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The Gateway to Study Startup (SSU) Optimization: Performance Metrics

by Craig Morgan

Standardized performance metrics that are actionable drive transparency and process improvement.

White Paper: The Gateway to Study Startup (SSU) Optimization: Performance Metrics

The sharpening focus on quality management is fueling greater use of standardized metrics to optimize clinical trial performance. Stakeholders are embracing this trend through growing adoption of cloud-based technologies, such as clinical trial management systems (CTMS), source data, and the electronic trial master file (eTMF). The information they generate is flowing into data analytic tools, and with this capability, standardized metrics are gaining mainstream status. But just because data from disparate sources can now be aggregated does not necessarily mean that this information or the resulting metrics are actionable, or can identify risk proactively. That's why targeted performance metrics that measure the many details of clinical trial operations are essential. And for study startup (SSU) in particular, performance metrics are critical, given that it is one of the most complicated parts of clinical trials and one of the most crucial to meeting site activation timelines and study completion milestones. Yet, its performance scores lag other stages of clinical research.

What makes SSU so challenging? For starters, it has no standard definition, but it generally refers to the sequence of steps performed prior to the beginning of a clinical trial. SSU tasks typically involve country selection, pre-study visits, site selection and activation, regulatory document submission, contract and budget execution, patient recruitment efforts, and enrolling the first patient. Because these activities are performed across numerous smaller internal and external organizations, as compared to study execution, they can easily disrupt study timelines.

Each of the SSU tasks is composed of multiple steps, and performance metrics are needed to evaluate them at a granular level, all in an effort to spot bottlenecks and identify processes ripe for optimization. Contract execution, for example, contains many sub-steps at the site level, starting with Site Contract Language Ready and ending with All Contracts Executed.

The information revealed by tracking all the sub-steps in near real-time fashion is in stark contrast to what is provided by so-called regulatory metrics, which are generated from data entered into the TMF, and are auditable. Typically, regulatory metrics are "high level", meaning that they do not measure each of the sub-steps, but rather track all-inclusive milestones, such as the planned date for All Contracts Executed. There is a lack of business intelligence with this approach, forcing stakeholders into reactive mode, since the information does not pinpoint which sub-steps are problematic and only becomes available after milestones are achieved.

Download our white paper The Gateway to Study Startup (SSU) Optimization: Performance Metrics to learn about how standardized performance metrics designed to measure various sub-steps help to optimize the SSU process by providing transparency into bottlenecks that could derail study activation, cycle times and budgets.

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