Unlocking Medtech Success: The Power of Real-World Data with Henrik Hjorth

So I think being able to follow patients long-term without adding a huge cost to it is invaluable for any company, but especially maybe a small company with limited resources. You're listening to data nerds in the OR, a surgeon's journey toward value-based care. The podcast focused on data and data science as the keys to a better healthcare system. Ideas brought to life by the vision and experience of host Dr. Bruce Ramsha. Each week Dr. Ramsha sits down with different players in the healthcare system to discuss how data is used today and what it could do if it were used effectively. How it could improve surgical quality, education, outcomes, and drive better patient care. Let's dive into the latest episode. Here's your host Dr. Bruce Ramsha. Thank you very much for attending our podcast, data nerds, and the OR. On this episode we have CEO of Nova Scientific, Henrik Horth, a friend of mine. We've worked together for almost gosh, 15 years or so in and out of projects and work primarily with the technology that Henrik helped develop. And I'll let him introduce himself a little more thoroughly. But Henrik, thank you very much for your time and for participating on our podcast today. Thanks, Chris. Thanks for having me. And it's an honor to be asked to be on board this podcast. I've been looking forward to it. And you're right. I was just counting the years backwards. And I think we met the first time maybe in 2006 or seven, even my name is Henrik Horth and CEO of Swedish company called Nova Scientific. I've been on and off but mostly on with the company and the technology that we're working with for the better part of the last 20 years. We started in 2004. It's part of another company called Roddy Medical Systems. That was another Swedish startup in the intervention, cardiology and radiology. Part of that company and I was an engineer just fresh out of university at time. At the brilliant idea of developing the world's first long-term absorbable surgical mesh with subtissue repair and reinforcement. And that's what we did. We spun off the project into a new company when Roddy got sold in 2009. And that was Nova Scientific that we formed. So I've had multiple hats and different roles within this small organization and I took a little break and worked for another Swedish implant startup called Austin Sign for a couple of years in the neurosurgery around the patient's specific implant for cranial reconstruction. Also fascinating technology by material-based innovative implants. By a couple of years ago, I came back to Nova Scientific and took on the job as CEO. So yeah, 20 years with the surgical mesh and I'm absorbable polymers. Yeah, congratulations on that career and the becoming CDO. We'll talk a little bit about that. The pros and cons of being in that kind of role. The first thing I want to hear from you from the perspective of you build a technology and you build a company and you want to see the impact on patients. I think that's why we all go into health care is we want to see our hard work have a positive impact on patients and patient outcomes. So maybe talk about the value of data. You bring something to market and you do the pre-market studies, but as we all know, we really don't know what's going to happen until we get it out in the real world in health care and surgeons use the product, patients get implanted with a product. What's your view on the value of data for supporting a product like Tiger Matrix? Data is it's obviously invaluable in what we do and now there's going to be maybe cases, for example, some products or technologies that have made their way fast up and get a fast adoption based on other things like aggressive marketing or some kind of hype. But longer term, if you want to be in this business, you better make sure that you provide patient value of some kind. And whether that is directed to the outcome or whether it is in the system that you're improving a process or you're taking down the cost or you're making something more efficient, then that's fine too. But before you've actually figured out a way to measure what you're doing and be able to correlate that to how things were being done before, then you're really not shutting anything except the intuitive idea of things being better. And I think maybe from our stories that maybe we were a bit just comparing our technology, which is based on materials that go away over time to what the gold standard was in 2010 when we first launched Tiger Matrix, it was permanent materials. And with the background we have, obviously, this has to be better because it goes away, right? And you're reducing risk. And yeah, we ended up, I think we ended up being right. But before you can substantiate that, you don't really have much. So yeah, at the end of the day, it's all about data and being able to present data and make data accessible to people. Question is just what kind of data you're going to talk about. So Tiger Matrix, as you said, it was the world's first long-term absorbable surgical mesh, but it's just not something that goes away over time. It's designed to, so we're using two different absorbable materials. And they have different times when they degrade and lose their strength contribution to the product. And this is the thing. This is allows us to produce a device that has one set of mechanical characteristics when you take it out of the box and implant it. And then over time, it, by design, then changes its mechanical characteristics along with a anticipated kind of healing trajectory of the patient. So in our case, it starts out very firm and this, like Bruce would like to hear the park. Bruce would try, but he won't be able to do it. But then over time becomes more flexible and more compliant. And the whole idea is to then the load that is being absorbed by the mesh early on should be transferred back to the year tissue that grows into the mesh in the patient. This then stimulates collagen or connect the tissue remolding. So the idea is to use different materials to have implants that have different characteristics over the time. Yeah, and that's what we try to help do together is identify how do we measure things better both for the outcomes we want to see together in the patient, but also for supporting the technology that your company