Episode #6: Real World Evidence has been saved
Episode #6: Real World Evidence
Life Sciences Connect
There has been an increase of access to data and different types of health data available. It's widely believed that Real World Data (RWD) has the ability to drive efficiency and business value. C-suite leaders in pharmaceutical companies increasingly view Real World Evidence (RWE) as strategically important. Equally a knowledge management system is a critical capability needed to maximise RWD.
In the sixth episode of Life Sciences Connect, we explore how best to implement knowledge management to maximise RWD, the role skills and talent has in maximising value from RWD and the challenges the industry faces in accessing such talent.
This episode is led by our host Karen Taylor. Karen is joined by Meri Petrovska, the Knowledge Management Director at AstraZeneca and Seb Burnett, the General Manager of ConvergeHEALTH by Deloitte Europe.
This episode explores:
- the impact of RWE on biopharma companies;
- implementing knowledge management systems to maximise Real World Data (RWD)
- the importance of change management
- the role of skills and talent in maximising value from RWD and the challenges the industry faces in accessing such talent
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Karen Taylor (00:00:03):
Welcome to “Life Sciences Connect”, Deloitte’s Podcast on the Life Sciences and Healthcare industry. This series features conversations with leaders from across the health ecosystem sharing their insights on the critical issues facing the industry today.
Karen Taylor (00:00:25):
Hi, my name is Karen Taylor and I lead Deloitte Center of Health Solutions, an independent research hub that supports Deloitte’s Healthcare and Life Sciences industry teams. One of the most fascinating aspects of my job is being able to meet industry leaders and gauge their views on the critical challenges that are affecting their businesses and to explore potential solutions to these challenges. In today's conversation, I'm joined by Meri Petrovska, from AstraZeneca and Seb Bernett, the general manager of ConvergeHEALTH by Deloitte Europe. Would you both like to take a moment to introduce yourselves, please? Meri maybe you could go first.
Meri Petrovska (00:00:57):
Thank you, Karen. My name is Meri Petrovska, I’m a knowledge management director at AstraZeneca. I work in the medical part of the business, and I'm also a product owner for the knowledge management platform. Prior to joining AstraZeneca. I was head of business intelligence for a specialized commissioning for Midlands and East, where I led the analytics teams across the region with special interest in data and drugs.
Karen Taylor (00:01:19):
Thanks. Meri, Seb would you like to introduce yourself?
Seb Burnett (00:01:22):
Hello, I’m general manager of ConvergeHEALTH in Europe and ConvergeHEALTH is a dedicated business unit within Deloitte that builds digital platforms for our healthcare and life sciences clients. I've spent my whole career in data analytics and digital, and I spend the last seven years supporting organizations maximize value from real-world data. So this is everything from helping drive end to end evidence strategies across an organization through to actually implementing the capabilities across the people, that data partnerships and the technology of the domains as well. Thank you.
Karen Taylor (00:01:59):
So as Seb has already mentioned, the focus today is on the role of real-world evidence in providing both insights into existing diagnostic and treatment pathways, helping us to identify unmet need and demonstrating the clinical and economic impact of interventions within the healthcare system. We've heard a lot about real world evidence. So Seb could, could you tell me what's all the buzz about why is it so important for biopharma?
