Pharma Market Access Insights - from Petauri Evidence

Navigating market access: Launch strategies and health economic modelling for medical devices

• Petauri Evidence • Season 8 • Episode 1

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Discover how health economics can support successful Medical Device launch, proposition development, and market access

This episode offers an insightful journey into the world of medical device market access. Specialists from our health economics and market access teams explore the hidden challenges faced by medical devices companies when taking new innovations to healthcare leaders, communicating value and demonstrating evidence, and offer tips on leveraging health economics for a successful launch.

Juliet Wallace (Senior Partnerships Coordinator) delves deep with our expert panel into the pivotal role of health economic evaluation in supporting market access for medical devices. Juliet puts audience questions to Hannah Palin (Director, Local Market Access), Calum Jones (Associate Director, Health Economics), and Evelyne Priestman (Consultant, Health Economics).

See full details at: https://mtechaccess.co.uk/launch-strategies-health-economic-modelling-medical-devices/

This episode was originally broadcast live as webinar in February 2025. 

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- Welcome everyone to our webinar, Navigating Market Access Launch Strategies and Health Economic Modelling for Medical Devices. Please see our YouTube channel for past webinars and look out for more content from us in the future. So for those that don't know us well, Mtech Access is a global specialist health economics outcomes and market access consultancy with a track record in expert delivery. We provide specialist support to MedTech and Pharma clients as part of the Petauri platform. We now collaborate directly with colleagues in the US and Europe, with a broad range of specialisms all working to improve patient access. So firstly, I'd like to introduce myself. I'm Juliet Wallace, Senior Partnerships Coordinator at Mtech Access. Today I am joined by my colleagues from our market access and health economics teams. So we have Hannah Palin, who is the Director of our Local Market Access team. She specialises in the launch and commercialisation of new healthcare interventions. She's an expert in developing materials that communicate the clinical and economic value the products deliver within the care pathway. We then have Calum Jones, who is Associate Director in our health economics team. Calum has deep expertise with early and global health economic models to support strategic decision-making, reimbursement, and HTA. And then last but not least, we have Evelyne Priestman, who is a Consultant in our health economics team. Evelyne's expertise lies in leveraging health economic modelling for the commercialisation and launch of medical devices. I'd like to say thanks to everyone who's submitted questions in advance. Over the course of the webinar, we'll do our best to get to as many of these as possible. Please use the chat box or the question box function to add any additional questions over the course of this webinar. And for any that we don't have a chance to get to, we'll make sure to follow up via email after this webinar. Amongst the attendees today, we have a range of experience spanning many types of medical devices and technology, including attendees based in lots of different geographies. We will therefore try to make sure all of our questions and answers abroad and apply to as many of you as possible. Okay, so I'd like to start with our first question to our experts; why is it essential to establish a clear value proposition for an effective market access strategy in the MedTech sector? Evelyne, would you like to start?- Yeah. Yeah. So I think this is one of the key differences between medical devices and medical software and active Pharmaceutical ingredients as an intervention. With an API, it's very clear what the effect is. With a medical device or a piece of software, it is not always clear what the benefit is. So you need to spend some time to really think about, well, what benefit is this providing to the patient, to the clinician, and also to the healthcare system. It's more of a traditional approach you would maybe use in consumer products to really match what all the different stakeholders require and need, what their jobs to be done are with what your solution provides. Because once you have established those value claims, that is really the core of your value proposition and the market access and the health economics. They are there to support you in communicating the value proposition to your users and your stakeholders, which is also purchases, et cetera.- Thanks, Evelyne. Calum, do you have anything to add?