There is long-standing recognition that Risk-Based Monitoring (RBM) is essential to ensuring the safety of your patients and the success of your clinical trial. Data quality and patient safety are also at the core of quality by design (QbD) initiatives, which focus on identifying key risks to subject safety, data quality and GCP/regulatory compliance, setting the foundation for RBM implementation.
RBM itself combines the tasks of remote central monitoring, to identify trends or outliers by viewing aggregated, study-level data; remote monitoring, to assess potential site issues regarding data entry timeliness and performance; and on-site monitoring, to assess investigator involvement and GCP compliance. These are all part of an overall quality management and mitigation system that is recognized in the latest International Council for Harmonisation (ICH) good clinical practice (GCP) E6 R2 guidelines, which encourage the implementation of improved and more efficient approaches to clinical trial design, conduct, oversight, recording and reporting.
Despite the enhanced implementation tools for RBM that are now available, many companies are still using standard/basic business intelligence (BI) tools (e.g., ad-hoc reports, dashboards/scorecards, spreadsheet integration, data visualization) to support their RBM needs. While a number of these tools are helpful for certain aspects of clinical operations, it can be difficult to retrofit these tools for the purposes of RBM.
Most BI tools provide pretty charts and graphs, but they only provide static views of data and often rely on multiple spreadsheets to fill in gaps. Considerable time and effort are required to compare and reconcile data from many data sources—time that could be better spent in conducting the trial. However, many of these tools are already available within the organization, and it can be perceived as easier to continue with "what you know" than to plan and implement a new RBM system. Indeed, reported challenges to successful RBM include a lack of technology and a clear plan.
RBM inherently requires a dynamic view of the potential risks in a trial for rapid issue identification and resolution. Because of the recognition of the limitations of traditional BI tools, purpose-built tools for RBM are becoming more readily available. An example is Bioclinica's ClearSite system, which, in addition to providing useful and attractive charts and graphs, is also SMART, powered by machine learning and predictive analytics using algorithms and decision support models that assist in making increasingly more informative decisions based on real-time data. In addition, the ClearSite system communicates effectively with downstream systems for action item tracking and adjustments to monitoring recommendations (i.e., on-site visit schedules or source data verification strategies) based on actual site behavior.
With these advanced, SMART RBM/quality management tools, you have greater trial oversight, higher quality data and greater safety for your patients. They also free up valuable time for research staff to focus on more important tasks than data aggregation and analytics. While the move to advanced analytic technology tools can seem like a large leap, the investment ultimately helps you ensure that you are proactively managing risk appropriately and utilizing your resources wisely.
If you have any questions about RBM or ClearSite, I'd be happy to talk with you. Feel free to reach out to me in an email at firstname.lastname@example.org.