Sandra: Hello and welcome to the Rise of Integrated Data and Medical Device Webinar hosted my Medi Data Solutions with special guest speakers from Halloran Consulting Group and Shyft Analytics. All callers will be on mute. Please use the tap function to submit questions that will be addressed at the end of the event. This 60 minute webinar will attempt to provide the audience with an overview of the promise and challenges of the data age in medical devices. In addition, we will take a deeper look at specific segments of the industry that are impacted by the rise of integrated data. We will have five presenters, in total, speaking today. Our first presenter is Glen Devries, president of Medi Data Solutions. Following Glen are speakers from the Halloran Consulting Group will be Mark Vermet and Brendon Slagel. We will then hear from Josh Ransom from Shyft Analytics. The final speaker of the day will be Marco Vandoveran from Medi Data Solutions. Without further ado, I would like to introduce Glen.
Glen: Hi. This is Glen Devries. Thank you so much for joining us. I’m the president and co-founder of Medi Data and somebody who is, frankly, very interested in the world of medical devices. A lot of people look at Medi Data, and, in some circles, we’re known as a company related to drug development. But, actually, if you look at the way we try to improve what happens in drug development, a lot of what we bring to it comes at you from the world of devices and thinking in a much more agile and iterative way. And so, just to give you some perspective on how we think about the device market overall, and then, we’ll have some other speakers for you today to take you into deeper topics. So, next slide, please. I think it doesn’t matter what part of life sciences you’re in. It’s important to understand the world that we are moving into. We are living on a planet where we have an aging population that’s happening, whether you’re looking in Europe or the Americas or in Asia Pacific.
Glen: The growth in healthcare spend is going higher and higher. And I will paraphrase, actually, a presentation that I saw at another meeting where it’s actually a problem in physics. You cannot have this spend continue to rise as a percentage of global GDP and not have an issue. It physically will have to slow down. And I think, in a way, that’s the challenge that we are required to rise to, in the life sciences industry. And you see that that challenge is resulting in these growth rates, which are, I think, once again, relatively high compared to what you see as the healthcare growth, I think, in a very positive way. And I think there’s, actually, some very positive, potentially, trends that we’re looking at that affect our industry, when you kind of delve down one step.
Glen: So, let’s look at the medical device industry and some of the things that are changing. If you look at the business models and the regulatory models by which we are entering and managing the lifecycles for devices, they are changing. I think it’s probably more evolutionary on the regulatory side, and in the commercial side, the potential for really much more revolutionary change, and in terms of new business models and value based care. But these are exciting opportunities that I think the device industry has ahead of itself in terms of thinking about how we actually get treatments into the hands of patients who are waiting for them.
Glen: That doesn’t mean that these new opportunities always come easily and that they don’t have significant challenges. I think that the fact of the world is that we are looking at issues around market access and margin pressure that just did not exist before. That’s really the – the upshot of those challenges, those challenges of the physics, the economics of managing healthcare in new ways doesn’t meant that we are going to be facing tighter pricing pressure. And a bigger challenge to figure out how we get, as I said before, drug – devices to the patients who need them. I think that that is also part and parcel to the exciting world of precision medicine and making sure that we get the right device or drug or combination to the patient who is going to benefit from them at the right time. But that is, inherently, a complicated problem to solve.
Glen: The more specific and the more effective our devices get, I think this is more mathematics than physics, but the certainty is we are going to have an increasing challenge of finding of that person who is going to have that much more impactful therapeutic intervention based on that right device being given to them, at that time.And so, if you look at these challenges, again, I’m not trying to now bring only pessimism, I think this explains what is the potential for the middle. How do you balance these opportunities and these challenges? So, one thing, if you go to the next slide please, that I think is going to be absolutely critical in the world of medical devices is how people think about ecosystems. And this isn’t a medical device specific idea. This is not a life sciences or healthcare specific idea. We see this in industries and in consumer interactions all over the world. How we integrate specific products into the ecosystem of devices and digital experiences that are just part and parcel with the everyday lives of people around the world.
Glen: That is the way you begin to fix those problems related to market access and margin pressure that becomes inclusive, not just R&D but of the commercial aspects of going to market with a medical device. It’s also a way that we can start to solve for those ideas of precision because it gives us a much more pervasive set of data by which we can interact with people. And if you go to the next slide please, I really think that the way that we think about data is not just something that’s going to affect those commercial models, as I had in the slide before, around value based care. But it’s, certainly, something that is pervasive in the way we are going to be thinking about those evolving regulatory models. I just have a couple of references here for articles related to the progressively incremental thinking around how the risk based approach to clinical testing of medical devices – and this is an FDA example, but I think you can see how the rest of the world can and will move along.