- I just, yeah, I think that's, that's really well put, Evelyne. I mean, I think, I mean, when we're, when we're trying to communicate our, the value of a new medical device, I mean it's, it's a reasonable rule of thumb to try and focus in on, I think two or three, maybe four, probably two or three value drivers. So we're thinking, of course, in the realm of cost savings, patient outcomes, or even perhaps actually sort of streamlined care practice, we might want to see a sort of a procedural over sort of efficiency benefit there. And so in doing so, if we can narrow that down into a few value claims, value drivers, we can avoid actually just overwhelming our target decision makers, which is a very tangible problem that we can come up against and ensure we're in that respect in addressing their, their main priorities. And actually to that end, I mean this, this all comes down in, in a health economic modelling context to, to defining your, your pico of course for your device, that population intervention competitor and in this case outcomes. And of course, this should be defined really from the very outset of what we're we're aiming to achieve here and as early as possible. I would also note that, if we're trying to substantiate claims around those value drivers and we're thinking about the data we need to use, actually, it's a good rule of thumb to be made using data that's as local from the perspective of the decision maker as possible, that can be really persuasive. So reimbursement decisions for devices are typically going to going to happen. As many on the webinar are likely to know at the local or regional level that end real world evidence from say, pilot hospitals or regional contexts are likely to more likely to resonate than, than we might expect. Broad national or international data to, to local is a, is a good way to go in that respect. And, but making sure that those, those value drivers are few and as pertinent as possible and that's inherent within that early defining of your pico, these, these are key tenets to adhere to.- Hannah, anything to add?- Yeah, to echo, I think call and Evelyne have covered a lot of the points that on projects when we're supporting more, maybe it is models, maybe it's written communications or presentation communications, it is being able to go back to what is your core offering and also reflecting back on how have you got there. So strategy can, in the development phase be quite open-ended. It may have still cases where you're still to prove out whether you can stand by that claim or whether you have to adapt it, but thinking about it early as possible is really important. And evolving that strategy as you develop your asset, as you refine it, as you identify the most viable or attractive use case, which I know we'll talk about a little bit more. And then also bridging that open out for and third, that as you look at different market dynamics and different stakeholder dynamics, so that having that core truth, it sometimes gets referred back to that core set of statements and claims. It keeps things consistent. It allows you to place yourself very clearly in the mindset of the people that you are engaging with and also be able to go back and actually prove all the things that you are explaining or, or even if you are in a situation where you aren't in a point of proof, you can collaborate with stakeholders to stake, find that evidence to generate that source off the back of everything that you already know or that you hope to prove as well.- Great. Thanks all. So this moves us on nicely to the next question. How does the care pathway mapping and modelling help in identifying the optimal positioning of a MedTech product? I'll start with Evelyne if that's okay.- Yeah. A again, I feel, I mean I've spent over 10 years in medical devices and software. That's, I think we're also a big differentiator to what you would usually see in Pharma. You, your medical technology, your medical innovation, it needs to fit within the care pathway. You want to minimise any disruption to the care pathway if possible. So you need to imagine how your users will use it within the current pathway. Will it add to it, will it enhance the pathway? Will it change the pathway completely? And if so, in what way? When should it be used before something, after something? Should it be used once, twice, several times? All these are questions that are important to define early on because it, it influenced not only your value proposition but also how we will communicate that to your key audience and how we'll model it. As health economists, the cost and the health benefit of the innovation depend on where it is used in the pathway, how often it is used, and if anything maybe what decisions it influences further on, on the pathway.- Okay. Calum I think you're frozen.- Oh, I think Calum's frozen.- It's well, in that case Hannah.- Yeah, that's fine- In there.- Yeah. I mean, Evelyne and Calum obviously come into this with an understanding of the pathway and use that as a basis for modelling. I guess I come in from a slightly different angle, although it's completely complimentary, which is how are we going to commute, how this communicate, how this pathway will change, how do we get stakeholders invested and tie them in? How do we tie in how that care pathway has to evolve or has to change or looks different? How does that meet objectives that aren't automatically just financial objectives? Obviously most of these stakeholders do have a financial objective, but you know, lots of words that we know are time and time coming back. Again, how, how does this link into sustainability target? How does this link into benefits for the patient? How does this drive care into a more of an outpatient environment, for example? So a care pathway and a care pathway understanding can really support a lot of activities, a lot of things that you may need to prove to kind of drive uptake, but also