Addressing Real-World Evidence (RWE) challenges in healthcare

Real-World Evidence (RWE) has become a crucial component in healthcare decision-making, influencing health technology assessments (HTA) and medical practices across the globe. Despite its importance, the utilisation of RWE faces several challenges that impede its full potential. In this article CELforPharma faculty member Prof. Dr. Thomas Wilke, from the Generating RWE for Optimising Market/Patient Access course, explores the typical challenges associated with RWE and outlines potential solutions to enhance its strategic value and application.
 

🚧 Challenges in RWE:
 

1. Strategic Value and Acceptance: One of the challenges is the lack of awareness about the strategic value of RWE. Additionally, even when the importance of RWE is recognised, specific RWE data often fail to gain acceptance.
    
2. Accessibility and Relevance of Data: The availability and accessibility of relevant Real World Data is often limited. Databases that contain valuable data are either unknown or inaccessible to many researchers and decision-makers. And even when data is available, it can be outdated, reducing its relevance and applicability to current healthcare scenarios.
    
3. Bias and Data Quality: Comparative effectiveness research, a critical application of RWE, is susceptible to bias. The inherent variability in real-world data sources can lead to results that are not entirely reliable. This bias can stem from several factors, including patient selection, data collection methods, and analytical approaches.
    
4. Primary Data Collection Challenges: Collecting primary data through medical chart reviews (MCRs) and surveys can be time-consuming and logistically challenging. These processes often face long timelines and high costs, further complicating the generation of timely and relevant RWE.
    
5. Integration of Local and Global Data Needs: Balancing local data needs with global processes presents another significant challenge. Local data is crucial for addressing region-specific health issues, but integrating this with global standards and practices requires harmonised and collaborative efforts across various teams and jurisdictions.
    

💡 Potential Solutions:
 

1. Evidence Generation Planning: A well-planned evidence generation strategy is essential. This includes transparent and up-to-date methodologies that ensure the produced RWE is both relevant and credible. Regular updates to database repositories should be planned to maintain the timeliness and applicability of the data.
    
2. Techniques to Improve Data Quality: Implementing trial emulation frameworks can enhance the reliability of RWE. Techniques such as matching, quantitative bias analysis, and machine-learning approaches can mitigate biases and improve the robustness of comparative effectiveness research.
    
3. Utilisation of Existing Site Networks: Leveraging existing site networks in key geographies can streamline data collection processes. These networks can facilitate faster and more efficient primary data collection, ensuring that the data is both comprehensive and timely.
    
4. Collaborative Decision-Making: Effective RWE utilisation requires the collaboration of global and local teams. Shared decision-making processes, ideally harmonised by appropriate software solutions, can bridge the gap between local data needs and global processes. This collaboration ensures that RWE is utilised effectively and consistently across different regions.