Seb Burnett (00:02:24):
So real world data and real world evidence is something that's been actually around for decades, but there’s been an increase of access to data and lots of different types of health data. So not only clinical data, but genomic information as well and the improvements in technology is driving greater use within the life science organizations and now it's widely believed that real world data has ability to drive efficiencies and business value from how drugs are discovered, how they're developed, how they're commercialized, but also how they're reimbursed as well and we call this kind of the end to end evidence continuum. For that reason it is becoming a real strategic importance for and priority for more for biopharma, recognizing this kind of direction of travel with the last kind of three or four years. We've been running a benchmark survey to contract those trends and, and benchmark where organizations are where the priorities are and where some of the challenges is, last June we published our latest report and I'd just like to say that we actually run the survey before COVID hits as well. So just, just in terms of that, it's published in June. It was, um, it was, the surveys were conducted kind of January this year. We recognize kind of some important things going on. So I think the first one is we are still seeing a growing importance of real world evidence at a C suite within biopharma organizations and a hundred percent of the people that we surveyed believe that it will be important or very important to those C-suites organizations. The second thing is we're really seeing those kinds of priorities shift from traditional real adamant usage around kind of understanding disease of burden patients safety compared to the effectiveness and things like that. The next couple of years, we're starting to see top priorities for pharma actually to shift a new areas of value. So supporting regulatory filings and label extensions, as an example, um, augmenting clinical trials, the things like synthetic control arms as well, both in the R&D side and actually on the more commercial side, really helping inform design and then operationalize some of the value-based agreements, um, for some of these personalized drugs that come come along. With that in growing important and new priorities, as you can imagine, there's going to be Greg investments as well. Uh, and we kind of bucket these investments into three areas like the tech, the data and the people. And we're saying that pretty much, most of our, our survey participants are looking to invest in on all three. So I think a hundred percent recognized that they need to continue to evolve in technology. And this is a hugely in very fast evolving space in this, in this area, 94% are going to spend more on getting access to real world data and innovating around that era. So this could be around strategic partnerships and innovations to support things like instead that's a control arms I talked about, uh, and, and the majority eight tapes, and also want to increase the spending on internal resources and to do conduct more studies internally instead of continuing to outsource them to third parties as well. So we're starting to see kind of investments across the board to try and meet those chick imperatives of the C-suite and the organization, but an American review. Is there anything you wanted to add to that?
Meri Petrovska (00:05:41):
Thank you. So, yeah, I would agree with everything that you said from AstraZeneca's point of view as well. In addition to the fast development of technology capabilities, the growing population, a longer life expectancy, the environmental factors, and especially for us to TAs in which we are working in the speed in which the diseases in our patients are developing and especially the need to help our patients. We are realizing that, um, the soul has massive impact on the way that the pharmaceutical world is working and especially our organization. And there is the ever increasing need for new and improved ways of working. We can no longer do what we've been doing five years ago. Um, and the, all these, when you combine it with, competition in the pharmaceutical world, it means to us that we have to, uh, get our studies, delivered in a much faster way with less resources with less adjustments, um, than, than before. So the use of real-world data in R&D could be crucial in addressing some of these issues. So you already mentioned the synthetic controllers help with the initial site selection for studies based on the population with a particular disease. They're not part of the world, the understanding of the patient demographics, uh, creating, Tia strategy across the board rather than R&D working separately from, medical, discovering the effects on the drugs in populations, which have not been tested during development phases. It's all can be held by real-world data and evidence.
Karen Taylor (00:07:08):
Thanks, Meri. Our survey does suggest that the majority of biopharma companies do have an analytics platform in place, but the knowledge management is becoming even more, a critical capability. If these platforms are to maximize the value you can unlock from real world data. I understand they AZ recently implemented such a capability. In fact, as you said, your role is in knowledge management. So could you just talk through the value that this has delivered for the organization and the journey you took to get there?
Meri Petrovska (00:07:36):
Yeah, first of all, about the project, we did the thorough business analysis to establish the ways in which we can streamline and improve our process and the ways of working as a result of that, we realized that one of the capabilities that we need in AstraZeneca is a knowledge management system. Following a thorough, detailed and detailed RFP process. We decided to work with Deloitte to implement a slightly adjusted version of their data miner tool, this our decision, which I believe worked very well for both organizations. It was a feel-good project, which has had its own challenges, but you to the team spirit on both sides, uh, good project leadership, the dedication and the billing willingness of the whole team to succeed. We managed to overcome the challenges together into, to deliver on time and between our budgets. We were as a team, very proud of the success and coming from two different organizations. One could not tell the difference really, in terms of why did we do this? We wanted to improve the return on investment for the existing data investments and this was our primary goal. This meant that we wanted to think about the suitability of the available datasets within AstraZeneca. First, before we start purchasing new licenses, we also wanted to prevent duplicative spend. We wanted to be transparent and to share a view of what is already available to make sure that we won't be purchasing new data assets that already exist within AstraZeneca. We wanted to shift the mindset to a secondary first, and this became, this became the main buzzword secondary first mindset to drive faster and cheaper evidence generation. So we started thinking about novel ideas for filling evidence gaps to reduce the duplicative insight, and safe words, and to support, reproducibility wherever possible. We need to make the most of the existing work and to build upon and learn from it and not start each time from scratch. We also wanted to provide support in building TA specific, data strategies to drive value across the product life cycle and to support them to, and dividends generation processes in a new and novel ways so there's a result. We built the Kane platform, which we hope that will help with all of this item's
Karen Taylor (00:09:51):
Seb, obviously involved in this project. What for you were some of the key points along this journey?
