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Deep Knowledge of Study Startup Points Data in the Right Direction

by Craig Morgan
06Feb2017

Massive volumes of data are generated during clinical trials, but they are woefully inadequate at helping stakeholders spot risk factors and bottlenecks that can disrupt cycle times and budgets.

White Paper: Deep Knowledge of Study Startup Points Data in the Right Direction

This is due to the inefficient ways in which operational data are captured and analyzed, often relying on outdated methods such as paper, shared file drives, and Excel, which lack much needed project- and risk management functionality.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. Specifically, the Tufts Center for the Study of Drug Development (CSDD) has reported that SSU is a major cause of long cycle times, which have stagnated for two decades, and that eight months is an average timeframe for moving from pre-visit to site initiation. 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.

Industry-proven tools for SSU are key to better operations in the increasingly global realm of clinical trials. The tools have focused end-to-end offerings, starting with site identification, moving to feasibility assessment, and finally, site activation. Standardized performance metrics are essential to the identification of bottlenecks for each of the steps involved in SSU. For example, knowing that it now takes ten weeks instead of twelve to complete a series of tasks may be encouraging, but not knowing how long each individual element takes lets bottlenecks continue. Moving toward standardized performance metrics via purpose-built solutions allows stakeholders to measure what is happening in real time so they can identify risk proactively and take corrective action. This is a big step forward for the industry.

Focused Offerings and Standardized Metrics

SSU is a complex business, composed of country selection, pre-study visits, site selection and initiation, regulatory document submission, budget and contract negotiations, patient recruitment initiatives, and enrolling the first patient. Each of these steps means sending a litany of documents among various stakeholders within the clinical team, and to institutional review boards (IRB) or ethics committees, and regulatory agencies. Because of the volume of documents involved and the number of people engaged in communication, electronic systems are standard practice for capturing and handling the flow of information related to clinical trial operations. These include the clinical trial management systems (CTMS) and the electronic trial master file (eTMF), which stores documents required for regulatory submission, and ultimately, for archiving. But neither of these eClinical tools was designed for SSU, and therefore, cannot generate metrics to identify risk associated with that portion of the trial.

Fortunately, there are cloud-based solutions focused specifically on SSU. goBalto's Select and Activate are workflow-driven tools, with Select used for intelligent site profiling, and Activate for expediting document completion and management processes. Analyze is a data analytics platform, which allows the clinical team to aggregate data and customize graphs, dashboards, and other data visualizations of study status. Together, these tools optimize the SSU steps and generate data that can be used for metrics that provide real-time insight into study status. By drilling down into specifics of each workflow, they can gather real-time answers to: What percentage of sites are activated? When was the clinical trial agreement template approved? Which sites and which countries are behind in receiving approval from ethics committees?

Answering these questions is useful for any study, but the real value is in comparing those results to an internal benchmark based on information from numerous studies.

Download our white paper Deep Knowledge of Study Startup Points Data in the Right Direction to see how purpose-built SSU solutions track clinical trial operations using much needed standardized performance metrics.

See how you can accelerate your clinical trials—request a FREE demonstration today.

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