Analytics and Metrics Help Pinpoint Costs of Study Startup

Craig Morgan
Head of Marketing, Study Startup

The clinical trials sector has done a good job tracking costs associated with study execution, but costs linked to study startup are conspicuously absent.

A quick look at industry research confirms this gaping hole in cost accountability. For Phase III clinical trials, for example, it is widely recognized that total costs are substantial, ranging from an estimated $11.5 million to $52.9 million, depending on therapeutic indication. Upon closer examination, it is apparent that identified costs are almost exclusively tied to study execution, and rarely include details on the hefty costs incurred early on, namely those tied to study startup and site overhead. In fact, data from an often-quoted 2014 study published by the Department of Health and Human Services (DHHS) indicate that unaccounted for costs may represent upwards of 43% of the total cost of conducting a clinical trial.

With nearly half of clinical trial costs unaccounted for, including known administrative costs, and much of those linked to study startup, it is telling that the industry is finally moving toward identifying those costs with the help of purpose-built tools that can determine, with a high degree of accuracy, when it is time to stop identifying sites. Integrated workflow-driven solutions for study startup, allow stakeholders to gain insight at a granular level into how a study process is unfolding and the associated costs, which empowers life science organizations to move beyond milestones rooted in sequential processes in favor of a faster-moving parallel method to streamlining startup timelines.

An effective study startup process is fundamental to the overall operational success of a clinical trial as it is the most impactful phase of the trial’s lifecycle. The impact comes from a thoughtful upstream study startup approach that influences downstream performance through the use of purpose-built workflows. Essentially, workflows streamline and organize operational aspects in real time, enable the collection of data and metadata, help identify problems quickly, and allow detailed study startup costs to be calculated.

This represents a sea change as the DHHS study shows study startup costs as vague and incomplete, basically reflecting that traditionally, these costs have not been clearly identified. Instead, they have been overlooked or lumped into a handful of categories, such as the cost of IRB approvals, site recruitment and retention costs, and patient recruitment costs. As a result, critical multi-step tasks ranging from pre-study visits through to contract or budget execution have relatively unknown costs although they embody almost 60% of study startup cycle time.

It is noteworthy that workflow solutions align with the 2016 Good Clinical Practice (GCP) guideline from the International Conference on Harmonisation ICH-GCP E(6)R2, which calls for a heavy emphasis on quality from the beginning of a study, and extending it throughout the life cycle. Specifically, the guideline has an entire section dedicated to Quality Management, which states that to achieve quality, efficient tools are essential for proper data collection and decision making.

Download our white paper to learn how by embracing a systematic, data-driven approach, it is possible for metrics to identify more accurately the best sites, steps causing delays, the associated costs, and why this is happening. This change in approach will continue to highlight how improving the study startup process is a great opportunity, yielding a major impact on quality, timeline and overall cost of clinical trials.

Read the associated peer review article in Applied Clinical Trials.

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