Seb Burnett (00:09:59):
So I think as Meri said the mindset shift is a really important thing around driving kind of knowledge management. I think one of the challenges actually, I think with some parts of the organization, and this is generally organizations around the use of real world data and real world evidence is actually, especially in R and D actually taping many organizations. I've always been approaching the development process, clinic develop process in a certain way. And they're used to doing in that way and actually trying to change that mindset shift to try something new or something different that's innovative sometimes can be a bit of a challenge. So I think that providing and exposing the right knowledge, the right information and the right successes is kind of key to really bringing people along that journey. I think that's something that for me, is really important to success of any project. You can put the technology in place, but unless you do the legwork and the footwork around the change management, I think you're never going to get as much success as of those as possible. I know Meri spent a lot of time driving those conversations across the organization to try and kind of maximize the value that as an organization AstraZeneca got from their investment not just the investment in the platform itself, but also the investment around the data and the licenses going forward. As you can imagine, as more and more of this data becomes available in different guises, making sure that you're making the right strategic decisions around creating a data footprint around the therapeutic area to support the evidence generation across the life cycle, I think has become really, really important for biopharma going forward.
Karen Taylor (00:11:39):
Meri for you, what were some of the challenges that you haven't mentioned it or even mentioned, but you found that getting over that hurdle, if you'd known how, what to do at the end, what you've you knew from the end, what to do, what would you have done differently? Is there anything you would have done differently?
Meri Petrovska (00:11:53):
So I would say that the change management is one of the biggest challenges sold as a platform is a great resource. As of today, this is still not a system that a pharmaceutical company couldn't exist without. This means that we needed an excellent ongoing change management and marketing campaign, the standard ways of rolling out a new system where you do a training and everybody's using it because it's crucial to their work that was not sufficient. We had to think of new ways that are beyond the standard rollout training go from, implementing a new system. So creating positive user stories, introducing the relevant subject matter experts to various user groups, finding relevant examples of where the system can save us time, frustration and effort. These are all things that we had to think of. One other challenge is the adoption and closing the loop. So now we have the system and while people are realizing the potential of using such system, adopting it on daily basis is still a slow process. To help reinforce the usage of the system, especially to help in closing the loop, to bring back and share the outcome of the work we have to think even further. So we implemented a new standard operating procedure for secondary usage of data that pretty much mandates the uses the use of fabrics and the bringing back the evidence into the system. Then, partnerships finding alliances, finding, support us in all parts of the business and helping create cross teams and cross geography. Partnerships is absolutely crucial listening to the users, trying to help them, with various other bits and not only with accessing the platform, connecting with them, to connecting them with the right people, it's all vital for them to succeed. One last thing I think, um, everybody needs to start small. You don't want to be a victim of your own success. So one knowledge management platform cannot always cater for everyone's needs, and we need to differentiate between the various needs and to start small. We first implemented term the real world and evidence catalogue driven system to shoot medical, the medical organization first, which is reflected in the metadata and the language that we used on the platform. But we are currently working on a strategic trend to drive interconnected 10 knowledge management systems and data catalogues across the organization. We are thinking about the next steps, analytics, data, mining data, characterization tools, because of the interest. So you don't want everything to sit in one platform because very often it will not be suitable. So that is another and other challenge that we are trying to overcome in a different way. Of course the platform on its own has no value and if it's not properly adopted in news and we were very lucky to have very strong leadership and a great data science and health informatics, and epidemiology teams who helped us, in the development, in the rollout of the system and in supporting the secondary first, my mindset. They participate in the training sessions, they shared relevant examples. They advised on the data characteristics, they helped build on any previous work, they helped with the hands on work. So it's a joint effort and it needs buying from everybody and that is a huge challenge.