as a way of engaging and get stakeholders invested in making the changes and really understand the changes. And also, you know, care pathways should, if done well and if rigorous and if kind of up to date take out an element of risk for stakeholders because they should be able to, through quantitative methods or qualitative methods, be able to implement your device, whatever that might be seamlessly into what they are. But I think that's the caveat is what we often find is there is no one size fits all for care pathways, especially in situations where funding and reimbursement decisions is at a devolved level. Maybe some of you have seen recent white papers and similar talking specifically for the UK about some technologies around software and similar shouldn't be devolved anymore and they should be more a centralised decisions. But every market will be handling these differently. Each device and where it fits and how it's going to be reimbursed will affect ultimately what a care pathway or how many care pathways you need to consider. So for me, a care pathway should also be complimentary to a reimbursement pathway or a funding pathway. So maybe that's just a few examples of where we've been developing them to support various clients and where we've seen looking at a care pathway as, as a particularly important and, and important piece to do.- Yeah, that's really interesting.- It's also important to, to add, there is a, there's obviously huge variability in care pathways. Something may be done, a disease may be managed in one way in the UK it's managed in a completely different way involving completely different healthcare professionals in the us. So that's something also to bear in mind. We try and maximise the flexibility within our models to account and to allow for different care pathways depending on who your audience are. But that's something to bear in mind. There is, there can be a lot of variability in terms of how things are done.- Absolutely. Just quickly chat, Juliet, can you hear me?- I can hear you, yes, we- Can. Well, I'd like to apologise everyone from my webcam not working. Not ideal on a webinar, but I, if I can be heard, that's great. I would, I really Good point, Evelyne and Hannah, I just thought that the, I mean, of course from a health economic, economic modelling perspective, I mean whether our device is going to be replacing or adding to the existing care is standard of care is extremely important. The critical consideration, it's going to directly inform how we define the devices PICO, that population intervention comparator outcome, it really is framed around that and therefore standing from the, from as early as possible. Actually in our endeavour here, the, the pathway that we're, we're either adding to or, or indeed replacing that, that's really couldn't be more important. And, and actually if we're outlining each step in the care journey or the patient journey, ideally from really diagnosis right through to follow up, but that can set us up really nicely to assess which staff resources or procedures the new device might affect of course, or which clinical outcomes are going to be of most pertinent and feasible to, to, to demonstrate the benefit in. Ultimately, of course, what we want to do is create that detailed picture of that existing care pathway, putting us in nice good stead to ensuring that we're measuring what truly matters for the decision makers at hand.- Thanks everyone. That's really interesting. So this one is actually probably most relevant to you, Calum. Why is it important to start health economic modelling early in the product development process and what benefits does it bring?- Yeah, no, brilliant. Thanks. Good question. So I mean, when, when you first come to substantiating claims for a new medical device, think particularly the local procurement level, which certainly in the UK is, is is the standard approach. It's usually required to demonstrate for our decision makers how it's going to affect their, their short term budgets and their, their resource use. For this, it's particularly helpful and a good idea to source early stage data. So this comes to the, the, the, the need for, for early action here. So even from a small pilot that could be, and that could be really driven to undercover, to uncover rather the key cost-based drivers, which obviously could include say staff time or, or consumables. And of course the, the, the costs, the, the basises for potential savings that that might be readmissions, re reductions or reduction in procedure time. And you know, we can use these insights then from that early level, that early stage to inform the foundations of that initial economic model be that a budget impact or cost, which tend to resonate best with NHS trust and ICSs and regional procurement bodies. Also important at an early stage and, and, and it's probably in, in concert with, with planning and running small pilot studies, starting to really think about pertinent, I think sort of scenario is essentially that, that are going to demonstrate it, that really it's it's touch on the key value claims that we were making. So what scenarios do we think are going to give a payer a local decision maker? A sense that we've tested a broad range of real world possibilities such that we can enhance our overall confidence. And usually what we'll do is we'll start with a, a best