Glen: Maybe not 100 percent in lock step but along with the trends that we’re seeing from the FDA, how those are evolving in a very transparent and I think positive way for the device industry. And then, how, from a regulatory perspective, again, once – this is something that the device industry is ahead of the molecule industry in life sciences, not just in regard to how they both think about incremental development, but how regulators are thinking about what constitutes evidence. You’re seeing a desire to pull in other kinds of data. Can we pull claim style data into the way we think about the safety, efficacy, and value of a device? Can we repurpose data that was, as another example, used or generated in very specific previous experimental contexts, but to make a statement about safety and efficacy in a new patient population is a very compelling environment to do that around pediatrics and how we look at ex pediatric data in a pediatric context to make a prediction about the risk benefits of devices. But the fact of the matter is that, as compelling as that is, that is an idea that is new and exciting and, once again, one where I think the world of devices is leading the way. If you go to the next slide please, these are just dimensions of what happens in this new world of big data.
Glen: Again, I think the device industry, as a whole, is quite good at dealing with data and incremental data. But without being at all accusatory like most sectors, life sciences, etc., has a lot of transformation coming. Now that we not only see this data as a stream that we can use to improve our risk benefits for devices themselves, but it actually transforms the way we think about everything related to patient identification, interactivity with physicians, managing the value of the care that’s provided with our devices. So, big data is a thing that kind of accelerates our ability to respond to all of those challenges and take advantage of our new opportunities. If you go to the next slide please, I’ll really give you a concrete example. And it’s an example I like using.
Glen: It’s a device. It’s not a medical device. But it’s a device that is in the consumer world. You have the dumb devices of the past, and a thermostat is a great one. It’s a device that innovates your home. But it’s really data entry only device. You say what temperature you want, and the thermostat market was revolutionized by the analytics approach. It’s inclusive of big data but also the math that goes around processing that data that companies like Nest started in the smart thermostat world. Instead of just entering data, the thermostat actually starts to predict what you would have entered and try to get ahead of when you are coming home and might want your house warmer or cooler. And I love this analogy for how the medical devices and how the practice environment of the future is going to work.
Glen: We are going to see patients themselves, certainly physicians, and I think, in a quite literal sense, the way the algorithms that power many of the medical devices that we’re talking about today evolve that they’ll start to become that much more predictive and not reactive. They will not wait for somebody to adjust the parameter to change the way they behave. Or they will react to the changing physiological or environmental state that the patient is in. and if you go to the next slide please, I think we can’t forget that part of this big data transformation, and part of what is going to fuel these smarter environments where devices are part of systems that are inclusive of systems, inclusive of people. So, the physician, maybe the patient, people around the patient, in more collective ways. But really, ecosystems.
Glen: Devices are going to be part of environments where sensors that are not actually embedded, necessarily, directly on a device but might be worn by a patient, might be carried by a patient, might be present in the patient’s home, present in the patient’s car, all of these sensors are really what are kind of the new both confounding set of variables and exciting source of beneficial information that can be incorporated into these ecosystems. It’s becoming so common to have medical sensors as part of people’s every day lives. I think you can even make the argument that they’re actually medical devices, in many ways. Probably ones with a low enough risk that they’re not going to necessarily go through the kind of testing that a stent is going to go through. But, actually, I think you can make the argument, actually, that even your phone, as I said before, is something that you can use for, if not always therapy, certainly diagnosis.
Glen: If you go to the next film, what you’re seeing now is a video of how the accelerometer in a phone can be used outside of a clinic as a diagnostic to look at, in this case, it was designed for looking at disease progression in rheumatoid arthritis. Not only, by the way, is this a diagnostic, if you hold your phone and hold it properly, but it will actually start to tell the patient, if they are doing the diagnostic incorrectly. So, this is, again, a really interesting way to think about these ecosystems of the future that I’m talking about. This is something that the patient can do with technology in their home that can inform them about things related to their disease progression. It can inform their physicians. And really interestingly, in this example, became, actually, an input for how we think about what an effective therapy is in a research context. It’s making some quantitative, subjective data part of something that used to be rather qualitative and objective. So, again, just hopefully some concrete examples to set the stage for today. If you go to the agenda slide, please, you see we’re going to have regulatory and commercial covered as well as what that means for actual research clinical studies themselves. So, with that, I’ll say thank you, again, for all of your time. Thank you to our presenters. And next up is Mark Vermet from the Halloran Consulting Group. And he is going to speak to you about regulatory process.
