Real-world data (RWD) are essential for making decisions about product value, safety, effectiveness, and other potential applications. However, compared to clinical trial data, there is far less control over how and what data are captured. Data may arrive fragmented, inconsistent, or incomplete, creating downstream challenges in analysis and interpretation. Addressing these issues calls for a pragmatic, risk-based approach to real-world evidence analytics that focuses resources on the variables that matter most for your endpoints, paired with strategies to improve data integrity and reliability from the outset.
Real-World vs Clinical Trial Data
Clinical research data are collected under strict protocols and supported by controlled data entry systems. In real-world settings, those controls rarely exist. Data often come from disparate EHR systems or other sources where formats, standards, and completeness can vary considerably.
Common challenges for RWD analysis include:
- Lack of standardization: Variables may be captured differently across sites or stored in free-text fields that make consistent interpretation difficult
- Incomplete datasets: Key elements may be missing because they were never collected
- Retrospective limitations: RWD are often gathered after treatment has occurred, removing the opportunity to influence how they were documented
- Integration complexity: Data must often be consolidated from multiple systems and formats before they can be meaningfully analyzed
A Practical Framework for Risk-Based Real-World Evidence Analytic
When working with RWD, cleaning every variable to the same standard is rarely feasible or necessary. A risk-based approach directs attention to the elements that are most critical to the study’s endpoints or decision-making needs, allowing teams to concentrate resources where they will have the greatest impact.
Setting the Right Foundation for Data Quality
The most effective way to improve RWD integrity is to address quality before collecting large volumes of data. That begins with a realistic assessment of what data are available across participating sites or data sources. This often means confirming with investigators what can be retrieved from their systems rather than assuming all desired variables will be present. Aligning early on achievable targets helps avoid wasted effort on data points that will be incomplete or inconsistent across the cohort.
Early checkpoints are another practical safeguard. For example, reviewing the first 20-25 cases can reveal whether the incoming data align with your goals for real-world evidence analytics and whether adjustments to the collection plan are needed. Small course corrections at this stage can prevent major downstream challenges.
Balancing Automation and Human Oversight
Many of the most time-consuming aspects of data review involve repetitive comparisons across datasets or domains. This work can be partially automated to improve efficiency. AI-enabled tools for EHR analytics can interrogate data in the context of the protocol, identify anomalies, and produce targeted summaries. Instead of manually downloading and sorting multiple exports, reviewers can ask the AI system focused questions (e.g., adverse events that may be linked to specific medications) and receive a curated output for follow-up.
While automation can flag patterns, it does not replace expert judgment. Human review is necessary for interpreting results, deciding on queries, and ensuring that flagged issues are resolved in line with the study’s objectives. The value of AI lies in freeing experienced data managers from repetitive tasks so they can focus on the work that requires their expertise, not in removing humans from the process entirely.
Applying Lessons From Clinical Data Management
While RWD present unique constraints, many challenges in clinical data management still apply:
- Thoughtful data structure: Use controlled fields and coded responses wherever possible to reduce variability and support cleaner integration
- Clear documentation: Maintaining traceability in how data are transformed or harmonized is critical, even when original sources vary in completeness
- Targeted validation: As in trials, focusing quality checks on the most influential variables ensures that resources are directed where they have the most impact
These practices provide a framework for working within the realities of RWD while still maintaining a high level of confidence in the outputs. By blending established clinical data management rigor with strategies tailored for retrospective, heterogeneous sources, sponsors can make real-world datasets more reliable.
Your Pragmatic Data Partner
Ephicacy brings experience across both clinical and real-world evidence analytics, giving sponsors a clear view of what is feasible and how to achieve it. Our teams combine targeted, risk-based strategies with emerging automation tools to improve the integrity of complex datasets while making the best use of available resources.
Contact us to discuss how we can help you design and execute a focused, risk-based RWD strategy.
