Maximizing real-world evidence for success in life sciences

Real-world evidence (RWE) is becoming increasingly vital in the life sciences and pharmaceutical industries as a tool for enhancing outcomes and reducing costs. Derived from the meticulous analysis of real-world data (RWD), RWE provides a comprehensive and accurate view of how medicines and treatments perform in everyday settings.

Benefits of RWE

1. Effectiveness and safety insights: RWE offers insights into the effectiveness and safety of products in real-world settings. Traditional clinical trials are highly controlled and may not reflect diverse patient populations and real-world conditions. RWE captures data from various sources, including electronic health records, insurance claims and patient registries, and provides a more accurate representation of a medicine’s performance.
2. Identifying unmet needs: Companies can use RWE to identify unmet medical needs and gaps in current treatments. This allows for the design of new products that address these issues and the identification of potential biomarkers or patient subgroups that may benefit from specific treatments. The result is more personalized medicine.
3. Regulatory compliance and accelerated approval: RWE can help companies meet regulatory requirements and fast-track the approval process for new treatments. The U.S. Food and Drug Administration (FDA) has recognized the value of RWE and established a framework for its use in drug development and post-market surveillance.

Challenges in using RWE

Despite its potential, RWE poses several challenges for life sciences and pharmaceutical companies:

1. Data quality and standardization. Providing data quality and standardization is crucial as RWE is collected from multiple sources, leading to potential data inaccuracies and bias.
2. Data access and privacy. Privacy concerns and regulations can limit access to high-quality RWE, resulting in incomplete or fragmented data.
3. Limited generalizability. RWE often comes from specific patient populations, which may not represent the overall population and therefore limit the generalizability of findings.
4. Lack of standardized methodologies. The absence of standardized methodologies for collecting and analyzing RWE can lead to inconsistencies across studies.
5. Bias and confounding. RWE is subject to bias and confounding factors, such as healthcare provider prescribing patterns or patient adherence to treatment.
6. Regulatory and reimbursement hurdles. While gaining recognition, RWE is not yet widely accepted by all regulatory agencies and payers. This poses challenges for its use in regulatory submissions or reimbursement decisions.
7. Cost and time constraints. Conducting RWE studies can be expensive and time-consuming, posing a barrier for smaller companies with limited resources.

Maximizing RWE’s capabilities with advanced platforms

To overcome these challenges and maximize RWE’s capabilities, NTT DATA’s Evidence Platform for Life Sciences offers a powerful solution. By using the cloud’s advanced data warehousing and analytics capabilities, the platform enables organizations to gather, analyze and interpret large quantities of real-world data in real time. This partnership facilitates:

• Integrated data access: Combines multiple data warehouses in a single cloud location for easy access and analysis.
• Enhanced analytics: Uses a powerful cloud platform to analyze data and present it in myriad ways for the most accurate insights.
• Comprehensive health data bank: Accesses NTT DATA’s Health Data Bank, including anonymized patient and claims information, community contextual data and more. Health Data Bank is preloaded with national health data from commercial, Medicare Advantage, Medicaid and Centers for Medicare and Medicaid Services (CMS) health plans.

This bundled offering helps life sciences and pharmaceutical companies integrate real-world data to innovate faster and address data-driven challenges. Our expertise in RWE, data analytics and artificial intelligence can help you gain valuable insights into patient populations and develop products that achieve the highest outcomes.

In a recent RWE use case, for example, we analyzed time-dependent disease severity patterns in prescription and reimbursement using real-world closed claims data in the atrial fibrillation (AFib) therapeutic area. The analysis showed that the proportion of severe patients being treated with a specific medication for afib increased over time for several years post-launch.

Learn more about NTT DATA’s Evidence Platform for Life Sciences and contact us to discuss how we can help you stay ahead in this fast-changing industry with our comprehensive data and AI solutions.