brings to the patient. I was impressed as we met. Like you said, I think I was, it was very in the chief of general surgery role. And we had a materials characterization lab with a biologic engineer, Sheila Grant. And so it was really interesting because I do remember some of those early discussions when you come to a herniac surgeon who my whole career as an expert herniac surgeon, I've used mesh and permanent mesh in the concept was a hernias a hole and you got to patch the hole with a mesh. And the first thought was, why would we want the mesh to go away? But then, you know, when your company and the colleagues you had really did a fantastic job with the pre-market studies you did, the data you generated. I think there was a three year sheep study. And then there was an ingwell hernia study in the EU. Maybe talk about the things you learned as you got some data from both animal studies. And then the human study before he launched in the US. And I'll talk more about how my mindset evolved around the use of resorbable synthetics rather than permanent synthetics. Yeah, and it really starts at the onset of starting a development project when you're getting in, because for us this was really new. We were in the vanishing cardiologists, they were data places, a pretty big role and it's pretty structured and mature already. But surgery being very multifactorial is a bit different. And it goes down all the way to what kind of requirements does a new product need to have and what do you need to live up to and where do you find that data? So when we started, we had to assess a bunch of new competitions. Don't want to share their data and let's understand the both. So we had to test our way through and then determine what's important and what's not and what's really going to make a different animal show. We got it right the first time. But at the end of the day, you can collect all these technical parameters and you measure things. And this is fun because in general, it's surgery community lists are still debating which characteristics of a surgical mesh that should be tested and how they should be tested. Even though there are standards for testing, it's not everyone's testing. You're talking about apples and oranges all the time. But that being said, you can have a product that is strong and it's big or it's thick, it's in there, it kind of fulfills all these requirements, but does it really matter to the patient? Yeah, it's ultimately you have to have that progression from a way that's small animal studies before we evolved into we figured that we need a model that has a relevant anatomy from a mechanical point of view or only a repair and it's very much a mechanical discussion for many users. So that's why we selected a sheep model and we realized also that we're always going to get asked what happens when the mesh goes with them. We need to have an answer to that question and that's why we ran a long-term preclinical study over three years, which we also published before we then entered into a first-in-man trial in Sweden, which was also part of the online documentation to receive a CMAR. When you start a project like that, then it's the first thing and it's new and you're challenging the idea that this needs to be in the patient for the rest of their lives. It helps if you're a little bit naive when you start because there's a lot of stuff you need to go through before you're even close to seeing it in patient. But the first patient was very rewarding, a very exciting involvement and there's been lots of data collected along the way. Yeah, and going back to when I first heard about the concept for a few years, I was a little unsure in the first inhuman, the inglinal data. Some of the direct hernias did have recurrences as we began to learn about the mesh complications and as we studied mesh after it came out of people, that was almost 20 years ago, it really began to change my thinking because I thought that mesh was inert. Permanent synthetic mesh was inert and we shield a grant or biologic engineer who educated me that no, when you put something in the body, it's going to undergo a biologic interaction. The interesting thing we found was the same mesh placed the same way in different people could have very different interactions and undergo very different amounts of a biologic interactions and that was really mind blowing for me because I was always teaching that it's all about technique and if you put the mesh in with good technique, you're going to have good outcomes and that's not true, unfortunately, in some patients. So yeah, I think about that time, a lot of the interest was in biologic mesh, which came in large, a very expensive and we were beginning to see the mesh complications like chronic pain, erosion, contraction and so we began to look at alternatives and when we started talking about a long-term resorbable synthetic, from my perspective, you know, the biologic is not only very expensive but they're also complex materials that come out of humans. A synthetic resorbable is a lot more consistent and that's what we saw when we began to do our clinical coli improvement project with you in abdominal wall reconstruction. We began to see the mesh had a fairly predictable performance in it, human being and as you know, we followed patients for quite a while and saw that good big concern was after three years and the mesh is gone, what's going to happen? We had nearly underpatients and a four and a half year follow up and we didn't have recurrences after the first couple of years. So it was rewarding, I'm sure, for you as well as me as the surgeon to see that kind of outcome. Maybe talk a little bit about the value, you know, once you get on the market and we start using it in a quality improvement project and you start to see the outcomes, maybe talk about the value of that kind of method that we developed to work with industry and really focus together on partnership in measuring our outcomes and learning together, how is that process for you? Yeah, no, it's certainly and it's a super interesting kind of area here because especially I've been around surgery and plastic surgery, neurosurgery, general surgery the whole time, but surgeries, there are so many factors affecting outcomes and as in industry, ultimately you want to talk about patient outcome one way or the other, right? But