2056_REVO_Evidence_QA_blog_image (2)-479925-editedMany pharmaceutical companies have realized the benefits of moving from standalone real-world data (RWD) projects to full-fledged, cross-functional real-world evidence (RWE) capabilities to drive everything from their R&D programs to their commercial teams’ efforts to demonstrate value—and technology is central to their success. “Real-world evidence is essentially extracting intelligence out of real-world data, and now that we have the right technology, scalable cloud storage and tremendous processing power, we’re actually able to sift through big data to find those insights,” says Abhay Jha, a principal at ZS and the business technology lead in the firm’s R&D excellence practice.  

“Sift” is an appropriate term, given the analogy that Jha and his colleague Qin Ye, an associate principal at ZS who has helped multiple clients leverage RWD to address clinical development, HEOR, epidemiology and commercial decisions, use for pharma organizations’ RWE capabilities. They compare a well-designed RWE platform to a well-stocked kitchen, offering all of the “chefs” throughout a pharma organization the proper ingredients to innovate. Jha and Ye recently walked us through this analogy as we discussed the current state of RWE technology and what it takes to implement it successfully. Here are excerpts from our conversation.

Q: When it comes to RWE tech solutions, could both enterprise and individual functions’ needs be met at the same time? Is it possible for different teams with different needs to use the same technology at the center of a real-world evidence capability, or does that require different types of technology?

Qin Ye: If you set out with the right design philosophy, yes, it’s possible to build a platform that sits at the center to support everyone. You need to leverage the latest and greatest technology to handle big data at scale. You need to adopt common data standards and core capabilities to manage and curate data, and build with user-centric design.

A user-centric approach will help you see that there are common steps and tasks that all functions typically would undertake when they approach data, but then you also see their specific needs and workflows, the need for different insights. These groups do need different functionality. We built the foundation of our platform in a way that enables the enterprise-level capability to manage and curate the data collaboratively, and then we built function-specific applications that sit on top of that foundation.

Q: Where do companies need to be on the real-world evidence maturity journey before they’re ready to adopt an RWE technology solution?

QY: They need to have a strategic maturity, so a true RWE strategy. They need to think beyond just narrow functional needs. They need an ecosystem to foster collaboration.

Abhay Jha: And an alignment between various functional heads, and an understanding that real-world data is something that needs to be explored further. At the senior leadership level, there needs to be alignment on the value of collaborating and the value of data-based decision-making to transform the status quo.

QY: And everyone needs to understand the objectives. Do you need to incorporate the use of real-world data into your teams’ daily processes to help them make decisions? Are you happy with every single study or project taking four to five months to complete or do you need something that can scale? Do you want to build a capability to generate high-quality and high-volume evidence that engages healthcare stakeholders at scale? Understand exactly what you want to do with the capability to shape where you are going.

Q: Do they need to change their operating models?

AJ: Even though we believe in having a single platform at the center of a real-world evidence capability, we’re not suggesting that innovation itself be centralized. We’re suggesting that innovation be federated. In other words, each functional group needs to do their own innovation and needs to have the flexibility of innovation, while the data and common intellectual assets like cohort definitions, vocabulary mappings and algorithms are all centralized.

The platform provides the support for innovation. It’s like a kitchen that has all of the ingredients. That doesn’t mean that the kitchen owner is the only one who creates recipes or cooks food. Every person or group can be the cook and can cook innovative new dishes. The RWE capability’s job is to provide a fantastic kitchen, wherein everyone has access to the same rich ingredients, but they can use them in different ways to make different foods to their liking.


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Q: What are some criteria that organizations should consider if they want to implement a world-class technology “kitchen”?

AJ: Any technology that you use should have the flexibility to scale up in terms of both the different types of data and the volume of data that it could take in. You should think in terms of big data. You need to have a good security setup that allows you to control who gets access and to what degree, essentially creating guardrails. Then you need something that has collaboration built in. You need standardization around how you do a certain analysis, how you define clinical terminology.

These sorts of things allow you to collaborate and give you the flexibility to perform analytics across all functions. That is one of the fundamental things that you have to keep in mind: less duplication, more collaboration. Then, most importantly, you need the ability to analyze data quickly. You can’t afford to have your teams putting in too much effort and time to generate insights.

QY: Data scientists spend so much time preparing data; you don’t want to have any of that same effort repeated from scratch over and over again.

Q: You created your own platform, REVO Evidence, which suggests that you felt that you were filling a gap in the market. What is the current state of technology solutions for RWE?

QY: I would say that industry’s minds are already made up that RWE is important, but a lot of companies aren’t seeing the ROI yet. There’s the organizational models that need to be in place, which we already discussed, and most companies aren’t there yet, but also the technology underlying RWE is not totally there. We see a lot of other solutions in the market, but the majority of them are not designed with a holistic, cross-functional model in mind.

AJ: These products are focused on a narrow set of functionality. They’ve created technology solutions that can serve functions like medical affairs or HEOR. None are focused on solving the data-to-insight challenge across functions. Many of these on the market are well-designed products, but they encourage looking at RWD in silos. We’ve created a centralized data hub with a whole series of tools for analysis that are specific to various functions, so it supports that federated structure that we discussed. So yes, we did see a gap there. That’s why we filled it.  

Topics: RWE, RWD, R&D, data and analytics, technology innovation, Innovation