case scenario, let's say where for example, with the device training ramps up smoothly over time complication rates will drop and a worst case scenario capture that where say add up to adoption's, going to hit a snag or costs overrun. And then we can look to refine these scenarios as we start to collect more data. Ergo of course here, hence we can see that starting as early as possible is, is key to, to making sure that as we, as we progress down that, that time we're adding to perhaps early stage pilot studies. We're thinking of introducing registry data, we're thinking of introducing natural history data to establish a, a good external control arm. And it's, but it's really all about as early as possible trying to understand what, how can we demonstrate to a, to a, to a decision maker, a target decision maker that we are not presenting a a theoretical or broadly in inapplicable set of data and assumptions and scenarios to the, in, to the needs of that individual decision maker. But actually they reflect real world consequences. And, and indeed the, the the, the fee actually reflects the feasibility of of rolling out a device in, in a, in a centre or a setting that actually looks like the, that of the decision maker. We can never, we'll never find that it wasn't worth starting as early as possible in in these these projects.- I'd just like to add to that also there's value in doing some early modelling before you have data before the pilot stage and I have done many of those models, therefore internal use only, you wouldn't show them to external stakeholders. However, it's an incredibly useful A to find out what is out there, what evidence do we have. Usually it's combined with a literature review of some kind'cause there may well be enough evidence out there or a lot of evidence out there that reduces the amount of data you will have to collect eventually in a study. And also to test your value claims. I have done many of those models where we've quantified all the value claims that the innovator wanted to make and then run the sensitivity analysis, run some scenario analysis and you can look at and see which ones have the biggest impact and the biggest impact in terms of health outcome and or budget impact. Budget impact may not always be the one that you thought was the impactful And trying to quantify these in an early model and playing around with it a little bit internally will crystallise more, which are the big hitters in terms of your claims, what are the claims that where your innovation really makes a big difference. And that's where early modelling can be really, really useful.- Great, I'm sure that's really helpful for our listeners. Okay, so moving on to the next question. So I know we've mentioned pilots quite a few times in that previous answer, so let's focus on that. Lots of med tech companies are asked to do pilots. Where does this sit in this conversation?- I wonder, Evelyne if you want to start- With Evelyne for that one. Yeah, this is an interesting question because I think you'll get three different perspectives on this. So I have spent the last six years mainly with early innovation and innovative medical technologies and devices and their pilots are really useful. Often the way we approach things is do an early model, leverage what is available on the published domain account for uncertainty and present that to maybe the clinical trials you need in a hospital or maybe some hospital stakeholders and say, look, we've done this, there's a lot of uncertainty, how about you let us do a pilot in your site so we can validate the assumptions we've used in this model. We can collect data and therefore reduce the uncertainty at an early stage. It's a really effective way to, to make some noise, to get exposure for innovation in some key hospitals. Ideally you want to select hospitals that are full of KOLs who can then maybe do a publication on what they have tried. So from that perspective in an early stage innovation, they're really useful. But I know my colleagues also have other experiences in this. So I'm handing over to Hannah.- Calum, would you like to come up first?'cause I thinking yours probably is from what I'm about to probably handle- Of course. I mean certainly from an economic modelling perspective, I mean as I've alluded to already, I mean pilot sites are, are tremendous, potentially pretty transformational source for informing the evidence base for, for our model.'cause of course they, they're generating, and this is critical not just in medical devices but but more broadly but but real world evidence in everyday clinical settings, incredibly powerful information potentially if executed and collect and collected and and and communicated correctly. This can be pretty invaluable for, for convincing potential adopters. I find it and if we can observe through pilot studies, you know, how the device is going to perform in the field. For instance it's tracking staff time, training hours, trainings a large issue that could be overlooked at times clinical outcomes, complication rates, certification levels of patients, then ideally admittedly through a snapshot because of course these pilot studies are are not likely to run for more than a matter of months usually whether the expected benefits and perhaps efficiencies from using the device actually manifest in reality. And I can't emphasise enough just