you really, as I said, we were able to have, you were able to have spectacular results, but it wasn't only the mesh, that's one part of it. It's everything from patient selection, procedure selection, technique and all these things that goes into ultimately measuring patient outcome in one way or another. And I think when that's where if you're a small startup, like we were, if the alternative is to say we're going to do this large, we're going to now bet on this large randomized clinical trial where we're going to measure A versus B and then you set that up and you recruit the whole thing around it and it's going to cost you a fortune and then it comes out and it's oh, we should have looked at this over here instead or we didn't see that and it's possible to back up and redo things. By doing it the way you did, we did, we do this under a set of parameters and then we change parameters and we see what kind of outcome changes we get, right? Well, we're doing in a controlled fashion and it reflects real world evidence and I think, I don't know, you the expert obviously, but it kind of makes sense if you're not taking a pill and make measuring an effect of that pill, but if you're just changing one piece of this complex puzzle, again, sure else it was invaluable because we were then able to remove an important contraindication based on that type of data and there's publications coming out of it and all sort of the traditional talking points that you need as a company to get the adoption and get users and so on. So yeah, I know there's been, there is a shift, trials, randomized trials, into registries and hopefully also in kind of a real world evidence data collection. I'm not a super huge fan of the new medical device regulations in Europe, but they are focusing more on post-market surveillance and follow-up of real world evidence and feeding that back into the regulatory borders to instead of candy having everything approved before launching, should fall. Still got to have evidence before you launch, but they put more emphasis on the old post-market surveillance thing in real world evidence. I think it's slowly moving in the right direction, I don't know if you, yeah, I hope I guess you have the same idea because as you know, I've been working on this with a small group for almost 20 years and the timing is finally getting better and I think the awareness and it's really a mindset thing, right? You know, not thinking we know the right, the one answer, but learning together how to improve outcomes through data, analysis, brainstorming. Unfortunately, people think that the doctor knows all the right answers and we really don't. We're flying blind without a good data and analytics infrastructure, but that's what the CQI method will do for us and we experienced it together and it was lots of different perspectives from the patient perspective to your perspective and your knowledge of the product and my expertise as a hernia surgeon, but to bring all that together around the patient and to see the impact on improving outcomes and the hospital was very happy because as you remember from the project, we actually saved a lot of money for the hospital and the hospital benefited financially as well. So that was a kind of win for everybody. Maybe just briefly because part of that project and part of the FDA work to remove the contraindication was the value of long-term follow-up and I know Remy, Foreman who now at Kerosent actually directs the patient effort and maybe talk about the value that that part of the project was for Nova Scientific and for the effort we had with the FDA. So kind of the type of technology we have and also in the context of what was happening around meshes at the time in surgery I think being able to show long-term positive outcomes builds trust and builds comfort with everyone and I can especially I can tell you there's an inventor and I say responsible for the company being able to know that there is not any kind of you know you got to everything is covered if you have a long-term success rate you know to expect especially when your project goes way over time it's super important. So I think yeah being able to follow patients long-term without adding a huge cost to it is invaluable for especially for any company but especially maybe a small company with limited resources. Going back to surgery being so multi-tractorial that it's you think you're looking at the Avers as big but what you should really focusing on what's happened with C over time and like someone once said that to any complex problems there's always a simple solution and it's wrong a little bit of that. Yeah it was the complexity of the patients these patients with complex abdominal wall urnius and recurrences that was a tough group and through our project together over a period of years we were able to see a lot of factors that we didn't think of at first the one that stands out and that I use a lot is the emotional state of the patient the neuro cognitive state of the patient when you've had a patient who's had 10, 20 hernias repairs and their intestines and skin is stretched down to their knee that's very disfiguring that's a trauma to a human being that is very very challenging to understand and deal with but yeah as we were able to use data science principles we were able to define the neuro cognitive state and then begin to do prehabilitation and implement preoperative things like cognitive behavioral therapy to help them resolve and deal with the trauma that they experience often from prior surgery and the results of prior surgeries being able to understand how to use data science tools to reveal those factors that we can then modify and do something about and then see the impact on outcomes that was very rewarding. It goes back wasn't it like how you define patient outcome what's the successful patient outcome and when and in time should you be measuring that is that just you left the hospital or how was your life looking one year after and I mean now Tiger Matrix is being used predominantly in breast reconstructive surgery and cancer patients which is also it's a very patient group that has normally been going through a lot and it's reconstruction and then there's a bit of those expectations on static outcome as well and you deal with these complex scenarios and then a question comes alive again so when