Life sciences organizations have access to more data than ever before. When they leverage that existing data during their research and development (R&D) process, organizations can drive more efficient drug development.
Innovation and regulatory approval rely on real-world data (RWD) because it answers questions clinical trials can’t answer. RWD also helps:
- Identify key similarities across a selection of drugs (including side effects).
- Apply medical reasoning to both new and existing findings.
- Formulate hypotheses from patterns.
The positive impact of life sciences on patient safety will only get stronger as organizations use existing data sources like RWD to decrease time spent in the R&D stage and bring drugs to market faster.
Importance of RWD
Enabling RWD-driven drug development accelerates the approval of more effective and affordable treatments. This approach successfully identifies patient patterns and density indicators and maps causal pathways.
RWD bridges the gap between research and practice. Drug developers study how a broad group of patients uses and responds to a drug to understand how patient characteristics and behaviors affect health outcomes — data that informs decisions for care. RWD also empowers organizations to design and conduct better studies and clinical trials, which don’t necessarily represent real-world settings.
Drug developers share and analyze RWD, applying the results of their analyses to product development and approval. This collaboration between drug developers requires a data fabric architecture to help life sciences firms overcome data silos, friction, and noise to maximize available RWD.
Sources of RWD
Drug developers collect RWD from various sources to contribute to the robustness of real-world evidence (RWE) already on hand.
- Claims and billing activities: Information about healthcare services, patient prescribing patterns, and population coverage.
- Product and disease registries: Collections of data defined by a particular disease, condition, or exposure.
- Patient-generated data: Data gathered directly from a patient through wearables and mobile devices to provide real-time updates to medical teams on health statuses.
- Electronic health records: Digital versions of patient charts with information about medical history, diagnoses, treatment plans, allergies, immunization dates, and more.
- Social media: Posts by patients on social media platforms or forums featuring unsolicited, first-hand data about a drug or treatment. Social media allows organizations to listen to patient voices in real-time.
Drug developers can use RWD insights to create more comprehensive hypotheses and investigate clinical research questions at a more granular level. Increasing regulatory success also enables life sciences organizations to reduce the financial risks associated with R&D programs.
Adoption of RWD
Historically, the life sciences industry has relied on clinical trials and commoditized data sources to inform the drug development process. However, regulators and life sciences organizations have begun recognizing the value of RWD sources. The FDA uses RWD and real-world evidence (RWE) to monitor adverse events and postmarket safety to make regulatory decisions, and our data suggests that 32% of life sciences organizations connect with RWD to drive drug development. Because of this adoption, the RWD and RWE market is expected to be worth $2.3 billion by 2026.
Midsize to enterprise organizations use RWD the most because their internal processes are more mature, and they’ve shifted their focus to data to compete within the industry. Meanwhile, smaller biopharma, given their lower overall case volumes, fewer products in the market, and leaner operations, often do not yet leverage RWD. As they prepare themselves to scale in the future, however, being able to leverage these data sources, among others, can become a key consideration..
Many life science organizations continue to rely on legacy internal systems — which may account for the lack of data integration. Over half of organizations report difficulty integrating their current technology solutions. Organizations can partner with third-party organizations to ensure their internal solutions work and support RWD connections.
This increased emphasis on intelligent analytics is a warning to life sciences organizations — regardless of size. Failing to acknowledge (and use) RWD deprives them of a rich source of existing data for building better insights and more accurate models to enhance drug discovery and development.
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