Trial team solutions
Leveraging cubeCDMS planned SDV to improve monitoring efficiency
Monitoring efficiency
Risk based monitoring
Resource optimization
Background
Historically, EPS relied on extensive on-site and off-site monitoring across all subjects to ensure data quality. This approach had proven effective over many years and supported the consistent delivery of clinical trials. However, as the clinical trial landscape evolved, it became increasingly important to align monitoring activities with study-specific risks and objectives.
With the growing emphasis on Risk-Based Monitoring (RBM), EPS recognised the need to improve operational efficiency while strengthening data quality. A particular challenge was controlling the scope of Source Data Verification (SDV) in a way that reduced unnecessary burden on CRAs without compromising oversight. This direction is also clearly reinforced in ICH E6 (R3), which highlights the importance of monitoring activities proportionate to risk.
EPS were facing two main challenges:
High CRA workload limiting effective monitoringCRAs were required to perform SDV across all data points, resulting in a significant operational burden. This made it difficult to prioritise high-risk sites or focus resources where they were most needed. |
Limited flexibility for monitoring and SDV strategies in EDCWhile SDV decisions were informed by central monitoring and site risk assessments, CRAs relied on the EDC to identify SDV targets. To operationalise RBM effectively, EPS needed a solution that clearly defined SDV scope directly within the EDC and allowed adjustments as risk profiles evolved. |
EPS adopted cubeCDMS Planned SDV as the foundation of its risk-based monitoring approach, enabling both the strategic and operational execution of risk-based monitoring.
Background
Historically, EPS relied on extensive on-site and off-site monitoring across all subjects to ensure data quality. This approach had proven effective over many years and supported the consistent delivery of clinical trials. However, as the clinical trial landscape evolved, it became increasingly important to align monitoring activities with study-specific risks and objectives.
With the growing emphasis on Risk-Based Monitoring (RBM), EPS recognised the need to improve operational efficiency while strengthening data quality. A particular challenge was controlling the scope of Source Data Verification (SDV) in a way that reduced unnecessary burden on CRAs without compromising oversight. This direction is also clearly reinforced in ICH E6 (R3), which highlights the importance of monitoring activities proportionate to risk.
EPS were facing two main challenges:
High CRA workload limiting effective monitoringCRAs were required to perform SDV across all data points, resulting in a significant operational burden. This made it difficult to prioritise high-risk sites or focus resources where they were most needed. |
Limited flexibility for monitoring and SDV strategies in EDCWhile SDV decisions were informed by central monitoring and site risk assessments, CRAs relied on the EDC to identify SDV targets. To operationalise RBM effectively, EPS needed a solution that clearly defined SDV scope directly within the EDC and allowed adjustments as risk profiles evolved. |
Solution: cubeCDMS planned SDV
EPS adopted cubeCDMS Planned SDV (PSDV) as the foundation of its RBM approach. PSDV enabled both the strategic and operational execution of risk-based monitoring by translating monitoring plans directly into the EDC environment.
The key benefits of the implementation included:
Targeted SDV based on critical risk data
SDV scope could be optimised by site, subject, and visit, aligned with trial-specific risk assessments. Clinical operations, central monitoring, and data management teams were able to align early in the study and dynamically adjust monitoring plans as trial conditions changed.
More efficient study planning
From study start-up, PSDV supported cross-functional alignment through SDV rate simulations, standardised specifications, and harmonised procedures. This led to streamlined operations, improved consistency, and strengthened data quality and compliance.
Results
By implementing Planned SDV within cubeCDMS, EPS successfully optimised its monitoring approach and achieved tangible improvements across trial operations.
Improved monitoring efficiencyCRAs could focus on defined SDV scopes, enabling more efficient monitoring activities centred on critical data and processes. |
Flexible, risk-driven trial oversightCentral monitors could rapidly adjust SDV scope based on Key Risk Indicators (KRIs), improving responsiveness, oversight, and trial agility. |
Enhanced regulatory alignmentBy embedding RBM into day-to-day operations, EPS strengthened alignment with ICH E6 (R3) guidance and reinforced core principles of clinical quality management. |
Voices from the field
Following the introduction of PSDV, teams reported a range of positive outcomes:
CRA
Clear identification of SDV targets within the EDC significantly improved operational efficiency.
Central Monitor
Faster KRI-driven SDV adjustments enhanced proactive risk management.
Data Management
Standardised PSDV specifications and processes reduced coordination effort with CRAs and central monitors, improving both system build and operational efficiency.
These insights highlight strong engagement from end users. We are committed to keep enhancing risk-based SDV to deliver oversight that is proportionate, adaptive, and aligned with real-world operational needs.
EPS adopted cubeCDMS Planned SDV as the foundation of its risk-based monitoring approach, enabling both the strategic and operational execution of risk-based monitoring.
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