how important it is to have to demonstrate that there is good reason to be confident that reality will bear this out via the projections of our model. We'll also be interested to see whether the device consumes more consumables healthcare resource utilisation than expected and whether for instance, these are interesting nuances in medical device evaluations, whether maintenance and software updates perhaps introduce costs that we didn't foresee before. The pilot study and pilot sites I think can also be useful for getting a good initial sense of which subgroups of patients are likely to benefit most from the device. This, this patient segmentation exercise can be greatly accelerated and enhanced through through pilot sites, which obviously down downstream is going to be going to deliver some nice efficiencies for helping us to focus in on those best super populations and segments. But sorry Hannah, please I'd love to hear what you have to say about this actually.- Yeah, I think I should probably start by, I do completely agree and recognise all the positives I guess that both Calum and Evelyne have highlighted about pilots and I do completely agree that they can generate that. I think without sounding like the bad cop, I think I just wanted to reflect when we did have this question posed to us is that frequently we are working with clients or approach clients or in discussions with industry or or whatever setting about how can we avoid doing a pilot every opportunity at every site, you know, every location that we approach, whether it's in the UK or any other market, they want us to run a pilot, they don't either. We, even if we've done pilots before, even if we you know, have good strong RCT evidence, et cetera, et cetera. So this is kind of, I guess once you've gone through that first innovation stage, you've identified where you should fit and where you have value and where you've demonstrated quite a lot of that data generation. And that's constantly comes up in conversation. It's, we are constantly being asked to do pilot after pilot after pilot. How do we get out of this cycle? And it's really interesting and I have to admit it feels a little bit, it's unique to every situation. It's, it is genuinely unique to the device or the med tech that we're talking in question. It's unique to the market that we are talking about and it's very much unique to what's happened before. And so I think that's why all the cases and the aspects that Evelyn and Calum have kind of indicated and hint at are so critical to ensure that you don't end up in this constant pilot cycle. It's really understanding what they're going to need when you are running a pilot, it's about ensuring it's powered correctly. So it is valuable, it is publishable, it represents what it is going to replace or what it's going to be complimentary to. And those are the cases where we've seen the pilots that have really been successful and that our clients have been able to get out of that constant cycle of being asked to do a pilot before anybody will even commit to funding them in the long run. It's those sorts of incidences. It's when they've thought about how is the data that I'm going to collect beyond just capturing a clinical benefit, how do we ca ensure we capture the system benefit in a way that is replicable for at least everybody in that market. So the UK for example, is very interested in how long a patient is in a clinical setting, who are they interacting within a clinical setting, which tariffs have been triggered for example. So it's, it's really important when you are designing these pilots is to try and future proof and really think all of those aspects that people are going to want to see evidence for that don't come out of normal RCT type trial designs and where pilots really allow you to do that. But if you focus too much on just we want to see what a clinical outcome is time and time again, we are seeing that people just end this constant cycle of pilot after pilot after pilot and obviously the more potentially disruptive you are to the system potentially. That's also I think something that we often see of people being asked to repeat pilots because they're kind of not able to explain and demonstrate how actually this disruption can be managed. It's not going to be painful forever, it's not going to need constant investment, at least from the healthcare system and how they can see that trade off quite quickly. So going back to the earlier discussions about understanding the care pathway and other aspects like that, they're the types of instances where we've seen, we've been able to potentially shift the conversation away from we've got to repeat a pilot, we've got to repeat a pilot. And obviously the earlier you can have that mindset, that's obviously an advantageous because you are approaching it in that manner. Obviously it's incredibly challenging potentially if you're almost doing it retrospectively when you are on the market and you are facing challenges. So I don't want to sound doom and gloom on pilots because I definitely think they have a really valuable important place, but I think my, I guess caution is that you've really got to think them through. You've really got to engage with them and make sure they really fit where you are going to place yourself in a given market, especially if your device has potentially lots of different use cases or lots of different slotting. Again, it goes back to where's your positioning, where's your valuing? But equally if you do very early pilots, maybe you need that flexibility and you may have to repeat them when you have home down exactly where you sit in a healthcare system. So hopefully that's a few slightly different angles than what Evelyne and Calum explained.