The First Step Towards Study Start-up Transformation: Business Process Optimization
In the first blog in our series, we outlined how organizations can take a stepwise approach to operationalizing metrics and achieving data-driven study start-up. In this blog, we’ll dive deeper into the first step – optimizing business processes and selecting modern study start-up technology.
Study start-up is highly complex with many milestones and dependencies – and errors during the start-up phase can have downstream impacts on study conduct. Excel trackers just don’t cut it when start-up teams are trying to manage country-specific intricacies, ever-changing regulatory requirements, and protocol amendments.
As Lorena Gomez from AbbVie shared, most companies – from global top 20 pharma to emerging biotechs – struggle with work effort duplication, process redundancy, and lack of visibility. While technology can drive efficiencies, it’s critical for organizations to avoid configuring new technology with suboptimal processes. Inefficient business processes translated into a good technology will immediately devalue, or invalidate, that technology. As companies embark on a study start-up transformation journey, conducting workshops with subject matter experts and end-users to identify pain points and inefficiencies is a crucial first step. Study start-up specialists can then look at processes holistically and collaborate cross-functionally to drive change in key areas and eliminate silos. Organizations that are most successful with data-driven study start-up are those that extend beyond just functional steps and account for holistic business processes. For example, considering the impact protocol authoring teams have on the site identification team and their processes will help reduce the number of steps and handoffs, and eliminate visibility barriers.
These optimization workshops can be done in parallel with the evaluation and selection of new technology. Harmonizing processes in a single platform that’s unified with other clinical applications simplifies the IT landscape and enables a cohesive experience for end-users. Reducing the number of systems and integrations also allows users to do their work, get visibility to KPIs, and analyze data in one place. If data exists across multiple systems, the quality and reliability of that data is immediately in question.
As part of process optimization and technology evaluations, study start-up teams should assess external data sources and determine how much of that data is actually used to drive start-up planning and execution. If the data is unreliable or not used to inform key start-up activities, eliminate that data source. Data integrity and value are far more important than integrating data from multiple systems.
Companies need to optimize and streamline processes before adopting technology to truly become data-driven and accelerate study start-up. Watch this 3-minute video for a summary of the stepwise approach and stay tuned for the next blog, where we’ll discuss how to standardize measurements.