Deep Knowledge of Study Startup Points Data In the Right Direction
Purpose-built SSU solutions track clinical trial operations using much needed standardized performance metrics
The current state of metrics, whereby massive volumes of data are generated during clinical trials, are woefully inadequate at helping stakeholders spot risk factors and bottlenecks that can disrupt cycle times and budgets. This is due to the inefficient ways in which operational data are captured and analyzed, often relying on outdated methods such as paper, shared file drives, and Excel, which lack much needed project- and risk management functionality.
These shortcomings are particularly acute during study startup (SSU), a phase that is widely regarded as complicated, slow, and in need of better operational tools. It is also pivotal to successful clinical trial operations. Anxious to improve SSU operations, stakeholders are embracing solutions with automated workflows that guide team members through the many steps involved and provide alerts for tasks needing attention. These tools are purpose-built, show a deep understanding of SSU, and enable users to comply with regulatory metrics at the country and site levels. They also allow users to develop performance metrics, which are the basis for predictive analytics, and are critical to building a dynamic atmosphere of continuous improvement.
Download our white paper "Deep Knowledge of Study Startup Points Data In the Right Direction" to learn about how industry-proven tools for SSU are key to better operations in the increasingly global realm of clinical trials.
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