Securing a Winning Strategy for Real-World Evidence

While the increasing importance of real-world evidence (RWE) is widely acknowledged, the dramatic shift required by biopharma companies to embed and secure an RWE capability effectively across the organization is still a work in progress

In Deloitte’s Second Annual Real-World Evidence (RWE) Benchmarking Survey, published earlier this year, the authors highlighted how the proliferation of healthcare data, advancing technology and analytics capabilities, and an increased regulatory/pricing focus on value showed how “the use and importance” of RWE in the life sciences industry had evolved in just 12 months. More specifically, this year’s report pointed to how “RWE initiatives are increasing at the executive level,” not just in regard to generating evidence, but also in supporting other research, corporate, and commercial objectives. Almost all (90%) of the survey’s respondents, Deloitte reported, “have either established or are currently investing in building RWE capability for use across the entire product life cycle,” with 70% building or increasing their internal RWE capabilities. “As a result,” the authors wrote, “RWE spending on talent and technology in the future is anticipated to increase.”

Culture shift

While the increasing use and importance of RWE is widely acknowledged, the shift required by biopharma companies to embed and secure an RWE capability effectively across the organization is still a work in progress. For Qin Ye, associate principal and global RWE lead at ZS Associates, in developing effective strategies, pharma companies still face a big hurdle “to overcome product-development-centric business models and be a lot more connected with the value they’re trying to bring to market and the problem they’re trying to solve for their stakeholders.” 

Qin Ye

Addressing this, he says, involves a fundamental culture shift. Companies tend to have a need to compartmentalize their functions to gain the necessary focus and efficiency, but that is at the expense of a more holistic approach. While pharma companies need talented teams with specialized training and knowledge, these highly skilled teams tend to base a lot of their decision-making on their experiences in the past. 

“It can be difficult to change that mindset to one that is more data-driven,” says Ye. 

In helping companies implement an effective RWE strategy, ZS sees “the change management of culture shift as the key priority.” A lot of pharma companies currently have a siloed approach to gaining access to and leveraging data. “Looking end to end,” says Ye, “the question should be, how do you leverage data to help you to make decisions and better position your product from the very beginning of the development process?”

This view is echoed by Saama Technologies, a clinical data integration platform company, who this year partnered with Informa Pharma Intelligence’s Citeline to bridge clinical trial information to RWE. Nekzad Shroff, Saama’s VP of field product management, told Pharm Exec, “The adoption of RWE by pharma leaders is a different problem from developing the actual capabilities involved. Adoption requires a shift to an exploratory mindset. There needs to be a tolerance of ambiguity and imperfect data to be able to actually interpret and gain insights in spite of data gaps.” He says that, with challenges and fears around data quality and security and the constraints of data sharing, “some companies would rather have siloed data than shared data.”

But Shroff believes the situation is slowly being overcome, as pharma organizations are building standards around how real-world data (RWD) is generated as well as interpreted. Similarly, the days when decision-making based on RWE “required an advanced understanding of mathematical statistical techniques as well as computer programming” are also changing, slowly. With increasing access to the “democratization” of these data sources and platforms providing actionable insight, Shroff says, companies “are now getting used to relying on these metrics to enhance their decision-making.”

Once that happens, this mindset starts to become pervasive and embedded into day-to-day business processes. And when that culture shift begins, “analytics tools can definitely help,” Shroff explains, “with IT playing a big part in providing a platform with the right set of capabilities, access, and security levels.”

New (and old) talent

Many large pharmas have identified the gaps in their real-world data and evidence capabilities and have the funding to fill them, bringing more data scientists into the talent pool. But, Ye warns, even when a company recruits talented data scientists, it can be difficult to leverage that talent, and equally difficult for the newcomers to gain an understanding of the business process. “It’s a merger of old and new, of two different scientific capabilities,” he says, “and that can be challenging if you don’t focus also on building the process transformation and enabling technologies. Just having new talent come into your team structure in a people-only approach will not solve the problems.”

Karim Damji, Saama Technologies’ senior vice president of marketing, has seen big pharma “running into talent issues.” For companies trying to expand their in-house data-analytics capabilities, there are questions around the talent associated with achieving this the right way, he says. “Pharma companies have been doing it the hard way for a very long time. The tools and technologies that are available to other industries have

Karim Damji

not been widely adopted by pharma, simply because of a fear of taking that legal risk associated with doing something novel.” 

