A practical approach to RBM
By: Nimita Limaye | September 13, 2016
Risk-based monitoring can add considerable benefits to the research environment, but companies need help figuring out how to implement these programs and track results.
Risk-based monitoring (RBM) remains a key topic for debate among clinical data management professionals. Tantalizingly, while it represents an alternative path to clinical development, the perceived complexity means it has probably not been implemented as effectively as it could and should.
As a reminder, RBM is a more proactive approach to risk management, in which sponsors allocate monitoring resources based on the level of risk identified for a specific trial or site, allowing monitors to focus on preventing or mitigating risks to data quality and patient safety, rather than spreading monitoring resources evenly across every site regardless of their performance. This drives efficiency in the risk management process, and supports faster and more informed decision-making, which can improve patient safety, increase study quality and enhance trial management.
The quality and cost benefits of RBM have been recognized with strong support from regulators and industry organizations. The U.S. Food and Drug Administration (FDA) released a guidance for adopting RBM in 2013 to help sponsors address some of the new challenges in clinical trial oversight, and to take advantage of the increasing use of electronic systems for alternative monitoring approaches. Several industry consortia are also supporting the adoption of RBM including Transcelerate, which launched the Risk-Based Monitoring Initiative in 2012 to help improve the quality and efficiency of these programs.
The support given by these industry stakeholders reinforces the value that RBM can potentially bring to the trial oversight environment, yet many biopharma companies have been slow to adopt an RBM strategy, in part due to concerns about how it will work, and how to implement an effective program.
At the 2016 Society for Clinical Data Management’s (SCDM) annual conference, I will be joining a panel of experts to address some of these concerns. My speaking session will focus specifically on ‘Optimizing Clinical Data Operations Strategy for the successful implementation of RBM.’
In the session, we will move beyond discussing what RBM is to exploring the fundamentals of how to implement it successfully within the trial environment. Executing RBM in clinical data operations (CDO) is still nascent and tends to be focused primarily on data review. While CDO does have a significant role to play in centralized monitoring, it is important that a holistic description of the process and involvement of other functional stakeholders is adopted to ensure greater understanding and better preparation for the use of RBM. At the end of the day, a successful RBM program hinges on the ability to look at multiple critical data points related either to the site or the subject, in order to make better informed decisions. I think leaders in the clinical data management space have been doing this all along and this is a place where they have an opportunity to demonstrate leadership.
Throughout the session we will talk about the challenges sponsors face when implementing these programs, from choosing the right trial for pilot projects, to engaging all key stakeholders, to leveraging effective analytics tools and to efficiently measuring the success of the risk assessment process. Due consideration needs to be given to each of these issues for RBM to be a success.
Beyond having a strong plan and infrastructure, we will explore the culture change that must be embraced as part of the program implementation. To be effective, RBM requires support from executive leadership and key champions within the organization, and a well-defined change management plan to make RBM an integral part of the organization’s commitment to quality and patient safety. It is critical that these pieces are in place so that early projects are implemented successfully giving champions an opportunity to demonstrate, through analytics, whether they were able to identify high-risk sites in advance, and reduce the level of site risk by identifying discrepancies.
The transition to RBM may present some initial challenges, but it is an inevitable step in the evolution of risk management. There is growing awareness among healthcare industry stakeholders of the value that RBM brings to the trial environment when one recognizes the right scenarios where it should be implemented. Working with experts to plan and implement an RBM model can help biopharma companies ensure they have all the key players, data, analytics tools and technology in place for these programs to succeed.