Why more complete real-world data is the engine of actionable health research

Medical and biomedical research are at an inflection point. While randomized controlled trials (RCTs) remain the gold standard for establishing efficacy, they are increasingly complemented by real-world evidence (RWE) that reflects how patients actually experience care outside controlled settings. Across the healthcare ecosystem, there is growing recognition that improving outcomes, enhancing the consumer experience, and lowering the cost of care depend on understanding what happens beyond controlled trials.

To do so, regulators, payers, and life sciences organizations require real-world data (RWD) that is broader, deeper, and more representative than traditional research sources alone. Yet the true power of RWD is unlocked only when it is complete, integrated, and analysis-ready.

Real-world evidence, generated from RWD, has become central to decision-making across the product lifecycle, from early research and clinical development to regulatory submissions, market access, and post-market surveillance. Regulatory bodies such as the U.S. Food and Drug Administration (FDA) increasingly rely on RWE to supplement clinical trial findings, particularly when trials are limited by narrow inclusion criteria, small sample sizes, or artificial care environments. But not all RWD are created equal. Fragmented, siloed datasets can lead to incomplete or misleading conclusions, slowing research timelines and eroding confidence in findings.

One of the most persistent challenges in health research is the incomplete view of the patient journey. Claims data alone, for example, may show that a prescription was filled, but not whether a patient adhered to therapy or why they discontinued treatment. Electronic health records (EHRs) may capture clinical context, but often lack longitudinal visibility across providers, payers, and care settings. Social determinants of health (SDoH), which account for a significant share of health outcomes, are frequently missing altogether when data sources are considered in isolation. Without a complete picture of a person’s health, researchers risk attributing outcomes to therapies or interventions when the real drivers lie elsewhere.

Integrated, analysis-ready, RWD addresses this problem by combining multiple data sources into a coherent, longitudinal view of patients and populations. Carelon Research’s Real World Data offering exemplifies this approach. Built on the Healthcare Integrated Research Database (HIRD®), it draws from decades of closed medical and pharmacy claims, linked EHR data, laboratory results, standardized cost data, enhanced oncology data, race and ethnicity data, enrollment records, and SDoH information. This depth and breadth, spanning data from tens of millions of individuals, with records and data that have been deidentified and comply with applicable privacy standards and frameworks, enables researchers to study real-world outcomes with a level of context and continuity that single-source datasets cannot provide.

Equally important is the concept of “analysis-ready” data. Integrating disparate datasets is only the first step. To generate reliable evidence, data must be cleaned, validated, standardized, structured, and deidentified under rigorous governance frameworks. These processes are conducted in alignment with applicable laws to safeguard sensitive health information.  When this work is done upfront, researchers can focus on insight generation rather than data wrangling, accelerating timelines and improving reproducibility. This is critical for studies intended for regulatory review, peer-reviewed publication, or high-stakes commercial and clinical decisions.

Completeness also determines whether research findings are truly actionable. Actionable insights require understanding not just what happened, but why. For example, integrating laboratory data and clinical context with claims can clarify whether a therapy failed due to biological non-response, adherence challenges, or access barriers. Adding SDoH data can illuminate how factors such as housing instability, transportation access, or socioeconomic stressors influence outcomes. The more researchers can understand these factors using deidentified data across patient populations, the deeper and more accurate analysis can be, enabling more targeted and effective interventions that improve outcomes and reduce unnecessary care later.

This principle of completeness extends beyond research datasets into the broader health data ecosystem. Carelon’s Health OS platform integrates clinical and claims data across all 50 states and more than 21 million members, connecting care providers, payers, and consumers to share critical information seamlessly. By enabling more informed decision-making, it helps improve outcomes, enhances the consumer experience, and supports efforts to lower the cost of care.

When research is grounded in such complete data, insights become more than descriptive; they become actionable. Life sciences teams can design better trials, target unmet needs more precisely, demonstrate value to payers, and monitor safety and effectiveness with greater confidence. Health systems and providers can translate findings into interventions that reflect real patient needs and constraints, improving outcomes while helping to lower healthcare costs over time. Ultimately, patients benefit when evidence reflects the complexity of lived experiences rather than an abstract average.