In the meantime, Damji adds, companies liked Google, Apple, Facebook, Amazon, and Netflix have been “gobbling up all the talent that pharma needs in terms of expanding internally.” 

That may be changing, as some firms are making high-profile moves to draw that talent into the pharma realm. Ye welcomes this, as such recruits are used to “thinking outside the box, have lots of experience dealing with a wide variety of data, and transforming how that data can be leveraged.” What’s more, he says, when these “outsiders” bring with them a “very humble mentality to engage with and learn life sciences, it helps to create a more impactful collaboration.” 

However, says Shroff, for pharma, it is a question of solving the issue “non-traditionally.” Instead of pushing to hire more data scientists, Saama also looks at “the other side of the equation.” That is, “How can we enable the existing employees and executives of companies to start to make sense of some of this complexity without needing an advanced degree in data science?” He adds, “That’s not easy, because sometimes there’s a reluctance to take insights from a system that you don’t understand in and of itself.”

Data sources

While pharma grapples with these organizational questions, there is also the issue of navigating the myriad-and growing-data sources available for leverage. The industry should avoid simply “rushing in and buying data from the biggest data set in the market,” says Ye. 

“Companies should start from their business need and their questions. While they do need some baseline data for the area they are focused on, they should have a more comprehensive view of the things they want to transform, and from that they can decide what specific data sources are needed.”

For Damji, amid the myriad data sources, a key challenge for pharma is around longitudinal information and patient centricity. But it’s not just about looking at all the RWD sources and creating an intra-longitudinal view of the patient from a data source. “Creating inter-longitudinal views of the information is a bigger challenge,” Damji says. “And this is where I think modern aspects are being developed outside of pharma. Technology assets, to be precise, will significantly help mine that and stitch it together.” (See graphic; click to enlarge).

Although Shroff points to a current lack of standards covering the use of RWE, he sees more standards being created around the interchangeability and exchangeability of data sets as the space evolves.

Graphic: Gold Rush of Insights; click to enlarge

“Once we standardize how patient records are used for insight generation and how technologies like blockchain can store that kind of data in a secure way, we start to get more consistent around generation, use, and interpretation,” he says. Further FDA involvement will see data sets become much more streamlined, Shroff notes, “as opposed to right now, where companies are looking at anything they can get their hands on.”

As the rise of patient data continues apace, with more wearable devices continuously measuring the attributes that pharma companies are interested in, Shroff believes that companies will start to go straight to patients for an increasing number of data points, rather than going through the traditional trial route. “A lot of pharma’s questions are answerable through observational studies and through custom data sets that they’re able to create. So, there will need to be creativity on how to get to the patients and how to get patients to agree to share their data.” 

There is work already being done in this area. Shroff points to California, where, by 2020, patients will be able to consent to have their own data used for a specific purpose without actually going through a third-

Nekzad Shroff

party consent mechanism. Shroff predicts there will be a consolidation of the companies that provide this kind of data.

“As standards become more established, the large data providers will start to snap up the data assets wherever they exist, because providing the right data to the right people is going to be a business in itself,” he says. “We’ll see new industries looking at how that data can be monetized and used for competitive purposes. All of that will start to happen on a much grander scale than we’re seeing today.” While this will be a fragmented landscape that consolidates slowly, Shroff says that the winners and losers will be determined by the data’s ease of use and adaptability. 

Meanwhile, the winners and losers in pharma’s quest to embed meaningful RWE strategies will be determined by their preparedness to make these dramatic organizational shifts, requiring trust-building and change management, along with investments in data sources, partnerships, and technology. According to ZS’s Ye and Abhay Jha, principal and R&D technology lead, it drills down to establishing a cross-functional operating model that considers how different teams

Sidebar: 5 Steps to RWE Success; click to enlarge

leverage data, with a clear action plan to measure ROI, leadership support, and the appropriate level of technology to support a data-driven model (see sidebar). 

“Without those driving factors in place,” they say, “efforts can remain motionless, which is where many pharma companies find themselves today.” 

Julian Upton is Pharm Exec’s European and Online Editor. He can be reached at julian.upton@ubm.com