- Yeah, great to hear all those different perspectives on the same thing. Fab, so perhaps for the next one I'll come to Evelyne first and then Hannah. So this is how can you get your medical technology in front of key decision makers and what are the bottlenecks in communicating a device's value to various stakeholders?- Bottlenecks as always data you need to, your data needs to be stronger, evidence needs to be strong before you take it to important stakeholders to convince them to do more than just a pilot because otherwise they will say, well your data isn't very strong, you need better data. Come back when it's better. Never underestimate that. So that's the biggest bottleneck we experience always is if sometimes clients do go out when the data isn't completely solid yet when there's still quite a lot of uncertainty because they will pick up on that. So from my perspective, that is the biggest, the bit biggest bottleneck, having the right data to make a a solid case to convince stakeholders.- Okay. Hannah, do you have anything to add on that?- I would say sometimes, and this is not unique to meta tech or med advisors, the stakeholders can be the biggest challenge. Identifying the right stakeholders, getting them to respond to you, figuring out what's going to trigger their interest, who is the right stakeholder class for you. There's obviously a lot of research and gathering and information you can do to try and narrow that down. Looking at funding flows, looking at what, what they interested in, where does your device potentially favour some of the things that they are trying to achieve. But it is not simple. Stakeholders are forever changing. Markets change the change how funding changes, stakeholders change roles, stakeholders may have different role titles and different functions. And I think it's also really important that, you know, in a lot of markets stakeholders are, are not just funding decision makers, they are responsible for lots and lots of different aspects potentially. So there is never usually one golden stakeholder that if you engage with them they will make all your dreams come true. But there is a lot of what you can do to improve the chances of when you do get their attention and when you do get that time with them, going back to Evelyn's point about having the data ready or at least having a plan of how you're going to communicate what you're doing next, you know, preparation is key because you may just get a 20 minute golden window with them in the first instance and they will make a decision of whether they want to continue engaging with you or not. You know, and there's lots of bodies and there's lots of people interested, you know, now we can use technology as a whole to improve healthcare systems. It's, it's, it's a hot topic, it's been a hot topic for years and years and years and it's definitely seen as a key way of making healthcare systems evolve and be fit for the future. You know, irrespective of what market we're looking at. We've got growing populations, we've got ageing populations, we've got pressures on budgets and skill pressures in terms of healthcare providers. You know, technology is time and time again pulled out as the solution to at least solving some of these problems or being part of the solution. But it isn't easy, it isn't easy to engage with those stakeholders and things do move. So there's, I think it's just to kind of, I guess not underestimate some of, in, I essentially am referring to the preparation that you may have to do to ensure that when you do get that window, you absolutely make the most of it.- Right, thank you. This next one speaks to probably Calum and Evelyne more. So Evelyne, can you discuss the role of health economic evaluations in gaining market access for medical devices?- Yes, I think partly of what a lot of what we discussed is is about that. Even though they are not quite assessed in quite a stringent way as pharmacological interventions may be by NICE, nevertheless they are being scrutinised at the local level now more, the national level. Although now with NHS England being restructured, that may be up for discussion again, as things are forever changing, but they will be scrutinised and both the, the impact on healthcare budgets will be looked at very strongly. We are in a position of scarcity and that's where we have economists operate in. But if it does impact the health outcomes of a patient, if it improves health outcomes, that also should be demonstrated. So it is crucial to have your health economic case ready for when you start engaging with, with stakeholders in the healthcare system.- Great, thanks Evelyn. Calum? Yeah, I was just passing over to you, but also it would be great to see how you can capture the long-term benefits from devices as well.