Site solutions
Implementing data-driven risk-based monitoring with cubeRBQM
Risk-based
Risk levels
Data insights
Background
For many years, CMIC relied on conventional methods – EDC, CTMS, and Excel trackers – to perform risk assessments. While these tools were sufficient for some time, the evolution of the clinical trials landscape and regulatory requirements led CMIC to reconsider their options.
They were facing an increasing trial complexity, involving larger patient populations, more sites across multiple regions, and diverse data sources. They were also concerned that traditional trackers could not handle the volume and variety of incoming data efficiently, making it difficult to maintain oversight.
The following pain points were the main drivers for change:
Difficulty visualizing complex data intuitively and in real timeTeams had to manually compile datasets, which made it challenging to gain a timely, holistic view of trial performance. Delays in signals identification led to reactive rather than proactive responses. |
Limited flexibility to adapt to trial-specific risk indicatorsTraditional trackers relied on metrics not reflecting each clinical trial’s unique risks. CMIC often found it difficult to tailor monitoring indicators mid-study, potentially missing emerging risks. |
For many years, CMIC relied on conventional methods – EDC, CTMS, and Excel trackers – to perform risk assessments.
Background
For many years, CMIC relied on conventional methods – EDC, CTMS, and Excel trackers – to perform risk assessments. While these tools were sufficient for some time, the evolution of the clinical trials landscape and regulatory requirements led CMIC to reconsider their options.
They were facing an increasing trial complexity, involving larger patient populations, more sites across multiple regions, and diverse data sources. They were also concerned that traditional trackers could not handle the volume and variety of incoming data efficiently, making it difficult to maintain oversight.
The following pain points were the main drivers for change:
Difficulty visualizing complex data intuitively and in real timeTeams had to manually compile datasets, which made it challenging to gain a timely, holistic view of trial performance. Delays in signals identification led to reactive rather than proactive responses. |
Limited flexibility to adapt to trial-specific risk indicatorsTraditional trackers relied on metrics not reflecting each clinical trial’s unique risks. CMIC often found it difficult to tailor monitoring indicators mid-study, potentially missing emerging risks. |
High workload for CRAs, limiting efficient monitoringCRA’s site visits were scheduled at fixed intervals, regardless of risk levels for those sites. This consumed significant resources, with CRA’s time not always focused on the sites that needed the most attention. |
Delays in integrating multi-source data, slowing down analysisWith data across multiple systems, integration was slow and labour-intensive. This lag prevented CMIC from making evidence-based decisions quickly enough to prevent risks from escalating. |
These limitations hindered CMIC’s ability to detect risks early and respond quickly. The company needed a data-driven solution that could support modern risk-based monitoring requirements while reducing the operational burden on clinical teams.
Solution: cubeRBQM
In response, CMIC implemented cubeRBQM, a solution designed to centralize data surveillance and streamline risk-based monitoring processes.
The system was deployed with the following key features:
Advanced data visualization
Built-in dashboards enabled monitoring teams to track key insights such as enrolment rates, screen failure rates, and protocol deviations. With data entry (cubeCDMS & cubePRO) updating risk indicators daily, the system provided near real-time visibility into trial health.
Automated data integration
Direct data ingestion from cubeCDMS ensured seamless daily updates, reducing the need for manual data preparation and enabling CRAs and monitors to focus on analysis.
Flexibility in risk indicators
While preconfigured KRIs were established at study startup, CMIC could add or modify indicators mid-study as new risks emerged, ensuring continuous adaptability.
Central and site level risk management
Central monitoring was connected to site risk assessments, allowing CMIC to quantify risks at both the trial and site levels. This linkage supported more objective site evaluations, helping CRAs prioritize visits where they would have the greatest impact.
Results
cubeRBQM became more than just a data management tool – it allowed CMIC to modernize its monitoring strategy in line with regulatory expectations and industry best practices.
Following its implementation, CMIC observed clear benefits in both operational and trial quality:
Faster risk detectionReal-time dashboards allowed monitors and study teams to track critical risk indicators continuously, enabling proactive interventions. |
Cross-site quality monitoring:With unified data views, CMIC could detect trends across sites, not just within individual centres, reducing systemic risks. |
Operational efficiency gains:CRA effort was reduced by approximately 14.4% FTE, demonstrating measurable resource savings. |
Alignment with ICH E6(R3):By embedding risk-based approaches into daily practice, CMIC advanced toward Clinical Quality Management (CQM), reinforcing regulatory compliance. |
One of the most notable achievements was the shift to data-driven site risk management. Instead of relying solely on subjective CRA assessments, CMIC was able to conduct multifaceted, quantitative evaluations that enhanced both the quality and efficiency of monitoring.
Voices from the field
CMIC’s team saw some noticeable improvements in their workflows, as reflected in the feedback they shared with CRScube.
A central monitor noticed a better reactivity during trial execution:
“The ability to quickly revise KRIs and add new visualization items was extremely valuable. It gave us the agility to adapt to operational realities without long delays.”
CMIC sees the implementation of cubeRBQM as an opportunity to further enhance efficiencies in the future:
“We see great promise in the future development of automatic AI-driven risk detection within the system. The scalability and adaptability of cubeRBQM give us confidence in its long-term value.”
Together with CRScube, CMIC is committed to evolving RBM solutions that remain adaptable, data-driven, and aligned with the needs of sponsors and regulators worldwide.
For many years, CMIC relied on conventional methods – EDC, CTMS, and Excel trackers – to perform risk assessments.
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