is all available now because a lot of it's been digitized in the U.

S. and across the globe, actually. Sorry, I got a little excited there, Richie.

Richie Cotton: No, no, I love the excitement. And that's kind of interesting that a lot of the stuff you mentioned about, well, okay, if you want to improve costs in healthcare, you've just got to intervene early. detect things before they become a big problem. I can certainly see how that's the case with opiates. You want to start, you want to identify the problem before people have messed up their bodies.

So, are there any other areas where you think there is room for like some, big improvements in, healthcare there, so, Like, are there any particular disease types or any particular conditions where you think there's room for a lot of use of data to improve the situation?

Travis Dalton: Yeah, I think that there 

Jocelyn Jiang: are quite some areas where especially I think the shoppable services where people can have the time to evaluate multiple options. That's where we have the better chance to provide using information to Provide more actionable insights before someone making that decision.

So I would say things like Orthopedic surgeries or trying to find the best provider to treat kind of a long term disease conditions That's when think using data and having that well rounded information both cost and quality to help make that optimal decision for the patients.

I don't know, Travis, if you have. Some more to add. I'll answer the question a little, differently than a specific condition or area. What I would say is that for us, a really important thing is figuring out what to work on. the world of things to work on is enormous. But the resources to do it are finite. And so, having product life cycle capabilities that allow us to understand the market, to understand the needs of clients and then ultimately to make things that matter.

Travis Dalton: Is really important. Or you end up doing quote unquote science projects that don't have scale benefits. And so companies, I think have to think deeply about how they actually create product life cycle so they can make organic capabilities that matter most of the markets they serve. and then you start to surface the things that can have the biggest impact, some of which have been mentioned here today.

And so there's just many, many, many things you could look at from a morbidity and co morbidities perspective and insights. But picking them and, and having impact and getting the market is also important as you run the business.

Richie Cotton: Since, Justin, you mentioned idea of, like, long term diseases and Travis, you talk about which areas have the biggest impact. So, it feels like with healthcare there are some things which are kind of cheap but happen a lot.

So something like flu happens to millions and millions of people, but also it doesn't have a big impact. And then you have some things which are rare and kind of devastating. So, cancer is, you know, Less common, but has a tremendous impact. So, is there like one area or the other where you think data is most important?

Travis Dalton: Data as a usable commodity where I said earlier is, you know, the new oil. Is going to be interesting in the sense that it's not all in healthcare usable because of lots of rules, lots of regulations, lots of PHI lots of other things like that. So, if you could truly figure out the best way to look at social determinants to bring in search information, to combine that with the medical record you really could start to predict with much greater accuracy certain things, like obesity in certain areas.

Looking at diabetes, instances of diabetes. Certain things like that could have predictive capabilities or qualities, and that way you could start to target programs around wellness and other things in targeted geographies and areas or you could spend social dollars in a way that's meaningful for a community to help prevent the spread of some of these things over time through education and otherwise.

And so I think that most things are predictable in some way if you can get the right amount of data. But there's a large debate in healthcare around what's acceptable, what's usable is it PHI, meaning health information that should be protected? Should it be usable? Should it be ingested? Is it cyber secured?

All of those things are just a massive part of the data infrastructure that have to continue to be answered so that we're able to use predictive models in a way that makes sense and most meaningful. Probably didn't answer your question, but I thought I said something

Richie Cotton: No that was really interesting. Um, Yeah. So, the idea that there are some sort of limits on what you can do ethically is very interesting, because I suppose you can sort of try and ingest everything about, everyone's lives sort of, Facebook style, and then get more information for better predictions.

So, can you talk me through, like, what are the limits on what data you can get in order to make predictions?

Travis Dalton: think when 

Jocelyn Jiang: we analyze those insurance claims data some other data that we can combine to help making some more accurate predictions are things like social vulnerability index, Just as an example so you can use a public available data in combination with the client or the patient's specific data together to bridge the gaps.

So, you are not exactly using some of the specific PHI information, but using some general information, knowing this person lives in this zip code, and then by knowing, by public information, what is the social index look like in that area, you can make certain prediction around, okay, for people live in a certain geographic area, what are they like, like what their you average I give educations are what they're dominant ethics in that geographic regions are so, things like that.

When you combine the specific data with more general data together, you can still make some pretty useful predictions around 

Travis Dalton: healthcare.

Richie Cotton: And I suppose related to this there's an awful lot of data privacy regulations around healthcare data. So you've got like in the US, there's the HIPAA regulations. So how do you go about ensuring compliance with all these regulations?

Jocelyn Jiang: we have very high standard on securing the data that we collect from the various sources. So, not only where HIPAA compliant, we're also, you know, high trust certified Just as an example, like certain techniques we use to protect those information is even when the data comes in to hit our server, the very first step we do is to separate the PHI information and the claim information of that individual into two separate repositories and using a common person ID to link the two information together.

