How Risk-Based Monitoring Redistributes and Reshapes Capacity Planning

Clinical trial monitoring is changing, and with it the assumptions that underpin how organizations resource their clinical research associate (CRA) teams and work with investigative sites. Risk-based quality management (RBQM), remote monitoring, and centralized monitoring have attracted significant industry attention, underpinned by rapid technological advancement, raising questions about how the CRA role will evolve. What receives less focus, however, is the direct consequence these models and tools have for capacity planning. In our experience, monitoring time is not reduced but rather redistributed across roles, locations, and timelines.

Recent survey data offer a useful lens on this shift. The 2026 Tufts CSDD Impact Report¹ shows that many sponsors have moved away from traditional monitoring models built around frequent onsite visits and exhaustive source data verification (SDV) and source data review (SDR). Many (43%) already report portfolio-wide implementation of centralized and risk-based monitoring, with a further 50% in partial implementation, pilot, or exploration stages. The picture that emerges is one of varied momentum, with adoption progressing but in different ways and at different speeds.

Supportive regulation, including the International Council for Harmonisation (ICH) good clinical practice E6(R3) guideline,² and demonstrable gains in data quality, integrity, and efficiency are accelerating that trajectory. What matters for capacity planning is not simply whether these models exist within an organization but how they change where effort is directed.

What the data say about distributed oversight

Tufts reports that under risk-based approaches, onsite monitoring visits now occur at roughly half to a quarter of their former frequency, adjusted by site risk.¹ This data point risks a critical misreading: that risk-based monitoring means less monitoring overall. This is not necessarily true.

Instead, monitoring activity is redistributed across a broader, cross-functional ecosystem. Reduced visit duration and/or frequency at lower-risk sites frees time and resources for more intensive monitoring where it is genuinely needed. Remote CRAs and central monitoring units absorb a larger share of routine data review and trend analysis, whereas onsite visits concentrate on areas that require direct engagement.

Central monitors can oversee far more sites, with Tufts reporting an average of 57 per monitor.¹ Their role centers on aggregated data review to detect patterns, assess risk signals, and direct targeted follow-up. This shift changes capacity requirements downstream. Rather than distributing workload evenly across field monitors, organizations need sufficient analytical capability to review data at scale and translate insights into targeted action at relevant locations.

The rise of data-driven central monitoring

Central monitoring teams shoulder a growing volume of analytical work across entire studies to enable signal detection in risk-based monitoring (RBM). This includes aggregating and reviewing data, managing trends, and coordinating follow-up across regions to provide risk-informed insights to help CRAs prioritize.

These responsibilities demand different skills than traditional field monitoring. Data literacy, cross-functional communication, and governance play a larger role. The Tufts report¹ shows that many sponsors rely on central monitors to review data at defined intervals, which makes well-designed workflows for prioritization and escalation essential, not optional.

Central monitoring does not replace traveling monitors. However, it does change how their time is used. Effective capacity planning recognizes the interdependence between central insight and field execution.

Redefining CRA expectations

The Tufts data shows that under risk-based models, CRAs monitor more sites than before (though this figure could be confounded by the parallel CRA shortage, hence higher site loads). In some regions, site assignments increase by as much as 40%, yet Tufts shows CRAs are reporting that they have sufficient time to complete onsite activities.¹ These findings suggest that capacity gains come from prioritization of risk, potentially coupled with the use of technology, not from doing less.

As sponsors move toward reduced SDV/SDR, a larger share of onsite time is now spent on other activities targeting inspection readiness. Depending on region, one-third to two-thirds of onsite effort focuses on protocol clarification, avoidance of deviations, issue prevention, training, and relationship management.¹ This aligns closely with findings from ICON’s own 2025 industry survey,³ in which sponsors report placing greater value on CRAs’ ability to identify risk, support sites, and resolve issues proactively rather than on exhaustive data checking. Sites and CRAs spend less time supporting routine verification and more time addressing substantive questions about protocol execution and data quality.

Supporting CRAs in shifting scopes

Although sponsors report improvements in data quality, integrity, and monitoring efficiency, CRA perceptions of data quality outcomes remain mixed, per Tufts survey data.¹ This divergence suggests that capacity gains depend on enabling CRAs as much as on the individual monitoring model design.

Layering our own survey insights³ with the Tufts findings,¹ we see 3 key levers for enhancing CRA efficiency within RBQM models. First, performance-enabled CRAs need clear role expectations, training suited to hybrid monitoring, and confidence in risk-based execution of their role. With evolving expectations, CRAs need targeted training and updated key performance or critical-to-quality indicators that reflect the new ways of working. Softer skills such as communication, collaboration, and relationship building are equally important, as are critical thinking skills, helping CRAs navigate site needs, support enrollment, and contribute to study‑level decisions.

Second, technology-enabled solutions that reduce administrative workload allow CRAs to focus on true oversight rather than routine tasks. With more sites and wide ranges of data sources, mobile tools and integrated artificial intelligence–enabled platforms can help streamline planning, reporting, and documentation to smooth operations.

Third, strong data workflows and governance are more important than ever. Timely access to structured data supports preparation and prioritization across onsite and offsite work. Intuitive systems help CRAs resolve issues faster and reduce the likelihood of downstream delays.

Investing in these elements translates RBM’s redistribution of effort into a sustainable capacity that equips CRAs to operate as site partners, data stewards, and oversight specialists.

A redistribution, not a reduction

Modern monitoring models reshuffle oversight efforts. They concentrate critical-to-quality factors, sharpen focus, and rely on coordination across teams.

The Tufts CSDD data provide a useful snapshot of this transition, and ICON’s own survey findings add texture by overlaying the evolving expectations of CRA roles. Together, these findings suggest that monitoring capacity depends on intentional, risk-based distribution of effort rather than uniform expansion or contraction. And although Tufts data show wide variation in adoption, particularly between large and small sponsors, RBM is our collective future.

As adoption of hybrid risk-based models increases, organizations that align capacity planning with monitoring design will be better positioned to support quality, efficiency, and site engagement as trials grow more complex.

References

1. Harper B, Do H, Smith Z, Getz K. Majority of companies report full or partial adoption of centralized and risk-based monitoring. Tufts Center for the Study of Drug Development Impact Report. 2026;28(1):1-4. Accessed September 9, 2026. https://csdd.tufts.edu/publications/impact-reports
2. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH Harmonised Guideline: Guideline for Good Clinical Practice E6(R3), Step 4 version. January 6, 2025. Accessed September 9, 2026. https://www.ich.org/page/efficacy-guidelines
3. Yeardley H, Cogley D, Matthews A, Perry J. More Than Monitoring: How Modern Monitoring Paradigms Impact CRA Roles and Expectations. ICON. 2025. Accessed September 9, 2026. https://www.iconplc.com/insights/transforming-trials/more-monitoring

About the author

Helen Yeardley is the executive vice president of clinical operations at ICON.