Karen Taylor (00:15:02):
Thanks very much. So over the past, nine to ten years or so, one of the things that I've really noticed changing is the importance and reliance on strategic partnerships. That's something we're seeing right across the industry and the partnerships involve multiple players across the whole ecosystem. So Seb, what is your take on the role of strategic partnerships?
Seb Burnett (00:15:25):
Seb: I think strategic partnerships can be critical for the future and the, and the realization of value of real world data going forward. One of the reasons why it is there are so many, and they're growing so quickly, it's because we're starting to get much more multimodal data and access to data through punches. When I say multimodal, I'm talking about the clinical data with genomic data, potentially linked with imaging data, and this really drives a lot of the value for biopharma, especially within the R&D domain. Actually, if you look at one of the big challenges around, we talked about R&D being one of the big areas for the future for R&D one the big challenges that we see within R&D is actually what they say is access to research grade data. So getting access to data that has the breadth and the depth required to be able to, drive the different value coming out of it. So we're starting to see those partnerships happen more and more frequently and get access to that kind of deeper data. I think actually in the future, we're going to start to see a shift in the way that we see getting access to data. So we used to see a lot of these collaborations with academic institutions, health systems, or et cetera. We're now starting to see a new model around kind of data marketplaces, pop up as well, that are starting to kind of curate this data and make it available, to biopharma for access. I think this is going to move into a new model in the future where potentially you can actually get it directly from patients but I do think that is in the future. The other thing that we're seeing that's really interesting as well is strategic partnerships. Traditionally we're based in the US because that's where it's easy to get access to data, but we're also starting to see biopharma start to look at other countries and build strategic partnerships there as well to kind of give better access to more global data. Actually from our survey, we're starting to see that China, Germany and Japan are actually, key countries for many biopharma and moving those partnerships beyond the United States. We know in China and Japan that regulators are also attempting to kind of expand the use of real world evidence in their review practice as well. So all this kind of work, these partnerships, the evolvement of how the regulatory agencies are thinking, I think can only be a great sign around how we can drive more value from real world data and health data in the future.
Karen Taylor (00:17:55):
One of the things that crops up, one of the barriers that crops up in all of our research is, equipping, staff with the skills and talent to embrace these new ways of working and this is a much more technical approach in some ways. How have you addressed the need to equip your staff with the skills and talent to optimize this?
Meri Petrovska (00:18:12):
So we needed a platform that will be, because it was an optional use and it's not a daily need. We need that a platform that will be really easy to navigate and to use where no new technical skills will be needed. This is why we chose the data miner, the Deloitte platform, because it works the same way that Google works. It works in the same way that Amazon one works. So we assume that everybody would be able to use it. It's so simple that you don't really need a new technical skills. So the platform itself is good fun to use even, and it's very easy to log on. It doesn't have any restrictions and it's very easy to read and find out the information. The more complicated technical skills, which come afterwards, if you want to start taxing the datasets and so on, this is where our data scientists come in and the platform is allowing everybody to ask for analytical help and higher technical skills, as well as just such the catalogue. So it's just that it's a little bit more than just a catalogue. For us, that was the right technical solution. Thank you
Karen Taylor 00:19:18:
Seb, would you like to add anything to that?