Mark: Thanks, Glen. This is Mark Vermet with Halloran Consulting. For those of you who are not familiar with us, we have the privilege of working with a lot of device companies, consulting with them both on process and regulatory, as well as the technology path.
Mark: So, before I turn it over to my colleague to talk about regulatory, I just want to reiterate some of the points that Glen made and show the audience what we’re actually seeing amongst our client community. Next slide, please. So, to reinforce a bit of what Glen was talking about, we’re seeing this more actively now in the device market. It perhaps may make some of the things we’re seeing with immunotherapy and some of the oncology. But the audience, for real time data, is really growing significantly. And a lot of the pressure to do this in device is to show outcomes prior to the device being released on the market. So, device companies now have to pay more attention to payers and to the providers. Mostly payers though because the device that’s going out on the market not only has to show the traditional efficacy and safety, but it also has to show a value against other competing products. So, Brendon and the rest of the presentation team are going to talk about this. But it adds a whole new dimension to study design. It adds a whole new dimension to how the devices themselves are deployed. And also, we’re seeing a lot of what we’re calling companion devices deployed. So, these are sensors. They could be Fit Bits. They could be other devices, including a phone where not only are you collecting information off of a smart device, but you’re collecting companion information that used to be collected with a patient diary or something like that to try and actually see if there’s a lifestyle improvement, or there’s compliance on the device itself.
Mark: So, if you could go to the next slide, please. What we’re seeing is that that move is requiring that we implement a – if you could – there you go. Sorry about that. A little bit of delay. So, what we’re seeing is that you really have to set up an ecosystem with a backbone on it. And this might be no new news. But gone is the traditional study model where you’re deploying a device to a select group of patients. You’re collecting the information on it. and you’re evaluating how well the device works and whether something needs to be changed before it gets released out onto the market. This study process and all of that is being sped up. But it’s also becoming a lot more collaborative. So, and in the center, the collaboration is the technology and the access across a pretty broad audience. So, it’s not just about bolting all of the technology together. It’s about producing interfaces and outputs with the data that are relevant to the specific reviewers.
Mark: So, it could be something very simple that you’re trying to guide a patient for, in terms of improving outcomes or compliance. But it could be very complex, in terms of working with the payer providers in terms of showing overall cost of the item, and its deployment, while you’re going through the study. Next slide, please. What this is driving, which is unglamorous and often not noticed amongst the audience is the whole need for management in getting on the same page as far as data dictionary goes. So, Glen was absolutely right with the amount of sensors, both direct and indirect, that are coming in to devices. We’re working with device companies that are changing their sensor technology on the device itself and putting together programs where that sensor technology is standardized.
Mark: A lot of devices were deployed out in the field before there were standards. And so, those are getting reviewed. But all of this streaming data coming in, obviously, needs to be normalized, so that the visualizations are accurate and that the data coming from multiple sensors looks and feels relevant, when you’re putting it into a dashboard. So, as I pass this off to my colleague, really, next slide, please, the moral of the story, what we’re seeing, in summary, out of the industry is that all of these things are increasing pressure from a technology perspective of device companies. We’re seeing mergers and acquisitions. And standardization and master data management are becoming a requirement. It’s not longer an option. You need to establish an architecture that supports what you’re doing now, plus, when you’re doing device clinical studies, as well as device design, you need to fit into an architecture that’s common. And then, everybody wants to look at the same data with shared meaning. But they’re probably going to want to visualize it differently. So, my colleagues who are presenting here coming up will go through a little more detail on that. So, with that, I’m going to pass it off to Brendon. He’s going to talk about the regulatory aspects of what we’re seeing.
