Implementing Risk-Based Monitoring in Your Clinical Trial Processes

Over the past few years, we’ve begun to see a shift away from traditional site monitoring and more movement toward risk-based monitoring (RBM) programs. Instead of following a traditional monitoring plan in which a clinical research associate (CRA) routinely visits a site – approximately every six weeks – to complete source data verification (SDV) each time, research organizations are adopting RBM in order to significantly reduce the time spent on SDV, as well as to improve data quality and ensure greater safety of study participants.

RBM hones in on high risk areas of a study and facilitates the identification of all key risk indicators (KRIs). Next, research teams formulate a plan consisting of notifications, responses, and rules that best addresses these areas of risk. As explained in some research conducted by Transcelerate, risk-based monitoring programs should excel in the areas of risk assessment and cross-functional risk mitigation planning, data integration, risk indicator data review, and risk and issue tracking, management, and analysis. RBM programs of this nature will also encourage collaboration and communication amongst team members.

One road block in an industry-wide implementation of RBM, however, is the lack of standardization behind this methodology – particularly surrounding terminology. A true definition of risk-based monitoring has yet to be solidified, and because RBM takes a more centralized approach, it is therefore often referred to as centralized monitoring. As another prime example, if one sponsor is using one set of terms and another sponsor uses a different one, and the CRO uses yet another, that will cause confusion and possible errors. It will take effort on behalf of research organizations, technology providers, the FDA, and other regulatory bodies alike to successfully standardize this ever-changing concept. Yet, the benefits of risk-based monitoring have the potential to make great improvements to the clinical trials landscape: cost savings, increased efficiencies, more accurate data, and greater patient centricity.

With this in mind, we’re continuously optimizing our system’s RBM capabilities to best fit the needs of research professionals. Currently, TrialKit can support any dynamically-changing risk-based monitoring (RBM) models, via API, that are created outside the system in analytical or biostatistics reporting. In other words, TrialKit will enforce constantly changing monitoring rules. TrialKit’s mobile app is also equipped with a risk-based monitoring function that can be integrated into the clinical workflow and make it easier to manually define RBM rules. This app offers several options that make it very flexible for implementing an RBM model by participant, site, visit, study, or any combination of the same. SDV can happen at the form or field level, enabling research professionals to determine specific fields in need of monitoring at defined review levels, and override those fields on a subject, site, or population-based criteria.

To learn more about risk-based monitoring processes and leveraging technology tools to accomplish it, don’t hesitate to get in touch with us.

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