- Sure, yeah, no, I mean a lot of it I I think you're absolutely right Evelyne. I completely agree. And, and I mean, yeah, I mean absolutely in the, in the uk most as, as most are, are likely familiar with day-to-day procurement decisions are going to happen locally or, or regionally. So, you know, that's again the, the individual NHS trust or ICS or regional procurement level. As we know, of course, these bodies tend to have finite budgets. They'll tend to focus on practical affordability within a short time window. Because of that, quite naturally the tendency is to lean towards a preference for a simpler budget impact or cost consequence analysis rather than full cost utility models, which would be typically required by, by nice. And as Evelyne points out, valuations for medical devices will happen through the ME process, cost per quality being necessary there, but this is going to be for certain high cost, high impact devices, though it's expected to introduce a substantial benefit to patients. And this is not the typical path for, for most medical devices thinking long term. I mean often the course we, it's a classic problem. We have, we have short trials and we need to think, we need to extrapolate into the medium to long term for a convincing take on, on the, on the longer term benefit of, of the, and the sustained benefit to some extent of, of the intervention we're talking about natural history data I think can potentially play a really important role there in helping us to define a model timeframe of at least five or even 10 years, or actually if, if we can, if, if possible further in spite of, as I say, what's likely to be limited clinical trial data. And I mean the key here with natural history is, is to of course observe how patients to fare over time without your device. Often naturally they're going to clinically worsen or incur higher cumulative costs. And we can create off the back of that median to long-term baseline projections of how our device in question is going to alter that trajectory probably by leveraging a series of scenario aligned conservative extrapolation assumptions. But really we need to be thinking about what, what, what yes data can we see already without the device? What, what is the normal trajectory of, of patient care and, and outcomes and, and therefore what are we likely to perhaps to see from our short term data, how's that likely to be persisted? How's is that likely to persist and extrapolate over time? And, and actually the effort in doing that, in doing so must be to ensure that the assumptions we're using are as absolutely transparent as possible, as well reasoned as possible that could well be leveraging precedent in the, the space where the medical device is looking to, which is looking to address essentially once we, of course, as we all know, we we're going beyond trial and we're looking into the future uncertainty just exponentially increases. We, we must make sure we, we carefully document and, and rationalise our extrapolations. But in the short term, as as coming back to that and, and it is largely a short term consideration with medical devices, local committees are, are going to want to see near term savings, tangible budget impact, but really key as more real world data is gathered over time. You know, be that from pilot programmes, registry data or as I've mentioned previously, external control arms, we can start to refine and strengthen the model for the longer term. In fact, what we'll see, not just in medical devices but across outside medical devices, actually there's, there's an increasing tendency and desire by decision makers and then lead modellers in response to that to have that iterative refinement and strengthening of modelling assumptions and, and, and data within as real world data is collected. And that's certainly no different within the medical device space. And I wonder, Juliet, if I could just touch on a small point on communicating device value. I mean when it comes to modelling, I'm a great believer in you can build an incredibly complex, sophisticated, impressive economic model for any intervention. But if it's a black box and it's very difficult to understand how you've justified or informed your model structure, the assumptions inherent within, across a patient movement within there the data you've collected, how you've collected that, how you are applying that data, the scenarios you've chosen and the impact of running those scenarios. If we're not clear about that, actually communicating that to a peer or a decision maker, is an almost hopeless and usually fruitless task. Visual simplicity is absolutely crucial in these models. Maximal transparency and really I think always coming back to the idea that we cannot make good with any economic estimation or extrapolation unless we can convince someone at the end of the day that this is something that can be repeated and readily understood. And that has to be inherent to every decision we make and, and the models we develop have to reflect that very, very much throughout every, every development phase, every every draught of that model, every draught of the concept deck to support that model development, it just mustn't be lost sight of that. Ultimately this has to be presented to real human beings. You have a lot of responsibility, they have a limited amount of time to understand the, the proposition you're putting to them. It has to be clear as day what you're arguing, the basis for that and what it means to them. Just a key tenant to bear in mind.