Clinical Researcher—October 2025 (Volume 39, Issue 5)
ON THE JOB
Sas Maheswaran, MSc; Ken McFarlane
As clinical trials become more complex, leading sponsors are shifting the focus of their clinical research associates (CRAs) from traditional compliance monitoring to proactive, data-enabled site support and oversight. Skill development, structural changes, and the use of advanced analytics are modernizing the CRA role—and the way we monitor clinical trial sites.
A recent poll found six in 10 organizations report high or moderate maturity in data-driven monitoring adoption.{1} The same poll revealed the biggest barrier to adoption is a lack of appropriate leadership buy-in to drive new operating models.
With the right training and technology and effective change management, CRAs today can act as strategic partners, using centralized data to detect issues earlier, support sites more effectively, and drive trial success. Getting there means operationalizing a data-driven approach, empowering CRAs, and shifting the model to data-enabled site monitoring.
Technology—Enabling a Shift in Focus
While CRAs have historically emphasized ensuring data meets ALCOA++ principles{2} and the site’s protocol and regulatory adherence, the modern CRA is poised to play an evolving role in driving site performance and improving study outcomes. This change has been enabled by modern data analytics tools which provide CRAs with a proactive view of data issues at a site level. Coupled with technology pushing certain administrative tasks into centralized review teams, this allows CRAs to focus more time on critical data elements, while also providing sites with the support they need to perform.
Technology has also enabled a shift in the focus of onsite visits, allowing CRAs to spend more time with the site avoiding risks/issues before they occur. With centralized review taking care of things like safety case reconciliation, data discrepancy resolution, and informed consent tracking, there is more time to address such important items as site training needs, protocol compliance support, enrollment support, and validation of equipment and the investigational product.
The reduction of source data verification (SDV) and shift to focus on targeted source data review (SDR) have also increased the amount of time CRAs have available to support sites with reconciling adverse events/serious adverse events (AEs/SAEs) and concomitant medication issues, attending to protocol deviation review, and fulfilling any re-training needs to ensure better compliance with the protocol.
New technologies have enabled global adaptability with the creation of flexible site monitoring models to ensure local regulations are met. We can now use data to drive the thought process about how we scale a process and how the monitoring process should be conducted globally.
Operationalizing a Data-Driven Approach to Site Success
Defining what a data-driven approach looks like is crucial for successful operationalization. There are three key considerations for data-driven approaches:
- Providing Consolidated and Trusted Analytics
This means both onsite and centralized monitoring teams are prepared with better contextual information prior to taking any action. The emphasis here is on “contextual” information. Data-driven decision making requires us to know as much information as possible to drive the right conclusions, without distraction from unnecessary noise.
- Proactiveness and Priorities
Consider how you can drive the biggest impact with focused efforts and eliminate non-value-added activity. When we use data analytics and reporting to focus on what matters most, we improve our ability to uphold compliance and submission readiness. In the ICH E6(R3) Guideline for Good Clinical Practice,{3} the regulators also have emphasized proportionality. Are we approaching the critical vs. less critical with the right controls, oversight, and scrutiny to ensure the best data outcome possible?
- Consider the Site Burden
What helps sites to perform and what prevents them from performing? Traditional activities like SDV do help sponsors get a leading indicator of possible misunderstanding of data requirements or data issues, but they’re not as impactful as performing SDR and other prioritized activities that help sites avoid entry issues from the start. It’s also worth considering the potential impact of changes in reduced onsite monitoring approaches. For example, if a site is performing well and seeing less onsite time from their CRA, we need to consider how to replace this with clear communication and support. Again, technology has a role to play here with the potential for virtual visits, remote visits, and other modalities under design.
Empowering CRAs with Analytics Tools and Insights
Moving from traditional spreadsheet approaches to centralized data-driven decision-making processes and systems empowers CRAs with the context needed to help them identify and follow up on the issues which will have the biggest impact on site improvement.
Instead of waiting for routine study-team meetings or sets of disparate lists from each team, CRAs can be one step ahead by prioritizing visits based on what the whole dataset is telling them. This might include potentially critical-to-quality issues, including under-reporting of AE/SAEs at sites, missing data, unexplained anomalies, late data entry, and possible duplicate or hard-coded data trends/issues.
The new era of site monitoring requires a mindset shift from passive post-entry review to CRAs becoming partners in site success prior to data entry.
A data-driven approach to site monitoring can empower CRAs to act with proactiveness and precision, while demonstrating empathy for site coordinators and managers.
Practical Tips for Shifting the Model
When shifting to a data-driven model, teams should always start with outlining their objectives and key results. This will allow the team to decide what enabling changes will deliver the desired results. This is not just about technology, but also about the processes and training required. CRAs need to be trained to interpret data effectively or supported by a central team which can provide them with the necessary interpretation and prioritization. This will allow them to focus on the most impactful issues and avoid overwhelming both sites and CRAs with signals. Important points to consider include:
- Clearly defining the purpose of your shift and your success measures. Establishing the correct objectives and key results and their associated key performance indicators is key to driving a program that evaluates the outcomes being delivered and confirming you’re on the road to meet your initial stated goals.
- Demonstrating the value of your model shift to sites. There can be a perception that site visits will mean a list of menial or non-value-added activities. However, if CRAs are visiting sites as data-driven enablers, you can start to build effective partnerships, increasing site success and quality and reducing site burden by eliminating non-value-added administrative activities.
- Planning for resistance and spending time and resources explaining the why, what, how, when, and who of any paradigm shift. Addressing common concerns is crucial. For example, often concerns are raised that reducing SDV will lead to data quality issues, or that the reduction in onsite monitoring visits will also reduce data quality and integrity. It’s key to communicate how data issues/trends/anomalies are now being evaluated and detected in improved and different ways, and the focus of the CRA is shifting to a more proactive approach. The two combined should prove to be more efficient and drive better quality, a concept that all study team members can agree is critical in today’s environment.
Conclusion
Transforming the role of the CRA and shifting to a data-driven monitoring approach is vital to stay ahead of the curve in modern clinical trial oversight.
This shift requires change—at an individual level, at an organizational level, and at an industry level. We need to empower CRAs with the training they need to utilize data more effectively and ensure humans stay in the loop as technology advances. We need to drive effective change management at an organizational level by clearly demonstrating the benefits of data-driven approaches. Finally, as an industry, we need to update the CRA best-practice operating model.
Those organizations which lead the change will define what excellence looks like for the monitoring of the future while also elevating their own impact.
References
- https://www.youtube.com/watch?v=oGbzdHLdKu0
- https://www.quanticate.com/blog/alcoa-principles
- https://database.ich.org/sites/default/files/ICH_E6%28R3%29_Step4_FinalGuideline_2025_0106.pdf
Sas Maheswaran, MSc, is a Vice President of Strategic Consulting at CluePoints with 18 years of clinical development experience at sponsors, sites, and software vendors. He is responsible for helping sponsors and contract research organizations (CROs) operationalize artificial intelligence transformations across the clinical development spectrum, with a particular focus on overcoming challenges in realizing risk-based quality management as a cross-functional obligation.
Ken McFarlane is a Vice President of Strategic Consulting at CluePoints who has spent the last 24 years in the clinical trial research and development space, including 12 years in various clinical operations roles within sponsors and CROs with a large focus on monitoring, project management, and oversight. He has also worked for clinical trial technology vendors, where he concentrates on innovating and streamlining processes to benefit sponsors, CROs, sites, and the patients they serve.