do we consider this being successful and what should we be looking at more to be able to talk about the later and when in time yeah and I think that's an open and a question that we can always do better with when we learn how to work together with multiple perspectives and listen to the patient perspective reminds me of the I use the money ball example major league baseball when data scientists finally got major league baseball to listen before that everyone used the same exact measurements the box score hits runs RBI's and nobody was innovating around how to measure things better but once they started to learn the principles of data science they were able to use much better measurements and it led to at least short term a competitive advantage for the teams that did that first now there's no competitive advantage because they're all doing it but in health care and surgery we have the opportunity to figure out how to measure things better and one example that I use comes from our abdominal wall reconstruction CQI project where we were measuring and this is really important to our project we were measuring wound infection and wound complications the way that everybody does because the CDC mandates reporting of wound infections based on the measurement of superficial deep and organ space and I think Remy talked to some patients who had wound infections and and asked what do you think of this measurement they were like that doesn't really matter to us and basically said well how should we be measuring wound infections and they basically said how invasive is a treatment required to heal our wound and how long did it take to heal our wound and when we measured it that way it was a much better reflection of value than the CDC definition so the point is when data science principles we can always improve how we measure things if we put our minds to it use different perspectives so but maybe yeah I was just saying that maybe in the industry we're a little bit more in a way used to having a continuous process because we have to do a continuous improvement right that is the essence of the regulatory systems and version I said 34 H5 you need to have a quality management system that is undergoing coolton scrutiny and you need to check and make sure your processes are adequate and then they should improve them all the time you can't fix the thing we're just doing one study it needs to be an ongoing process towards a volume to something better but again health care is it's it's on a small beast but everything bring up a good point when we were learning this and we were looking for how do we get health care to embrace the mindset of continuous improvement we thought the natural partnership would be working with industry because of exactly what you said I knew as a surgeon I was not really taught well how to measure outcomes of the whole patient process and use data science tools to improve those outcomes and I know hospitals were not taught that payers were not taught that but it was really industry was the only stakeholder in health care who really had that mindset of continuous improvement around their products so that was part of our thinking when we started our business around partnering with industry rather than going to payers or going to hospitals at first but I think more and more of the stakeholders in health care are starting to embrace this hopefully the timing is good to get this more skill because like he says health care is not a small thing it's a major global problem bad also anyway now you have this or you're starting it out and I'm sure care syntax if any has the best script on what you can do with data today versus what you could do 10 15 20 years ago and now it's a whole different the possibilities are very different now than they used to be in terms of measuring in terms of storing analyzing AI what out of it it's exciting to say where where we could end up yeah that's part of the time right the technologies are there we we're using you know LLM and Gen AI technologies to be able to acquire and clean data that we couldn't do you know a few years ago so that's hopefully it's all coming together maybe that's another question do you see differences in the use of data or the ability to use data in the EU versus the US are there differences in the privacy laws anything that you've noticed in your role yeah that's a good question I mean Europe it's not one then today right from that point of view and I think it's more about maybe cultural differences between surgeons and different regions of Europe or different regions of the world normally maybe my impression is maybe that in the US culturally you're a little bit more adaptive when it comes or you embrace new methods change a little bit faster maybe but I might go back to also there is a competitive system built in to also to how the surgeons operate that you know might be part of driving that too that you want to be at the forefront of adopting new things and new technology whereas Europe which also differs a lot internally has different views on these things I'm a little bit maybe concerned with Europe as a device market as a whole with the MDR now being in place making it really expensive and complex for companies to maintain and renew their CMARC even for production devices that have a flawless track record I think there will be important devices that are going to have to be taken off the market unfortunately it's it's unintended consequences I think I'm much more familiar with the FDA and we had David Earl on the podcast I know the people have good intentions they don't want to they want to do you know regulatory well that helps patients but I think they're and we've seen it in the US the unintended consequences are when you put too much regulation you stifle innovation and like you said we at the point where we may see good products being removed from market because the universe regulatory requirements and you know I hope that they'll be adaptive I think the US FDA has tried to be adaptive and I think we're just in the beginning of moving to real world evidence in a way that can be done very very safely and very efficiently and much less costly and so I think that's part of our future I hope care syntax with our methods will help to show how we can do that and make the patient value be the primary focus and in doing that we can