goBalto, a provider of cloud-based clinical study start-up solutions, announced the inaugural release of the ChromoReport, a quarterly analytical discussion on study startup (SSU), representing over 70% of clinical trial sites in phase II & III of the Top 25 pharma companies.
The sharpening focus on quality management is fueling greater use of standardized metrics to optimize clinical trial performance. For SSU, 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.
SSU refers to a series of steps performed before a clinical trial begins. These typically include activities such as country selection, pre-study visits, site selection and initiation, regulatory document submission, contract and budget negotiations, and enrolling the first patient. Since each of these steps has multiple components, delays in study timelines are commonplace.
In the search for the perfect investigative sites, as well as the ability to manage the ongoing progress of a study, sponsors and CROs attempt to understand their sites and their team's productivity using metrics and cycle times.
Jeff Kasher, president of Patients Can't Wait, and formerly vice president of Clinical Innovation and Implementation at Eli Lilly, said, “Turning big data into big insights requires analytics that are actionable. Performance metrics must be data-driven, standardized across studies, indication and therapeutic areas, and timely. They must also, importantly, facilitate a forum for discussion.”
“Utilizing overarching cycle times within your own company or team is only the beginning of creating the best results for your company. To truly begin to move toward 'best in class' or 'industry leading,' you need to consider your processes and cycle times from a bottleneck perspective, as well as industry standard,” said Jae Chung, goBalto’s founder and president. “By thoroughly understanding the data you will support your teams to make the best decisions to improve study startup, ensuring that the goal of getting protocols to patients faster is achieved, which represents the first step in competing a study on or ahead of schedule.”
Clinical trial metrics and cycle times have proven difficult to collect and evaluate across various studies, countries and sites. The lack of standardization in the definition of cycle times and metric starting points have confounded the ability to aggregate mass sets of data and determine the baseline, as well as the final results, which can support selection and the ongoing management of the SSU process.
“As the industry leader in study startup, we are well positioned to standardize across multiple data sets, to provide a single view of the real-world metrics and cycle times,” added Chung. “Each company has their own nomenclature for their processes and in some cases each group within a single country may have a different interpretation of what constitutes the completion of a milestone or cycle beginning/ending. These differences need to be managed if we are able to look at a diverse set and explore the cycle time differences across them. Our implementation of global milestones, standard workflows and our increasing volume of data enables us to showcase the evolving trends in study startup.”
Although overall cycle times can be an indicator of process waste and team inefficiencies it does not inform you of the cause of the problem nor what is driving decreases in cycle times. Organizations need to be able to analyze their data to determine the cause and effect of process, staffing, and regulatory changes on their cycle times.
The information revealed by tracking all the sub-steps in real-time fashion is in stark contrast to what is provided by 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. 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.
“We are able to see the subsets of the overall process, which can help determine the cause of cycle time change,” added Chung. “Engaging study teams with metrics that they use every day can improve both performance and quality. Ultimately, by optimizing study startup processes organizations can provide actionable intelligence about clinical trial execution.”
The ChromoReport is complimentary and can be downloaded from goBalto's resource center.