- Yeah, for sure. So Hannah, from your perspective, what is important to consider when communicating cost effectness evidence with key stakeholders and do you have any examples where this has been successful?- Yeah, so I think Evelyn and Calum have just set this up perfectly to this question actually and about, you know, in the sense what what we mean in cost effectiveness here because we're not meaning this in the traditional cost effectiveness phrasing that we might use in Pharma, which might make you think of ices and qualities. And not to say that isn't possible in some scenarios of the, the technology that we're talking about, but as a general rule, we're not, we are not talking in that sense. We are more talking about return of investment and cost utility and kind of how's this going to impact my kind of budget impact models or my capacity models or, or those sorts of types of models where, you know, in essence the stakeholders that we've already alluded to and we've talked about, I think through all of these questions, you know, they want to see how quickly when they invest in a technology and in, and in a lot of cases it is an upfront investment on their side, you know, maybe that's through a tendering system or they're having to buy a device or they're entering a contract or, or adapting and, and investing in a piece of software. I know it's, some of the call may not be, there may be a physical asset that's that's a little bit closer to to Pharma type scenarios, but for the vast majority it is an upfront investment and so they do want to see how quickly that investment that they've made will be returned or how quickly the investment not just in the physical asset but in changing their practises, investing in a different service provision, for example, that this device slots into. And, and that's got to really be quite a core consideration because you know, it's a real challenge if you are talking around a decade or more before they are seeing any sort of tangible return, you know, most cannot foresee that foreign advance at a local level, you know, most want to see it in a year. Ideally they would like to see at least neutrality or savings if they're, if, if you can be blunt with them and if you really ask them and if they're being honest, you know, that's they, they are talking here and now they, they want it as quickly as it's possible to be, which if it's, you know, a short term intervention so it slots into maybe an operating procedure, maybe it's a prediagnostic for example, maybe you will see benefits very, very quickly'cause it's about maybe shortening operation time or recovery time or, or other aspects like that. If it's a device where somebody's going to be receiving it for quite a long time and they only slowly get incremental benefits and maybe they, you know, it's associated with a Pharmaceutical product and, and it means that their adherence is better for example, maybe they will only see the, the adverse events or the effects of not being adherence in 2, 3, 4 years time. So you're then talking maybe a population health type argument. So I think it's about understanding where you fit and therefore what are you trying to communicate about the financial investment and the effectiveness of your device on that. We have quite a lot of examples where we've supported people do this really successfully and even in situations where it's been quite actually a big change in how the service has ultimately provided. So one that springs to mind was actually for a diagnostic and that actually quite changed how services were going to be provided. It was opening up the provision to a lot more individuals. It was shaping where the patients could receive their diagnostic procedure and it was taking a lot of people out of the preexisting services that had a very, very long waiting list. And so even though it was quite disruptive in terms of the service and, and you know, potentially increase the frequency that people receiving diagnostic tests and, and all sorts of factors that would normally increase the cost burden. And they did because it could change the service provision to a more cost effective scenario. It was incredibly successful and they're seeing multi-market success in terms of visits adoption. Now when we were able to create quite complicated model actually that, that showed how the changes in the flows of how patients moved, it was simple and the user could get straight to, if I invest X in year one, this is what I'm going to see, this is what I'm going to see at year two. But underneath it they could also access all the complexity of how we drive that, the robustness and how they could critique it and really know that it, it was right for them, if that makes sense. So that's just one case. We have lots of other cases and I know we're nearly at time, but I guess it's just to say happy to sit down with anybody and discuss how we might be able to support them in their particular role.- Right. Thank you so much Hannah. I think that really nicely concludes our discussion for today. So I'd like to say a big thank you to Hannah, Calum and Evelyne for such a fascinating discussion today. And of course thank you to the audience for listening to our conversation and we'll make sure to follow up with any of the questions that we didn't get to cover today and we'll be happy to signpost anyone to the right place if they'd like to continue this conversation. So thank you again.