minimize some of those burdens and I think the system that comes up with the best method to collect and utilize that also from a regulatory and a risk point of view we need to be part of that and collecting that along with surgeons and hospitals but I think the system that implements that in the smartest way first have a lot to gain in terms of weeding out what's good and what's less good but it's on the system side and then it's also you need a system regulatory system that is adaptive too and I mean the European being what it is with the third party water system it's a little bit slow in making changes I don't know if the FDA being one administrative body at least might be a little bit faster or if it's if maybe it's another device market that is going to come up and say hey over here we can make this happen but it needs to be a joint kind of everyone needs to be working alone the same ideas eventually and it doesn't seem to be any other way than just looking at what is best outcome well I think one of the values of our method is bringing your team the device team to the surgeon and the clinical team and working together maybe talk a little bit about the value of having that kind of relationship in our method of bringing the different perspectives together around the patient outcomes the value of having access to the clinical team as we learn together invaluable obviously and I wish we'd met earlier but you know it needs to be back and forth already from the idea stage and we would like it to partner out early with some super innovative very experienced surgeons it came more from the bind side but had lots of solid experience of biomaterials and how they work in the body and you're able to quickly kind of narrow down your specifications but just blindly developing something and then go to a hospital I just remember when we had a mesh that the first version you could just tear it apart you couldn't put a stitch in it and I went to hospital and waited and showed this and said and what do you expect me to do with this you know there's the instant things I probably should have realized that but being able to get that other perspective because it's all different and being in a hospital all day or dealing with patients all day and we're I mean this is what data nerds podcast and we're kind of mesh nerds and then you think when you're a mesh nerd you think well that you probably care a lot about meshes right there are soft tissue support and then we speak to a surgeon who's a user of meshes even though it's like it's such a small piece of the daily concern is which mesh to use that we don't have that perspective because it's so yeah so being able to partner up around the project as a whole and not just designing a child is invaluable and yeah I really mean I wish there would be more launch companies have different opportunities possibilities in terms of having clinicians and hospitals healthcare practitioners on board early but if you're a small company you're relying on people who want to innovate yeah I want to pivot a little bit we had a lot of discussion about your perspective on the value of data applied to your products but I know you personally have gone through some changes in your career similar a little bit to me where I went from a clinician I was researcher but then entrepreneur and eventually took on the role of CEO of a data science company a healthcare data analytics company and that was a big change for me I'd like to hear your perspective going from an engineer to now CEO of of Nova science of it what kinds of personal changes did you experience going through those different roles I love innovation and I think the early part of the project the start of a clinical medical device project or it separates I think as is the face when you get to launch and you get to succeed being used between those there's a lot of other things that need to be done and I was extremely fortunate to be at a company for the perfect size the raw medical systems was 150 people maybe and it was small enough so that you would even if you were young and could us to the upper management and owners who had this culturally implemented that give responsibilities to relatively young and fresh employees if they wanted to take it on but I was able to see all the steps hands on in the journey from idea and IP generation and writing patents all the way up to starting to sell a product we've done that once you've been through all those hoops it's invaluable because then you are pretty comfortable that you understand the challenges day-to-day of everyone who works in those boxes in a larger organization maybe or anywhere and that gives you the ability to communicate in a relevant way with everyone because you can a little bit adopt their perspective because I've also been in and out of quality assurance quality control IP sales clinical education you have taken a little bite of all those things and I think what it did to me was that it gave me a respect for everyone else's daily challenges and priorities and which then when the time comes and someone else would you want to take over this the whole bank here the whole organization is well yeah because I think I've benefited from being able to just have that perspective and being able to communicate with everyone and not just sometimes CEOs in our businesses taking from one end of the organization more than from the other and that kind of skews the whole thing a little bit maybe but yeah I just it's invaluable to being able to see all those even if it was for a limited period of time but you still you see what people are doing you understand it you don't necessarily understand all of it actually but you at least you can have a relevant conversation it gives you yeah because communication is everything when you're a leader right I was just gonna say I think you just described what a good leader does and is and it is understanding all those different perspectives for me it was a little bit of a shift from having been an academic surgeon where I was used to teaching people and telling people about mesh or hernias or whatever and being a CEO it's very humbling you have to really listen and ask questions and understand that especially if you haven't been through all of the different aspects of the company like you have but I think that was really description what a good leader