Enabling real-world evidence use cases through data analytics
We have previously discussed the benefits and business cases of real-world data (RWD) and real-world evidence (RWE). However, the key question is how can value be added to business using this “new” data? RWE has been around for some time, but recent advances in analytics enabled new use cases. It can provide understanding on how patient characteristics and behaviors affect health outcomes (helping to predict the progression of a disease, a patient’s responses to therapy, or the risk of adverse events) while also increasing the efficiency of R&D investments and accelerating time to market. For any company considering implementing advanced RWE analytics, its success will depend on using the right framework and capabilities.
The introduction of advanced analytics in RWE has made real-world data a more powerful resource for pharma companies. Unlike traditional RWE methods —which use descriptive analysis to characterize patients, and established matching techniques to compare patient groups with similar characteristics—advanced RWE analytics uses predictive models, machine learning, and unsupervised algorithms to extract deeper insights from rich data sets. These techniques can help uncover insights into drug performance, run accurate scenarios with predictive models, and generate hypotheses for different drug development phases. More examples in the image below.
What are companies doing?
Leading companies are increasing their investment in building big data pipelines, reusable analytical assets and models, new and improved analytical platforms, and ecosystems across business areas (image below). This approach allows companies to integrate multiple datasets, use them to build analytical models, and then “industrialize” the models to apply them in a broad range of contexts. Over time, companies can embed the models in user-friendly digital tools for a variety of stakeholders, both internal (R&D, market access, medical science experts, and so on) and external (healthcare professionals, payers, patients, and others).
Our insights on RWE capabilities setup
When investing in building capabilities in RWE analytics, five dimensions are emerging as particularly important.
Strategy: In digitally mature companies, business and R&D teams work together closely to identify the products and value-chain elements that can benefit most from RWE analytics and specify what value they want to create, how, and when. Having defined these goals, they maintain an intense focus on the products and development programs across R&D, regulatory, market access, and commercial activities.
Organization & processes: Companies set a global expert group to oversee RWE enterprise strategy, capability building, and governance. The group is provided with resources to invest in creating use cases with key business points and centralizing day-to-day RWE base-load activities. A new role is often created to work between RWE, key business areas, and R&D, charged with identifying opportunities, shaping a portfolio of work, and challenging business areas and functions to adopt innovative approaches. This role requires business knowledge (ideally gained inside the company), medical and analytical knowledge, communication skills, project management capabilities, and an entrepreneurial mindset.
Knowledge: Innovative AI approaches require dedicated expertise to produce reliable results. Operating knowledge will require skills not only from data scientists but also machine learning and data engineers. This team setup will enable delivery at scale of standardized, reusable data assets from multiple different sources and design factory-style platforms for handling automated evidence generation. To integrate medical, clinical, epidemiological, and business rigor into every process, it is imperative to leverage knowledge from subject matter experts that understand how RWE operates and delivers value.
Tools and environments: As a minimum requirement, companies that aspire to scale up their RWE analytics need a “sandbox” environment to conduct basic experiments with use cases and delivery models. More mature companies use scaled-up cloud platforms to build automated pipelines, repositories of analytical assets, and visualizations for use by multiple stakeholder groups. Some go further by building platforms that allow hundreds of analyses to be run across multiple patient outcomes and thousands of subpopulations. These evidence-generation engines deploy advanced and traditional RWE analytics side by side to derive insights into disease biology, real-world therapy usage, safety and effectiveness, drivers of healthcare professionals' choices, and other factors to support decision-making.
Data: Building a network of relationships with strategic data providers helps pharma companies securing privileged access to data and develop proprietary enriched data sets to answer business-critical questions. To do this, companies scan multiple market landscapes to identify emerging data generators and aggregators, develop a clear process for acquiring and accessing data, and adapt their enterprise governance to support collaboration. Some companies are starting to link RWE datasets with their data while reanalyzing their RCT data in parallel to build a comprehensive yet granular view of the effectiveness and safety of their therapies.
In combination with technology-enabled planning to integrate evidence generation across functions, these engines will transform RWE from a source of insights into a foundational pillar of corporate strategy and a key part of the value chain. Companies lacking this vision could struggle to compete with others that can base their decisions on richer insights generated at a fraction of the usual time and cost.
By taking full advantage of real-world evidence and advanced analytics, pharma companies can accelerate their transformation from product-focused to patient-focused organizations. Now it is time for the industry to set its sights on the next horizon of evidence-generation capabilities.
Please reach out to our Life Sciences Data & Analytics experts to find out more.
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*This article has been jointly written by Valter Caldeira and André Pombeiro