Brendon: Thanks, Mark. So, I’m going to shift gears a little bit and talk about how regulations have been evolving alongside the medical device industry as a whole. So, this will really underscore, I think, some of what Glen said as well as Mark. In the recent past, an enormous amount of energy was spent on generating data to meet specific needs. Whether that’s a silo of safety and performance, marketing claims, or reimbursement, it was really driven as a reaction to a given need. So, demonstrating safety and performance, typically, relied heavily on clinical data in the pre-market setting. So, conducting a pilot study in 30 or so patients followed by a pivotal study to show really safety and performance. And if your device was substantially equivalent to already marketed device, you might be able to skip ahead a little bit and not have to repeat a lot of the clinical testing that has already been done. And once you got a device on the market, you might find that you have additional marketing claims that are unsupported. And to be able to support those claims, you need to conduct a post market study, generate white papers, or use other very resource intensive processes to generate and gather that data.
Brendon: So, meanwhile, your health economics and reimbursement stakeholders would typically be reviewing registries and payer databases to make the case for reimbursement. But gathering sufficient data from these could prove to be challenging due to the limited specificity and procedure coding. So, where you might be able to compare Procedure A to Procedure B, you can’t really make that same comparison at the device level. And this is often the closest to real world data as you could get. And this may sound familiar to some people as something that resembles what they’re currently doing, but this mentality of constantly working to generate new data really needs to shift because it results in a lot of inefficiency. Go to the next slide, please.
Brendon: So, as an example, just to return to the clinical study example, traditional paper based pivotal IDE studies that might enroll a few hundred patients at a dozen or so sites is a pretty standard thing. I think many people will remember the days of carbon copied paper CRFs where you would have binders and binders of just paper forms where data would be entered. You would have a monitor from the sponsor come and review it, collect it, and then, go through months of data cleaning and query resolution. A lot of these CRFs would be customized to a given study. There weren’t many data standards. And a lot of that data was really specifically useful to the end point of that study.
Brendon: The manual entry part of it and the query resolution part of it resulted in hundreds to thousands of man hours to lock a database. And then, once you had the data analyzed, being able to compare it to other studies and other data sets could prove to be challenging, if not impossible, because it was an apples to oranges comparison. Another huge issue that I think Mark touched on is that data would only be collected in the clinic at regularly scheduled study visits on paper forms. So, you’d have patients coming in where you’re filling out quality of life questionnaires, things like that. And, again, just capturing data on paper and then, entering that into a database. On top of that, the paper based medical device reports and regulatory submissions using mail or fax were also hugely inefficient.
Brendon: And, again, this focus was really on generating and gathering data to meet a specific need. Next slide, please. So, this focus on pre-market data and lack of robust confirmatory data in the post market space, eventually, caught up with the industry. And I think we all know of a few high profile cases like the metal on metal hips and PIP breast implant scandal that have really forced regulators to shift their stance. The days of relying solely on equivalents to a market device to gain approval are now behind us. In fact, this past you may have published the final versions of the new medical device regulation and invitro diagnostic regulations that are going to completely replace the well-established and understood directives.And among the changes to these are increased clinical data requirements, not only in the pre-market space but also the post market space throughout the device lifecycle. Specifically, in Europe, using equivalents based on another manufacturer’s device is now almost completely out of the question. And even legacy devices that are already on the market will need to have sufficient clinical data to maintain their CE marks, in a few years, once a transition period is over. And in the US, while the 510K process is still alive and well, there’s definitely a trend towards requiring additional clinical data. An example of that is looking at AEDs where a number of different products hit the market based on substantial equivalents. Then, a number of malfunctions resulted in recalls and safety concerns.
Brendon: FDA came back and said all AEDs need to have clinical data to support them. They’re now Class 3 devices. And one of their justifications for that was that they said they assume most manufacturers have sufficient data, but they don’t need to conduct studies. So, the reality is now regulators are expecting companies to be gathering data. And it’s no longer something that’s at all gray. So, while all of this is happening, devices are rapidly evolving. And data is becoming more and more prevalent. Next slide, please. So, data is now everywhere. And paper is almost entirely a thing of the past. The focus has shifted now from figuring out how to generate and gather data to figuring out how to make use of an abundance of data. Software is now included in everything from watches to medical devices and even home appliances like coffee makers or, as Glen said, thermostats, which is pretty insane. And, fortunately, as all of this has taken place and this evolution has occurred. There have been some groups that have been looking at how to standardize the data, so that everyone can look at the same thing. So, C Disk and Fuse are two that come to mind there. So, there’s now more data at device manufacturers’ fingertips than they know what to do with. And instead of requiring clinical visits, to return to the clinical study example to gather data, it’s now possible to get direct real time data from enabled devices, general wellness apps, things like that. And it’s also possible for patients to get that feedback, as well as provide data based on quality of life, other things like that, to meet study end points from the comfort of their home, the grocery store, or wherever they happen to be.