is and does and how they interact with the team yeah but then I will obviously get different data to work with as a CEO that's something it's also super important to know what you're doing and what kind of priorities you have and especially from a small company point of view maybe certain data points are more important the mother when comes to how you prioritize it's been a fun journey I know idea it's gonna be a get stuck in medical devices for 20 years but I think that's the kind of the blend of healthcare hospitalizations which is really like very real and then you have labs and everything on the other end and it's a multi-paceted industry especially I think it's more companies where you can spread out early on and do all kinds of things yeah and I again back to healthcare we know we all go into healthcare because we want to see impact on improving outcomes and I think we're all aware that healthcare has some major global issues in terms of costs and outcomes and sustainability and we're all struggling to figure out how can we help the system transform to a sustainable system where costs are going down and outcomes are getting better and better have you seen any movies books anything that kind of has been motivational to you to encourage this healthcare transformation anything that comes to mind the thing I would like to kind of push a little bit for is to follow suede is fortunately no longer with us but Hans Rosling who is the founder of gapminder and gapminder.org is the website he also wrote the book called Factfulness which I recommend everyone to take a look at which really highlights importance of having the real facts when you make decisions. I've heard him live at a surgery meeting here in Uppsala where he challenges the perception of what do you think when it comes to these questions what is the average life length difference in the world and where is educational what about women's health and you think you have an idea of how things are but you're wrong because that was 30 years ago so that would be the one thing that I would do because it also he was a doctor and it touches on health care in or public health in many ways so they are super interesting really motivating and very relevant these days from one kind of aspects yeah that's a great suggestion I know of Hans Rosling through the TED Talks that he's given extremely entertaining so anybody searched Hans Rosling in TED Talks and you'll see some pretty amazing data demonstrations I agree Henrik thank you so much for your time I need any questions that come up based on our discussion so far perfect what when you talked about the idea that previously messers were not resorbable they were static permanent tissue repair devices what was the idea that took you guys from the current state into the resorbable mesh because I think that's a really interesting story so we had accumulated this kind of top-level competence not myself but a guy called Torbjörn Mathisson who's the world-leading expert on biomaterials and especially their applied into medical devices and we wanted to actually look for other areas where we thought absorbable materials could replace permanent materials and we came across surgical meshes for soft tissue reinforcement based upon I think at the time there started to be this case reports of the complications that you can experience if you have a chronic inflammatory response and the chronic foreign body reaction to a mesh that is then implanted into certain parts of the body and it looked like an area that was the needed improvement and it turns out later I mean in England it'll hurry up and many of the you can probably second this verse many of the normal or near repairs permanent materials do very well there's a very cost efficient good way to address that problem but we've also seen that in other areas permanent meshes do horribly and the risk benefit ratio is not that good yeah it's not just the materials also where you use it to know yeah like I said when and we first when I was first presented with residual synthetic concept it was like why would we want it to go away but then as you mentioned and as we learned through the data that there's different subpopulations of people and I learned that even in a subpopulation of patients maybe no mesh is the right answer a tissue repair is going to have the best value and another subpopulation a resorbable synthetic will have the best value and another permanent synthetic will have the best value we really want to be able to apply our data science tools at care syntax to define those subpopulations and match them with the best value treatment and that's how we're going to have a sustainable healthcare system I think I agree anything else on you maybe one more Bruce we face CQI a lot and it's we have to assume that some people will be tuning in for the first time but we probably need to define that term a little bit for the audience as well it's almost ingrained in my language to say CQI because we've done it for so long but clinical quality improvement or continuous quality improvement is is what CQI stands for when we talk about our care syntax offerings we look at working with medtech and provide clinical data as a service so that's the the method behind our clinical data as a service is a continuous quality improvement method really it's continuous value improvement but everybody's familiar with CQI because of the quality in the other perspective is for providers it's really trying to assess the value of any product within their actual patient care so clinical value analysis is also a concept that we're discussing with our provider partners and clients but yeah it's all founded on the mindset of like measurement and improvement if we can measure something that we can use data tools appropriately we can improve what we're measuring and that's really what we need in healthcare if we're going to have a sustainable system which I think we all want thank you Henrik and thank you on you thank Bruce yeah thanks guys really told the conversation and Henrik will be back in touch real soon because we have some data yeah because we're doing it again right we're doing it again we've got three surgeons three plastic surgeons have data and it's super important yeah that's gonna be good I think well thank you all very much have a great rest of your day yeah yeah [BLANK_AUDIO]