So, The PHI is stored in a completely separate storage place compared to the, the claims information. That way, as another layer, we're trying to, further protect the information become identifiable in the events of things like 

Travis Dalton: certain data being breached.

Richie Cotton: One thing that you've both mentioned a couple of times is the idea of quality and cost being separate things. So, I think a lot of the focus on this has been, let's try and reduce the healthcare costs for people. But can you also use data in order to improve the quality of outcomes as well? absolutely. I think that's one of the major, themes of the industry as a whole right now is figuring out how to best use data in a way that's meaningful to improve outcomes. our focus as a company really is, has been, I would say, primarily around providing cost containment waste and abuse solutions network capabilities, and now we've added the decision science piece.

Travis Dalton: But we're also, you know, very much want to facilitate anything that we can do to help the providers and clinicians and otherwise figure out ways to use data in a meaningful way. I mean, I came from the M. R. Background, so I don't, I don't want to talk too much about where I was.

But you know, there's an incredible amount of data that's generated, and there's an insights and technologies and rules and capabilities that are there that ultimately allow for much better care and capability and early, detection of things that never existed in the past. And the thing that's most interesting is it brings protocols forward in a way that in the past, you know, someone would have to cognitively process all this, but now they actually can think about things in a very different way because it's being brought forward than having to go find it.

So it's, kind of changing the way work is done over time. And I'll just say this, Richie, we, We want to serve the whole ecosystem of health care, so we actually we want to work with rural America to look at the idea of maybe helping them figure out ways to create better access to care in rural parts of the country to help keep costs down and quality up.

We want to bring those transparency products to, the full continuum of care actually as we go forward to kind of help with some of these problems we're discussing.

Richie Cotton: I like the idea of just helping out rural Americans. Have you got any other success stories where some projects have worked and it's improved people's healthcare?

Jocelyn Jiang: As a data platform where we have all these prediction powers. One example is using the claims data and then we predicted those high likelihood of people of getting uh, MSK surgery in the next whole month. That way, when, um. the client have a MSK point solution vendor that specifically support the members and help them guide and navigate them through the whole journey of their surgery events By knowing who those you know, like, high, high probability members are getting those surgeries are extremely helpful and they improve that outcome tremendously because rather than trying to, you know, like, wait for people to contact them, they actually know who those people are so they can proactively, as a point solution vendor, contact those members.

That are having the conditions and then understand which stage of the disease they're in and then help them find the optimal care pathway for their MSK conditions. Some may not even need their surgery, some may need a surgery so you need help finding the right provider combining cost and the quality information.

So, yeah. Having the data and the predict and then trying to early intervent and using the data further to find the right combination and the cost and then the quality providers. I think that's the type of problems using data. 

Travis Dalton: We're trying to solve together.

I think something, Jocelyn, we talked a little bit about it, I think she hit it really well. I mean, that's a great example. And I think I believe, is with the future of healthcare and data will really be moving from sick care, which today it's like, I'm sick, I better find someone that can help me.

Or maybe I should show up somewhere if I'm really not feeling well. To more of a wellness point of view. Where you can, you know, look at data, look at information, you can have early detection or prediction of the conditions that may present themselves, and then you can target certain groups of individuals or certain conditions, and then you can do things around that to, to, to create wellness.

And, you know, you see that with registries, which get, you know, putting together registries based on data that you can actually reach out to people versus waiting for them to show up when they're at their worst. And that's a big, change that I think can happen over time. And health care that really would make a big difference for the world's population in terms of health and wellness and benefit.

Richie Cotton: That is really interesting because, yeah, now I think about it, you don't ever go to the doctor when there's a real problem happening. And if you can intervene earlier then it's probably going to be cheaper as well as a better experience for whoever's sick. Now this all sounds very exciting, and I'm sure there are a lot of people who are interested in working in this field.

So, if you want to get involved in, like, data science or data analysis in the healthcare field, what sort of technical skills do you need?

Travis Dalton: Well, I'll start and you can say what really is needed, Jocelyn. So, so you're, I mean, you're talking to someone who learned to code COBOL, okay? that's my expertise is COBOL coding. But no, nonetheless, I mean, look, you know, I think it's, again, fascinating area. as opposed to some other interesting areas of technology, personally believe you could put a mission to it, which is cool.

serving health and wellness and otherwise around the world is an important thing to do. So you can get behind that. I think informatics, healthcare informatics will be just a huge need for that particularly in the United States, but also NHS and other parts of the world having worked in those, health systems myself across the world, data science, analytics, machine learning AI, the, the attributes of, intellect and problem solving, and, I would say curiosity.