Brendon: So, with data becoming so abundant, regulators are now shifting to try to keep up with it. Things like unique device identifiers, electronic safety reporting, and mandatory results posting on clinicaltrials.gov have resulted in an enormous body of data that can be accessed by multiple stakeholders. So, now, these additional regulatory requirements may seem like a burden to some. There’s also a huge opportunity for leveraging the same data for commercial purposes. Next slide, please. So, here, I think some manufacturers may be up in arms and outraged about the new regulatory expectations for data collection. But others that are a bit more savvy are thinking more strategically about how to use that data to their advantage. Again, looking at unique device identifiers, with those being added to implants and a number of other devices, there’s a huge opportunity to leverage that information for comparative effectiveness research, reimbursement, and post market surveillance. So, where ICD 9 or 10 codes have allowed for some comparative effectiveness research to be formed on the past, it’s been limited by the inability to look at the device level. Now, with UDIs, it’s possible to make real world comparative use comparisons at the device level. So, looking at one spinal disk implant versus another or one pace maker versus another, rather than just looking at the procedure level. So, here, the key to success is figuring out not only what data must be collected for regulatory purposes, but how else it can be used to support the device commercially. And really looking at that and trying to map those things out ahead of time will pay dividends for manufacturers going forward. So, with that, I’ll turn this over to Josh Ransom from Shyft Analytics.
Josh: Thanks, Brendon. So, next slide, please. I’ll give you a little background on Shyft. I’m the head of clinical products here at Shyft focusing on the real world evidence and big data analysis space specifically for Shyft. Shyft is a software to service cloud provider of insights and data analytics for both pharma and other life sciences companies. So, I’m going to be talking a bit more about real world evidence, real world data. And I think it’s important to start off by defining what that is because there are multiple definitions. So, when I’m saying RWE, RWD, I’m being a little bit more inclusive of both claims, electronic medical records. And I think most germane to today for my talk piece is about registries. So, obviously, for at least the last two decades, we’ve been collecting a lot of information in these registries around individual products, medical devices. But there was a bit of a seat change, if you will, back around 2009 where we really started seeing in the marketplace different ways of capturing the data, trying to get much more granular with the real world evidence. And we’re seeing, increasingly, as Brendon alluded to, with the UDI, we’re seeing a change now even more deeply into trying to capture the individual products that are being used.
Josh: In fact, I’d say it’s such a level of seat change that it’s going to allow the medical device community to leapfrog over what pharma and other life sciences companies are going to be able to do with their types of RWE analysis. So, with the ONC HIT requirement for EMRs to actually support UDI for the talks, open conversations by the insurance claims providers that they are going to be capturing the UDI as part of their ongoing data coverage. And the way that multiple registries are out there trying to pilot and figure out how best to incorporate UDI into their ongoing patient level analysis, it’s really getting us to a point where what we’d like to say is we’re nearing a tipping point. We’re not quite there yet where we’re going to have the real world evidence, the data to really do everything we want to.
Josh: We’re in the pilot phase right now. But we’re getting really close. And it’s going to be really exciting, from our perspective, about everything that’s going to be possible. As we’ve said, compared to effectiveness research, down at the level of individual products, being able to self control four different versions of the same product line, being able to differentiate competitively between other brands and be able to truly talk about value, outcomes, adverse events to focus in on the conversations that payers and physicians want to talk about, i.e., value and outcomes that my product is better than the competition. And while there is, as Brendon also alluded to, a major need for new capabilities in order to conduct this type of analysis, it’s our perspective here at Shyft that companies who truly focus on RWE and are tackling this appropriately are going to be able to develop a sustainable competitive advantage over their competition and be able to win decisively, when it comes to being able to talk about value to the point that we think that there is, actually, commercially, billions of dollars on the line, when it comes to really utilizing real world evidence appropriately.
