This is the third post in a three-part series on quality implications of RBM.
Editor’s Note: This is the third post in a three-part series. You can read the first post
Alsumidaie’s report on the BMS pilot project referenced another interesting
The post also stated, “While cost savings is important, the impact that RBM has on data quality is a more important outcomes measure.” Certainly, data quality in clinical research is more important than costs savings. However, when assessing RBM performance, there is no need to choose between reducing costs and increasing data quality. A well-implemented, fully-integrated approach to RBM can deliver – indeed, has delivered – substantial cost savings while simultaneously increasing data quality.
We know about simultaneously increasing data quality and reducing costs with RBM from direct experience in a 3,386-subject global phase III study. Over a 27-week period, SDV% decreased and data quality increased (error rate decreased).
We realized 76% cost savings in the same study – triple the percent savings that Amgen anticipates from RBM on a Functional Service Provider model.
How did we achieve costs savings of 76%? At the most basic quantitative level, we achieved such savings primarily through substantial reductions in SDV: a 75% reduction for non-critical fields and 12% for critical fields.
What made savings on this scale possible simultaneously with an increase in data quality is a fully integrated turnkey RBM solution that is individualized for each study. This solution integrates:
RBM Success Is to the Agile
In developing our RBM solution, we were unencumbered by many factors common in Big Pharma. We faced no high-level management imperatives such as reducing headcount and outsourcing as many jobs as possible. We were free to focus on creating the most effective integrated RBM teams for each study rather than dividing labor between central employees with an analytic role and outsourced field staff with more mundane responsibilities. We did not have to implement RBM within the constraints of technology infrastructure that provides little flexibility, represents a large corporate investment and would take years to replace.
By contrast, we were free to define processes and build technology with a single goal: to allow us to optimize RBM for each individual study that we execute. What is more, the cultural transformation that seems to be an obstacle to RBM adoption in many places is not an issue for us. Our culture is all about innovations that enable more efficient trial management based on access to timely information and performance metrics. Our RBM approach evolved as a natural extension of how we think studies should be run in the first place.
We realize that most Big Pharma companies today operate within frameworks that bar the type of integration that has made our RBM approach so productive. However, Big Pharma might wish to reflect on whether their approaches to clinical development have lost sight of the paramount importance of proactive, data-driven, individualized study execution involving integrated multidisciplinary teams. Sacrificing such a mode of execution and such integrated teams is a high price to pay for the theoretical advantages of new clinical operations models.
Unlike Big Pharma, most biopharma companies are free to outsource studies for execution by fully integrated operational teams that have successfully completed the transition to risk-based monitoring. Such companies can enjoy an immediate leap in the efficiency of monitoring – and a substantial competitive advantage over Big Pharma in bringing new drugs and biologics to market.
This post first appeared on the Health Decisions blog, Trials Without Tribulations