Okay. Will be important. So I think employers myself, look for attributes as much as skills. and how you show up in a, in a way that, indicates that you'll, you'll, grind out when things aren't going well. But you'll also seek new capabilities and skills over time are really important and Jocelyn could probably speak much more eloquently to the technical capabilities needed.

Richie Cotton: Sure. Yeah. So, that was really interesting. But it's all about attributes as much as skills. But Jocelyn, do you have anything to add on technical skills that you need for working in this field?

Jocelyn Jiang: I think on top of that, just maybe a little bit of basic understanding of the U. S. healthcare system. Like a little bit understanding of, some basic coding or whether it's, like the diagnosis coding system, the procedure coding system, just so you know, like how things are being categorized or being coded or being interpreted to better apply the right technologies onto the right data.

Think that two combination together will really power up the way that we leverage data and then make it super actionable and predictable.

Richie Cotton: Okay, yeah, so just understanding what these disease codes actually mean and how they relate to like a real condition. Okay now we talked a lot about the U. S. healthcare system. I am curious as to whether any of these sort of approaches you're taking are applicable to other medical systems around the world.

I within Europe, for example The structure of the payments is very different from the U. S. So how globally applicable is all this?

Travis Dalton: I had the opportunity to cover global healthcare for Oracle, which I'm sure many folks know as a big technology provider. So, global healthcare operates. Differently in terms of how, payment system works. A lot of times it's a, it's more of a social approach to it based on taxation.

It's government driven with some, some private option typically available at times, but all the same problems still exist in many ways. quality, wait times high costs, readmission issues and otherwise. So I think a number of companies are working on that. For us. You know, we're primarily focused right now on the U.

S. and the work we're doing here. I do think over time as we mature as a company, as we grow and as we develop that a lot of the work that we do, particularly direct to employer or through other channels to employer, where we use our solutions for reference based pricing, for looking at claims, for looking at costs, for looking at prediction, I think those are very relevant for employers around the globe.

 There's just, the last thing I'll say about that, Richie, is that there's a massive sometimes barrier to entry for companies to do work in other parts of the world. almost every region has their own set of quality standards, cybersecurity standards data rights and usage restrictions.

high cost and sometimes it's a big barrier to entry actually for innovation. So I think it's something interesting to think about that policy really could help move forward the industries across the world if we had ways to better, to reduce the cost of entry for technology companies and businesses.

Different topic, probably for a different day, but something to think about, too.

Richie Cotton: Yeah, I can certainly imagine how the data cleaning would become dramatically harder if you got different like diagnosis codes for different countries and things like that and different data formats as well. Alright, so, maybe a plan for a different company in a different country then.

Travis Dalton: Yeah, if you, probably won't invite us back, but if you do, we'll talk about that, too. We'd

Richie Cotton: All right. Super. So, just to wrap up, what are you most excited about in the world of healthcare and data at the moment?

Travis Dalton: I think I'm 

Jocelyn Jiang: super excited about these I think GPT like artificial intelligence technologies and what it can really lead us, just like what Travis mentioned earlier about the doctors and providers these days are using these ambient technologies to really rather than type, hand type those EMR information when they talk with patients now is using technology to people.

translate those patient conversations into a digitalized, standardized you know, like data sets where you could really leverage that Normalize the data rather than, you know, messy unstructured data to further predict and the better prevent certain condition from worsening or happening.

So I think that's what I'm super excited about 

Travis Dalton: in this industry.

Yeah, I think we talked about a little bit this idea that you can start to focus a little more on wellness than just sickness I think is important. really giving individuals a, the ability to have some autonomy over their own health and, and wellness and choice is important.

So I think, you know, that access is important. Autonomy, understanding your choices through transparency frankly can be a comforting thing when you're in a in a moment where in a confusing process where someone you know or love isn't feeling their best is disconcerting. And so I think there's an emotional element to it as well over time where.

we're, predictive models can double wellness, but we're also bringing cost control. We're bringing other transparency solutions to the table that just make it, just, just makes it freaking easier. It's hard, man. It's hard figuring out. This discussion today, you know, I mean, we live our lives doing this every day.

I can only imagine how the average consumer feels sometimes. So, kind of demystifying it, making it simpler, making it easier, making it less cognitive burden to have to figure out where to go and what to do. I'm excited that I think that the next decade in health care is going to be the decade of true and meaningful innovation, and it's going to really come to the market, and it will be through technology.

So that's that's pretty cool thing to be a part of.

Richie Cotton: All right. Yeah, just empowering people to make decisions about their own healthcare. That sounds like a wonderful goal. Excellent. All right. Thank you so much for your time, Jocelyn and Travis. Like, yeah, lots of insights there.

Travis Dalton: So it's great. Thank you.

Yeah. Thank you very much.