Josh: Now, that’s not to, somehow, gloss over, put on rose colored glasses when it comes to RWE. Next slide, please. There are significant challenges that we are very well aware of. Despite the great strides we’ve been making, being able to capture all of that value, we are sitting on a current relative lack of data. As I said, there’s a tipping point. We’re getting close to that data really flooding in. But, historically, we aren’t there yet. And, not only is it a lack of data, but there’s a lack of data standards. It’s a bit of a wild west when it comes to the real world data. And, unfortunately, it’s still the world of garbage in/garbage out, when it comes to your analysis. Now, combine that with because this is a new approach, at least from our perspective, what we’re seeing in the market places, there’s also a lack of capabilities, when it comes to being able to effectively do this type of analysis at scale. Not just for medical device companies. I’m not pointing the finger. It’s across all of life sciences that’s facing this struggle. And then, combine that with, on the data supply side, there’s also the demand side. You’re seeing the waxing powers of payers, as was mentioned, about the requirements for re-evaluation, about the need to focus on value and value based contracting, seeing the over stressed providers and their push back on access to being able to discuss the relative value.
Josh: But, again, this is an area where we believe that real world evidence has a strong place in overcoming these challenges. We’ve seen, from the world of personalized interventions, as Glen mentioned, talking about personalized interventions, not just personalized medicines, but personalized interventions and devices and use that we’ve seen from that world that both payers and providers are more open to engaging when the conversation is about value, about a new type of information that they are not familiar with and when there is an openness, a transparency, and a focus on value to those conversations. So, while there are challenges, we think that there’s, actually, some clear pathways to being able to capture that and change the conversations successfully.
Josh: So, a couple of examples teeing that up. So, about 2009, there were previous registries already going on, your Glory registry and Medi Cal that pre-dated the UDI discussions where they were trying to get to a different level of information and analysis about individual products that it’s still providing very interesting insights. But it was, I think, around the time of 2010, with the Mercy pilot, with FDA coming out and actually supporting that pilot that we’re seeing a real change in why we were sent talking about that tipping point to the new data. Where this first major pilot approved and funded by the FDA is now leaning towards a much deeper analysis at that UDI level for looking at the comparative effectiveness research between medical devices. Next slide. What that looks like though, in practice, when we’re talking about observational research and doing that deep, deep level of comparative effectiveness research, it’s being able to both dive in deep, look at the outcomes that you would use as you’re trying to design your clinical trial, your outcomes of pain, walking time, PROs. It’s looking at those same levels of complications, fractures, DVT, cardiac events. But having to do so in a slightly different way from what we would historically do within the realm of an RCT.
Josh: And having to utilize different methods for control, to do fully block experiments using methods like high dimensional propensity score analysis to control for those confounding factors like disease, severity, and incompetent interventions. But being able to do that and doing so across potentially, at least in some of these data sets that are out there, at a minimum, hundreds of thousands of patients, if not hundreds of millions of patients. And I don’t say that facetiously. They truly are data sets with hundreds of millions of patient lives that one can track longitudinally. And starting to see that there is actual information down to the level of UDI and some of those data sets that will allow us to do this type of high dimensional propensity score analysis supported successfully. So, next slide. So, I’m not going to belabor the points.
Josh: I do want to be able to get it over to Marco. But we did want to lay out what those – what we see as the pathway to fully recognizing the value from this integrated data source of real world data. And then, in the near term, if not already doing this, it means truly defined clearly, spelling out, and defining both the outcomes and attributes you are looking to analyze. Similar to just taking a data science approach to it. Very clearly defining your data acquisition strategy and being holistic in that view. Don’t be penny wise, pound foolish. Tackle trying to get the data set that will allow you to do the entire breadth of observational research that you need to support that value dossier development and the messaging strategy for the physicians and providers you need to speak with. As you’re doing that, making sure that you’re also performing a full GAP analysis of the data strategy against your current HEOR capabilities, confirming that you have that necessary capability in house or have at least lined up the approach, the vendors, the technologies that can support the observational studies and diving into the real world data.
Josh: And ensuring that you are truly part of the pilot. You’re part of the conversation. You’re going to own that conversation and being part of the UDI collection ecosystem. That you’re not going to just be sitting back waiting for the information to come. But ensure that you’re going to own the conversation. Now, in the midterm, branching off from that, laying the foundational ground work, really ensuring that you’ve got the necessary capabilities in house, center of excellence set up to be able to conduct these comparative analytics practices appropriately. Ensuring you’re doing so in a way that can be supported and can be scaled and can be done transparently through tracking, auditing, being able to really openly have the conversations, as you’re doing these value estimations and analyses for the conversations you’re going to end up having with payers and providers. And you want to be doing that transparently, so that they are going to end up trusting you. This is an, ultimately, talking about value means being open with the partners, with the stakeholders you’re going to have to be engaging with.
