Over the last decade, regulators have encouraged sponsors to adopt risk-based monitoring (RBM) as a way to improve trial oversight and focus attention where it matters most. More recently, ICH E6(R3) has reinforced the broader shift toward risk-based quality management, while FDA guidance on artificial intelligence has added a new layer of discussion around how technology can support monitoring activities.
Despite that momentum, implementation remains uneven across the clinical research landscape. Many organizations support the idea of RBM in principle, but continue to struggle with consistent execution across studies and programs.
Recent surveys tell a similar story. Roughly 65% of trials now include at least one RBM element, yet only about half of organizations report implementing RBM across a majority of their studies. Training also remains inconsistent, with only about 20% of surveyed teams reporting extensive RBM-specific education.
Common Barriers to RBM Adoption
RBM is widely accepted as a framework for improving oversight and efficiency, but it often breaks down during execution. That’s because it requires changes in how teams think about risk, how data is reviewed, and how monitoring work is shared across functions.
These challenges tend to fall into a few common areas:
Growing trial complexity
Clinical trials today generate more data than ever, and it comes from more sources. Decentralized trial elements, wearable devices, patient-reported outcomes, imaging data, and expanding endpoint sets all add layers of information that must be monitored simultaneously.
This volume and variety make it harder to distinguish signal from noise. Risks aren’t always obvious, and teams can struggle to prioritize which issues require immediate attention versus those that are less urgent.
Skills and training gaps
Many teams understand the principles of RBM at a high level, but haven’t received structured, hands-on training on how to apply them in real-time.
As a result, implementation can vary significantly from one trial to another. Risk assessments may be completed inconsistently, and monitoring activities may default back to familiar site-focused patterns rather than risk-driven approaches.
Fragmented data sources
Clinical, operational, safety, enrollment, and site performance data often live in separate systems that aren’t fully integrated. This fragmentation can make it challenging to spot risks when they arise and may allow issues to escalate over time.
Resistance to change
Some organizations still rely on traditional monitoring models that involve routine site visits and retrospective review.
Shifting toward RBM requires a different mindset, where oversight is guided by risk signals rather than uniform checklists. That shift can be uncomfortable, particularly in environments where established processes have been used for years.
4 Ways Sponsors Can Strengthen RBM Programs
These challenges are common, but they aren’t insurmountable. Sponsors can strengthen RBM implementation and improve consistency across studies by making a few targeted changes.
- Start with critical-to-quality factors and risk assessments
Strong RBM programs begin with clear definitions of what matters most. Critical-to-quality (CTQ) factors are especially important because they help identify the data and processes that directly affect participant safety and study integrity.
When these priorities are defined early, risk assessments become more focused. Monitoring activities can then be aligned to the areas that matter most rather than spread evenly across all data points.
- Invest in centralized monitoring capabilities
Centralized monitoring adds a layer of oversight by analyzing study data across sites in aggregate.
Statistical surveillance can help identify trends, outliers, and emerging patterns that may not be visible during routine site visits. This allows teams to intervene earlier and direct attention where it’s most needed.
- Build a flexible monitoring team
RBM works best when monitoring isn’t tied to a single model. A combination of centralized, remote, and on-site monitoring allows sponsors to adjust oversight based on study risk and site performance.
This flexibility helps ensure resources are directed toward higher-risk areas while still maintaining appropriate oversight across the study.
- Use technology to support risk identification
Technology, including artificial intelligence, is increasingly being used to connect disparate data sources and identify potential risks more quickly.
The FDA’s recent guidance on AI builds on this growing interest, particularly in how automated tools can support, rather than replace, human decision-making. In RBM programs, these tools are most effective when they enhance review processes and help teams focus their attention, rather than operating in isolation.
Where RBM is headed
RBM is increasingly tied to the broader move toward risk-based quality management under ICH E6(R3). As data volume and complexity grow, sponsors are placing greater focus on targeted oversight and less on uniform monitoring. Those that invest in more structured, risk-driven approaches will be better positioned to meet evolving regulatory expectations.
Support for RBM programs
Effective RBM depends on aligned processes, clear prioritization, and the ability to interpret data across multiple sources. Harbor Clinical regularly works with sponsors to support these efforts through services such as vendor oversight, quality assurance, data management, pharmacovigilance, and other functional support areas that strengthen study oversight across the trial lifecycle.
To learn more about our services, request a proposal through our online form here.