Standardized performance metrics that are actionable drive transparency and process improvement
goBalto, announced today the September release of the ChromoReport, a quarterly analytical discussion on study startup (SSU), representing over 70 percent of clinical trial sites in phase II & III of the Top 25 pharma companies.
As the debate over reforming the nation's healthcare system rages on, there's at least one goal all sides can agree on: bring costs down. Reducing the amount of money and time spent on clinical trials is a priority, as they represent the biggest cost of drug development.
The status of clinical trials continues to stymie industry stakeholders anxious to rein in the cost of product development and adhere to tighter timelines. Despite intense pressure to speed development, mounting evidence documents ongoing inefficiencies tied to complicated protocols, globalization, and old-school paper-based processes, driving clinical stakeholders to embrace technologies that are finally moving the needle.
Study Startup (SSU), encompassing all steps required to initiate a study, is a very complex and recognized bottleneck whose functions are performed by multiple people in multiple locations at the sponsor, Contract Research Organization (CRO), and site levels, all of whom need to communicate and share data.
A critical question facing many sponsors and CROs is whether another system to support SSU is really needed. It's a fair question in the clinical world where the urgent need for better execution of clinical trials has led to a proliferation of technologies, namely the clinical trial management system (CTMS); the electronic trial master file (eTMF); electronic data capture (EDC); and others. Each of these solutions focuses on specific pieces of the clinical trial continuum, yet, they overlook aspects unique to SSU, a multifaceted (cross clinical trial technologies and countries) process that continues to stumble and stall clinical trial timelines.
To this end, an SSU application is a critical additional to the ongoing automation of clinical trials, highlighting the value of technology that streamlines bottlenecks allowing stakeholders to better adhere to established timelines and budgets, cutting runaway costs and speeding the delivery of life saving medicines to those in need.
To make this happen, a dedicated SSU system integrated with the other clinical trial technologies is essential, but what about the organizational structure of clinical operations teams? Do centralized groups outperform non-dedicated groups?
In a recently completed comprehensive study conducted by Tufts Center for the Study of Drug Development (CSDD), Start-up Time And Readiness Tracking (START) II, 2017, found that European respondents reflect slightly higher percentage who work in centralized groups compared to U.S. and Canada but the difference was not significant. Moreover, regardless of organization (Sponsor vs. CRO), centralized groups have longer cycle times, however, the differences were again not statistically significant.
On average, for a given multicenter study, sponsors report that 28% of the sites that they engage are 'new' relationships; of which 13% are new to clinical research. Localized functions were found to be 6.2% faster than centralized functions when working with repeat sites, and 3% faster when working with new sites. However, a decentralized function was found to have an impact on the percentage of sites not activated for studies, with 11.2% of sites never activated compared to 10.5% for centralized functions. These results may be a facet of the adoption of Study Startup management technology, with centralized groups showing greater adoption of technology. Moreover, this is reflected in the frequency of time savings reported due to technology by centralized functions, twice as many reporting large time savings than organizations with localized functions.
To centralize or not? The START II research concluded that there was no conclusive evidence that centralizing the function of site identification through to activation achieved significant improvements in terms of cycle time reductions. Irrespective of organizational structure both groups face similar challenges and see the same opportunities for improvement.
But what do the industry metrics have to say about cycle time performance of multi-country vs. single country studies?
Patient's Can't Wait
"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."
Jeff was formerly VP Clinical Innovation and Implementation at Eli Lilly and Company
President and Founder
"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. 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."
See how you can accelerate your clinical trials—request a FREE demonstration today.