Delivering the power of good-quality Real-World Data in drug development

Executive summary

The growing availability and diversity of Real-World Data (RWD) has opened new opportunities for drug development and discovery. Its systematic use is transforming how we understand the safety, efficacy, and utilization of medicinal products in real-world settings.
High-quality RWD is critical to improving patient outcomes by providing insights that complement traditional clinical trial data. Yet, as data complexity increases, so do the challenges — from data quality and access to analytical methods and privacy concerns. This article explores what constitutes good-quality RWD and its impact on modern drug development.

What defines good-quality Real-World Data?

There is no single, formal definition of “good-quality” RWD. In general, it refers to data collected from routine clinical practice that accurately reflects real-world patient experiences and outcomes. For data to be considered high-quality, it must meet key criteria:

Accuracy

RWD should be reliable and free from errors or biases. Standardized data collection methods help ensure precision and consistency across studies.

Completeness

High-quality RWD captures a full spectrum of relevant variables — from patient demographics and medical history to treatment patterns and clinical outcomes — offering a comprehensive view of patient populations.

Representativeness

Diverse datasets that include variations in age, gender, comorbidities, and socioeconomic background improve the generalizability of findings across clinical settings.

Source reliability

Data sources such as electronic health records, registries, and claims databases must have robust governance and validation processes to ensure data integrity.

Data quality standards and best practices may differ across regulatory agencies and regions, but collaboration among healthcare providers, researchers, and industry remains essential. Within pharmaceutical companies, dedicated Data Landscape teams now play a crucial role in maintaining and improving RWD quality through standardized collection, integration, and analysis.

How good-quality RWD accelerates drug development

In a competitive and highly regulated environment, good-quality RWD helps the pharmaceutical industry bring better drugs to market faster, more safely, and more efficiently.

1. Preclinical research

RWD informs early-stage research by highlighting unmet medical needs, mapping disease progression, and identifying potential therapeutic targets.

2. Clinical trial design and recruitment

By revealing real-world patient characteristics and treatment patterns, RWD helps design more inclusive and representative clinical trials, optimizing recruitment and endpoint selection.

3. Safety and efficacy assessment

Post-approval, RWD supports ongoing safety monitoring, long-term effectiveness evaluation, and identification of rare adverse events.

4. Comparative effectiveness research

RWD enables real-world comparisons between therapies, refining clinical guidelines and supporting shared decision-making between physicians and patients.

5. Regulatory submissions

Regulators increasingly recognize RWD’s value in supplementing trial data for specific subgroups or long-term follow-up, supporting submissions for label extensions and post-marketing studies.

Real-world examples of RWD in action

• The FDA’s Sentinel Initiative: Monitors the safety and effectiveness of medical products using RWD from diverse sources, influencing regulatory decision-making.

• Oncology applications: RWD has been used to assess treatment effectiveness, expand labels, and identify patient subgroups likely to benefit from targeted therapies.

• Rare disease research: High-quality RWD fills data gaps where traditional trials are limited, improving understanding of disease natural history and informing development strategies.

Conclusion

Good-quality RWD is an invaluable asset throughout the drug development lifecycle. It enriches scientific understanding, strengthens evidence for safety and efficacy, and empowers regulators and researchers to make more informed decisions.

By maintaining high data quality standards and fostering collaboration across stakeholders, the life sciences community can unlock the full potential of RWD to improve healthcare outcomes globally.

References

• Real-World Data Quality: What are the Opportunities and Challenges? by McKinsey. This article discusses the quality of RWD, focusing on the challenges and opportunities associated with ensuring the quality of RWD. It also provides recommendations for improving the quality of RWD. [Link: https://www.mckinsey.com/industries/life-sciences/our-insights/real-world-data-quality-what-are-the-opportunities-and-challenges] 
• Real-World Data: A Brief Review of the Methods, Applications, Challenges and Opportunities by BMC Medical Research Methodology. This article provides a brief review of the methods, applications, challenges, and opportunities associated with real-world data. It also discusses the importance of ensuring the quality of RWD. [Link: https://bmcmedresmethodol.biomedcentral.com/articles/10.1186]
• EFPIA’s Position on the Use & Acceptance of Real World Evidence by International Markets https://www.efpia.eu/media/602956/efpia-rwe-position-paper_aug2021.pdf A must read position paper  on EFPIA’s view on the use and acceptance of Real World Evidence (RWE) to support benefit/risk decision-making.
• The Regulatory road to Innovation by EFPIA https://efpia.eu/media/541132/efpia_regulatory-road-to-innovation_leaflet.pdf An infographic about EFPIA’s vision of future drug development and discovery 
• Klimek P et al  Quality Criteria for Real-world Data in Pharmaceutical Research and Health Care Decision-making: Austrian Expert Consensus. JMIR Med Inform. 2022 Jun 17;10(6):e34204. doi: 10.2196/34204. PMID: 35713954; PMCID: PMC9250059.
• Valla V et al Use of Real-World Evidence for International Regulatory Decision Making in Medical Devices. International Journal of Digital Health DOI: 10.29337/ijdh.50