Josh: But then, leading to that long term, it does mean getting to that place we’ve all be wanting to get to, the “value based pricing nirvana” and building off of these previous steps to get to the conversations with payers where you’re actually having the frameworks and the technology systems set up to do true evidence based, value based contracting with the long term repeatable evidence collection that’s scalable, in order to be able to truly have and track the value you are providing. In other words to give, as the payers are going to say to you, to give the – to ensure they are getting the value that you promised them. And in what we’ve seen is that this can be a win/win situation, a win/win/win for all parties, if done appropriately. So, with that, I will turn it over to Marco for him, so that he can talk more about the clinical trial realm.
Marco: Wonderful. Thank you, Josh. Thank you, everybody, for joining us here today. I will try to focus here in the next few slides with a view towards the clinical trial realm leveraging what was said before with Mark, Brendon, as well as with Josh. If we looked at the next slide, you heard the broader overview, Glen starting to talk about the challenges and opportunities. And one thing that I think we will agree to is that the pace and the acceleration of capabilities in software and technology is rapidly advancing. So, are the things that we’re talking about, things that are really playing in the future? And I think the answer is no. This is technology. These are capabilities. This is a view. This company is to conduct clinical research. We can start to do today. These are not things that are happening years out. These are capabilities that can be leveraged and approached in today’s world. Go ahead and look at the next slide. Again, this is sort of the MediData view in how we think really about data integration in the context of a true platform.
Marco: There are many components that were brought up, whether it’s a clinical regulatory strategy into sort of commercial and reimbursements, the payer sector that will play into how you are looking to get your products to market. In the data, underlying to all of that, is surrounding the view around clinical data, operational, and financial data that, with all of those aspects of the platforms are being brought together in the capabilities, at least from a Medi Data perspective that we can bring together. So, whether it’s in the planning, leveraging benchmark, using data on financial or design of studies that will help design and focus on better studies. If the interactions with sites around their contracting time and activity and the monitoring interactions were data, it was becoming more important to the upfront assessments and planning but optimizes the relationships that you have and can have with the sites.
Marco: So, positive effects on enrollment, positive effects on the relations with the key opinion leaders associated with different areas that you operate in. Study conduct, I’ll talk about it in a minute because that, really, from a data integration perspective, is part of the central hub where a lot of this data is coming in. But new areas around the patient and the importance of how the patient is going to play into capturing the data and leveraging the data into your assessment and analysis in keeping the patients engaged in that context is becoming more and more important. And then, of course, all of that gets leveraged with much better tools around data, analytics, visualization, benchmarks. I’ll talk about it here in a minute.
Marco: So, if we think in the context of big data, on the next slide, companies really have to think what is appropriate in the context of their particular products and studies. We talk about sort of big data as a general term. Big data is really the culmination of all different types of data that is being brought together. So, if you’re thinking about your more traditional EDC, the site collected data associated with entry in platform systems, laboratory data associated with better conduct of the studies that are being done, image data, very important in medical device studies, bringing capabilities around assessments of images in context of the conduct of clinical studies. Operational and financial data, as I just mentioned briefly, around sites, and the interactions, comparative information that would allow the use of the bigger data sets to become available to compare against. And then, the digital patient data, again, some examples we were given earlier, it’s a huge area of growth, in many ways, where companies are looking to integrate completely different type of data source streaming into the platform. It will start to allow very rich operation set of data when you conduct other clinical studies. And Josh, in the context of real world data, think about different sources of health records, pair data, reimbursement data associated with your clinical data becomes a really interesting concept. And the capabilities exist in today’s world. But you will have to determine the approach to applicability to your particular clinical study.
Marco: On the next slide, let’s focus a little bit more on what it means and how we can leverage and the tools that become available associated with sort of the big data. And it’s a garden model that effectively speaks about big data as being foundation of collecting some components associated with where we think about the variety of the different data, different types, different sources, different criteria of how data is being collected, and the sources where they come from. The velocity of data is very different, if you think about the streaming data associated with patient reported sensors, for instance. This is a very different type of data accumulation in this big data source compared to, let’s say, side entry data in a more traditional way. And then, the volumes of data, of course, are growing exponentially.
Marco: How and in which way you can help manage, assess, and make the data available to various stakeholders in the clinical studies is really an important part. So, on top of this sort of base layer of the big data are sort of two components. It would be how do you use and how do you analyze the data. What type of tools are available to analyze the data and make it visual to the different groups and the different types of assessments and analysis that we would like to do? And benchmarking is a really big, important part more and more in many of the discussions that we have with many of the different device companies is around some level of operational, financial, and clinical comparability to the extent that very good data is available to that. And, on the right hand side, you see the data science.
