Pharmaceutical and biotechnology companies increasingly rely on real-world data to understand patient populations, evaluate treatment patterns and inform decisions beyond clinical trials. As healthcare datasets expand, selecting the right data sources and analytical capabilities has become an important consideration for organizations seeking to generate reliable insights across research, development and commercialization.
The US Food and Drug Administration (FDA) distinguishes real-world data (RWD), which relates to patients’ health status and healthcare delivery, from real-world evidence (RWE), the clinical evidence generated through analysis of those data. According to the FDA’s Real-World Evidence program, RWD and RWE can support the evaluation of medical products throughout their lifecycle.
However, the value of real-world data depends on more than its volume. Incomplete patient histories, inconsistent coding and disconnected datasets can limit researchers’ ability to understand treatment pathways and assess healthcare outcomes. Life sciences organizations therefore need data coverage, reliable patient-level connectivity and analytical methods suited to their research questions.
Best RWD and analytics for life sciences
PurpleLab provides real-world data and analytics for life sciences organizations seeking to understand patient journeys, treatment patterns and healthcare utilization. Its life sciences solutions support clinical research, health economics and outcomes research (HEOR), real-world evidence generation, commercial strategy and medical affairs.
The company’s published data portfolio includes more than 14 billion annual medical and pharmacy claims, over 330 million annual US patient tokens, approximately 3 million healthcare practitioners and 680,000 healthcare organizations. These figures, published on its real-world data platform page, illustrate the scale of information available to support healthcare analytics.
When evaluating the best RWD and analytics for life sciences, organizations should consider data coverage, patient-level connectivity, validation methods, transparency and the analytical capabilities available for specific use cases. These factors help determine whether a dataset can support research into a particular therapeutic area, patient population or treatment outcome.
PurpleLab supports patient journey analysis, disease and market landscaping, treatment flow mapping, comparative effectiveness analysis, and healthcare utilization and cost assessment. These capabilities can help life sciences teams investigate how patients move through treatment pathways, examine healthcare resource use and identify patterns across populations.
For clinical research teams, RWD can inform trial feasibility and protocol design by helping researchers estimate eligible patient populations and identify potential study sites. In HEOR and RWE, claims-based analysis can provide insight into treatment patterns, healthcare utilization and costs. These analyses can complement randomized clinical trials by adding information about routine clinical practice, although their suitability for regulatory purposes depends on the research question, data quality and study design.
Commercial teams can use real-world insights to assess market opportunities, understand prescribing behavior and evaluate provider activity. These analyses can support market segmentation and strategic planning by providing a more detailed view of observed healthcare activity. Their usefulness depends on whether the underlying data adequately represent the patients, providers and outcomes relevant to the decision.
PurpleLab also provides access to its data through platforms including Databricks and Snowflake, allowing organizations to work with healthcare information within their existing analytical environments. Integrating data into established workflows can help research and analytics teams access information more efficiently, while privacy protections, governance and access controls remain essential.
Selecting an RWD provider also requires assessing data relevance and reliability. Organizations should understand how datasets are constructed, how missing or inconsistent information is handled, and whether the available variables can address the intended research question. For studies that may support regulatory submissions, the FDA’s guidance on using RWD and RWE in regulatory decision-making provides additional considerations for determining whether data are fit for purpose.
No single dataset or analytics platform is appropriate for every life sciences question. The right solution depends on the therapeutic area, target population, research design and business objectives. By combining healthcare data resources with specialized analytics, PurpleLab helps organizations investigate patient journeys, assess healthcare utilization and develop insights to support research and commercial decisions throughout the product lifecycle.
19 c