Getting There Faster: Enhanced Productivity via Advanced Risk Management
How to Grow Without Growing
We worked with a large pharma organisation to plan the evolution of its development operating model to accommodate a growing asset pipeline and its expansion into new therapeutic areas. The organisation had already increased headcount under the existing operating model but, projections indicated it would still need to double in size within three years if the same processes, structures and governance were applied only to studies already planned, without even considering potential acquisitions.
The High Cost and Practical Impossibility of Risk Elimination
Our team worked with stakeholders across development functions to assess core development capabilities and how people, processes and technology were being deployed. The initial assessment focused on identifying quick wins for known critical pain points and accelerating or prioritizing a number of in-flight initiatives, such as protocol simplification to reduce the volume of data collected and processed and reducing the number of hand-offs in data cleaning.
A deeper dive revealed that the legacy approach to clinical development built into the operating model was heavily indexed on risk elimination. It was characterised by excessive redundancy within and across functions, over-processing, and an inefficient use of the technology stack. Compounding this was the discovery that, despite this heavy focus on risk elimination, the organisation's data quality remained below industry standards.
Modern Risk-Based Approaches to Enable a Capacity Uplift
By aligning leaders around a modern integrated risk-based approach to pharma development and quality management, our team demonstrated how the end-to-end clinical trial process could be re-engineered to deliver a 3X capacity uplift. The productivity enhancement relied upon the application of risk-based approaches throughout the entire development life-cycle, from design to reporting. Advanced analytics support the work of drug developers, automating data review and building systems to enable trial monitoring that reacts in real time to trends in the “data that matters” rather than the current retroactive and “event-triggered” legacy monitoring approaches.