Marco: It’s really about machine learning and predictive analysis and also the example that Glen briefly mentioned in the context of consumer market is starting to really occur in the life sciences and the medical device companies. It’s the software technology capabilities that really start to think differently with how the larger data sources and the data sets are being looked at, and the software technology capabilities will allow us to much better source that data and make better decisions into the future. So, just to wrap it up on the next slide, I guess our view around data integration, within the clinical context is that the thinking in the industry has really shifted towards the full focus on platform, software as a service thinking. No longer are there disparate systems that somehow can extract data that are being brought together.
Marco: The thinking has moved into a true sort of platform with the capability of integrating different sources of data and different geographies, different type of influence associated with how and in which way you collect your data. I think it also helps facilitate, which is a really important part. It sometimes does get missed somewhat over another’s values and perspective and whether these are the sites of the clinicians that you work with, or for organizations. It is into the platform that gives them access to the various capabilities and functionality areas that makes the use in the leverage of the data underlying to all of it much easier. And all of it is intended that, with the platform focus, we can help support the medical device companies to drive efficiencies, effectively minimize the risk as they conduct their studies, in order to get products approved into the marketplace and, of course, save time. So, it is about data access. It’s about data acquisition. And, importantly, on top of that, is how we leverage the data, not just a collection of the data, but much more in the decision making capabilities beyond just a pure, clinical perspective. So, with that, I’m going to hand it back to our operator, Sandra. We do have a few more minutes for questions, I’m assuming. So, Sandra, please.
Sandra: Thank you, Mr. Vandoveran. That concludes the presentation portion of the webinar. We will now open up Q&A. Please remember to use the chat function to submit your questions. I would like to hand over the call to Namesh Patel from Medi Data to moderate this session.
Namesh: Thank you, Sandra. With only five minutes left, we’ll take a few questions. But we’ll make sure we follow up in submissions what we weren’t able to address today. So, the first question we have is for Josh from Shyft. Josh, which devices are therapeutic areas we have most impacted by the rise in real world data?
Josh: Great question. Well, this is my perspective. Maybe Shyft and I’m sure others might disagree with me. I think I would come back to – where I come back to is looking at the global burden of disease study out of Washington. So, I think your – in the cardiovascular realm, and in the orthopedics realm, so they’re both, from a spend perspective and a quality of adjusted life here perspective both the biggest areas. So, stents, pace makers, your hip and knee replacements of orthopedics, I think all of those are huge areas ripe for being able to do comparative effectiveness research, being able to differentiate between products. Those are the realms I would start with because there’s also a long history there. But I’d love to hear the other panelists’ thoughts on it as well.
Namesh: Any other feedback from the panelists?
Mark: Yeah. This is Mark Vermet. I concur with what Josh just said. I think what you’re going to see is a focus on long term issues in the device world, just as we’re seeing now with chronic illness in the pharmaceutical biotech world. I think if you can think about the long term healthcare problems, that’s what you’re going to see the device targets for and the ones that are probably going to offer the highest ROI in this particular area that we’re looking at today.
Namesh: Thank you, Mark and Josh. One final question, given the time we have. This is kind of a broader question. I’ll start with Brendon though. What are some steps medical device manufacturers can take to start using regulatory data more efficiently?
Brendon: Great question. I think one of the biggest things is to really start looking at data from a much higher 30,000 foot view throughout the device lifecycle. So, instead of focusing on what it’s going to take to get a device to market and really specific pieces of data that you know you will need, really looking at the device lifecycle and figuring out not only the data that you need to generate or collect for regulatory purposes to support the safety and performance of the device. But also, what other data is available and trying to proactively map out ways to use that. So, looking at whether it’s to support claims, whether it’s just for manuscript submissions and things like that to build a larger body of evidence, so that you can really maximize the impact of the resources that are spent to generate that stuff.
Namesh: Thank you. That’s all the time for questions we have this afternoon. I’ll hand it back to the operator.
Sandra: That concludes the Rise of Integrated Data and Medical Device Webinar. I would like to thank our presenters for their time and insightful presentations. For the audience, further information will be providing contact details for our speakers, as well as the recording for the presentations. Thank you.