Seb Burnett (00:15:25):
Yeah, absolutely. So this is where I get quite passionate actually around the kind of capabilities needed to support biopharma because for me as an analytics guy, you know, data scientists and researchers, haven't had that kind of user experience that you get with some of the newer technologies, you're obviously coding and doing a bunch of things like that. So I think actually creating a really seamless user experience and collaboration layer that sits on top that allows you to find knowledge around data, around studies that were done before potentially about partnerships and other things, to then be able to inform how you most efficiently, drive an evidence need in a way that's sufficiently robust to get to your end decision is really important. What we're seeing more and more is that more and more internal investment in people means people want to do more studies internally, but there are only so many data scientists actually there are in the world. My colleague, Jeff Morgan did a panel recently where one of the participants actually said there just aren't enough data scientists in the world to meet the demand of all the biopharmas, and what they want to hire. So what we're starting to see now, and we've been driving heavily is around how do you want to move from that knowledge management layer to make it seamless and easy to use as Meri said, for all parties, but then how would you then embed that kind of seamlessness and that user accessibility to democratize the analysis layer as well and the reporting layer. So how do you then once you've decided what data you want to study on, how do you have a user who doesn't necessarily code can actually create a cohort and then run a report on applications, allow them to actually start to drive insights or drive evidence without having to throw that to the data scientists and that kind of end to end seamless experience, I think is becoming more and more important and actually just more and more needed and demanded by the users of today, as we start to kind of democratize and speed up that end to end process of you know, define your question to getting your evidence.
Karen Taylor 00:19:31:
Meri, what sort of feedback have you had from some of the users within your organization?
Meri Petrovska (00:18:12):
So I'm very glad to say that for now it's all positive farm. We expected the very small amount of people to be interested in using the platform. Initially, I mentioned we built it for the medical part of the organization, but with the wider interest in real world data and real world evidence we are seeing the numbers quadruple and more. These are users that are using it pretty much on a daily basis. So everybody thinks it's very easy to use the platform. It's not hard to find anything, it has lots of information which previously has not been there and lots of people on now know that if there is a new data sets available, that information will end up in their inbox. So they don't have to search or hope that they will find the information somewhere near the coffee machine, if you like. So it makes everything accessible, it makes everything transparent, people are finding subject matter experts. They're starting to build relationships across the board. They know who is doing what, so we are sharing literally everything in the company that goes on based on the real world data. Everybody's really happy about it.
Karen Taylor 00:22:49:
So quite a compelling return on investment. Just before we finish, is there one message, that you would like our listeners to take away who might be thinking of going on the same journey, is there just one lesson from both of you that you could share?
Meri Petrovska (00:18:12):
All I can say is that the sooner we realise the importance of real world data and evidence throughout the life cycle of products in the pharmaceutical industry the better, in fact in all industries data is becoming more and more important. Certainly the COVID pandemic has highlighted the fact that real world evidence and big data will become even more important in the new normal in the future.
Seb Burnett (00:15:25):
Thanks Meri, I think the recovery trial is a great example in recent months around how access to real world data and ongoing access for looking at patients in the future is a great example of how that can be used for kind of academic and research purposes. If there's one thing for me that I would kind of ask people to rethink about carefully when they go onto this journey it's around the people aspect of it, because I think that is something that is often underestimated. The change of mindsets as Meri talked about is something that takes time it doesn't happen overnight. I've seen many different organisations approach this kind of marketing PR change of mindset in different ways. So you really have to understand the culture of the organization to drive that forward but the one thing of all, when you do talk about that kind of change in mindset, it does seem to work it's around making sure you balance kind of quick wins that have demonstrable value that you can easily use to show value to the stakeholders across the organization, to gain momentum, to gain excitement, and to continue to deliver bits of value on an ongoing basis, because it's very easy for people to be naysayers and then trying to create that kind of wave of momentum we've seen been really successful in organizations, but it takes effort. It takes time, and it takes careful execution planning to do that.
Karen Taylor 00:24:57:
Thanks very much Seb and thank you Meri. That was a really fascinating discussion and it's so encouraging to hear about the success can be achieved using real world data and building these platforms. I'd like to thank you both for your time and thank our listeners for joining us to hear about the use of real world data.
I hope you’ll join us again for our next episode in the Life Sciences Connects podcast series.