Key takeaways

• RWD comes from routine care, such as EHRs, registries, claims, wearables, pharmacy and PROs, and when analyzed, becomes RWE that complements RCTs

• Pharma applies RWE across the lifecycle, including trial design, label expansion, safety, market access and post-launch performance

• Robust data governance, such as privacy, security and lineage, and bias mitigation are critical to make RWE decision-grade

• Regulators increasingly accept RWE when studies are well-designed, transparent and minimize bias

• AI/ML and strong IT partnerships accelerate RWE generation, improving speed to evidence and patient outcomes

Healthcare is immersed in a wealth of invaluable data. Every aspect, from patient records and diagnostic tests to treatment plans and medical trials, generates information that holds immense potential for advancing patient care. The sheer volume and diversity of this data are expanding rapidly, with the compounded annual growth rate (CAGR) projected to hit 36% by 2028. 

Understanding how medications perform in real-world scenarios can revolutionize the pharmaceutical industry. It can drive efficiencies in drug development and identify new patient populations for existing treatments, ultimately leading to increased profitability and shareholder returns. 

However, pharmaceutical leaders must effectively harness RWD and RWE to establish a lasting competitive advantage in an evolving landscape. To begin, it's essential to grasp the fundamental concepts of RWD and RWE.

What is real-world data (RWD) in healthcare?

Real-world data (RWD) is information generated in routine healthcare settings, including outside controlled clinical trials, about patient health status and care delivery.

Why it matters: RWD reflects diverse patient populations, everyday clinical practice and variations in care, helping stakeholders understand how therapies perform beyond idealized trial conditions.

Example: RWD shows how patients with multiple conditions respond to treatment.

Where RWD comes from: EHRs, registries, claims and wearables

• Electronic health records (EHRs): capture labs, diagnoses, treatment plans, medications, imaging and clinical notes

• Disease and product registries: track outcomes for specific conditions or therapies over time in structured formats

• Claims and billing data: record procedures, diagnoses and costs submitted to payers, enabling utilization and outcomes analyses at scale

• Patient-generated data and wearables: collect activity, heart rate and symptom data between visits via smartphones and sensors

• Pharmacy and dispensing data: show prescription fills, adherence patterns and switches

• Patient-reported outcomes (PROs): reflect symptoms, quality of life and treatment burden directly from patients.

Integrating these varied sources is essential to produce reliable, decision-grade real-world evidence (RWE).

What is real-world evidence (RWE) in healthcare?

• Definition: RWE is the clinical insight produced by analyzing RWD

• Role versus RCTs: It complements randomized controlled trials by showing how treatments work in everyday care, across broader populations and settings

• Example: RWE can reveal side effects or benefits in patient groups underrepresented in trials

• Regulatory acceptance: Both the FDA and EMA now consider RWE for approvals, label expansions and reimbursement assessments

• Who benefits: Pharma, payers, clinicians and — most importantly — patients use RWE to guide decisions.

RWE is becoming essential for modern, evidence-based healthcare decisions.

Mind the efficacy–effectiveness gap

The efficacy-effectiveness gap underscores the importance of bridging the divide between clinical trial results and real-world outcomes. While randomized controlled trials remain essential for establishing initial drug efficacy, understanding how drugs perform in the real world is equally crucial. The divergence between idealized trial conditions and real-world use can lead to unexpected efficacy issues or adverse effects, highlighting the need for a comprehensive approach to evaluating drug performance.

How pharma uses RWE across the product lifecycle

Pharmaceutical companies are increasingly recognizing the potential of RWE to enhance decision-making and drive value across the product lifecycle. Examples abound, such as Pfizer's successful use of electronic medical records to gain approval for treating male breast cancer and AstraZeneca's demonstration of its diabetes treatment's effectiveness through real-world data. McKinsey estimates that embracing RWE could unlock substantial value for pharmaceutical firms, potentially exceeding $300 million annually for top players.

Label expansion and market access

Pharma companies use RWE to demonstrate how a therapy performs across real-world populations and settings, generating evidence that can support supplemental indications and label expansions. By quantifying effectiveness and outcomes in routine care, RWE helps validate clinical benefits outside trial cohorts.

RWE also strengthens payer negotiations. Comparative effectiveness, adherence and resource-utilization analyses show value in target populations, informing coverage, reimbursement and formulary placement. RWE helps pharma demonstrate effectiveness in diverse populations, which supports reimbursement and market access.

Safety and pharmacovigilance

After approval, RWE enables continuous safety surveillance at population scale. Large, longitudinal datasets help detect rare adverse events that are unlikely to appear in pre-approval trials and reveal risk factors in subgroups, including the elderly, comorbid or poly-medicated patients.

Ongoing monitoring with RWD supports faster regulatory and clinical responses, including updating labels, issuing communications or informing risk-minimization measures, while giving clinicians timely insight into real-world safety profiles.

Modern IT tools, particularly AI/ML, augment pharmacovigilance by mining unstructured notes, claims and device data for early safety signals. Techniques such as anomaly detection and natural language processing help prioritize true signals over noise and shorten time to action.

How IT partners accelerate RWE adoption

In this landscape, IT companies play a pivotal role as strategic partners, leveraging their expertise in data integration, analytics and technology infrastructure. By enabling pharmaceutical companies to harness diverse sources of real-world data, IT firms facilitate the generation of actionable insights for drug development, safety monitoring, and market access strategies. Moreover, they ensure compliance with regulatory standards, providing secure and scalable technology solutions for RWE generation and submission. 

Through collaborative partnerships and innovative solutions, IT companies contribute to advancing evidence-based healthcare and fostering innovation in the pharmaceutical industry. By empowering stakeholders with the tools to harness the full potential of RWD and RWE, these partnerships pave the way for improved patient outcomes, streamlined drug development processes, and enhanced healthcare decision-making.

Regulatory landscape: FDA and EMA on RWE

The FDA defines RWE as clinical evidence about a medical product’s use and potential benefits or risks derived from analysis of RWD and runs an RWE Program to guide when and how RWE can support regulatory decisions. FDA materials emphasize fit-for-purpose data, rigorous methods and transparent study conduct. 

The EMA is scaling use of RWE through initiatives like DARWIN EU, which delivers decision-ready evidence from European data networks and registries to support benefit-risk assessment and lifecycle oversight. EMA guidance highlights high-quality data, appropriate study designs and reproducibility. 

What regulators look for in RWE

• Completeness and relevance of the underlying RWD

• Data quality, provenance and transparency of curation

• Robust methods to control bias and confounding

• Prespecified protocols, reproducibility and auditability

Data governance, privacy and bias in RWD

1. Privacy: Protect patient privacy with de-identification/pseudonymization, explicit consent where required and role-based access that limits identifiable data to those with a legitimate purpose. Implement robust encryption and data-minimization by default.
2. Governance: Establish clear data lineage and stewardship, maintain audit trails for all transformations and document standard operating procedures. Ensure compliance with HIPAA (US) and GDPR (EU), including lawful bases for processing, retention limits and subject rights management.
3. Bias: Common sources include selection bias (who enters the dataset), measurement bias (inconsistent coding, missingness) and confounding. Mitigate using propensity scores, inverse probability weighting, cohort balancing, sensitivity analyses and pre-registered protocols; complement with clinician review to validate findings.

Principles of responsible RWE

• Privacy-by-design and least-privilege access

• Transparent lineage, standards and auditability

• Fit-for-purpose data with active bias mitigation

• Reproducible methods and prespecified analysis plans