By Craig Morgan, director, goBalto Inc.
Underperforming investigative sites have long been a puzzling issue for clinical trial stakeholders. There are lots of reasons for sub-optimal performance, ranging from inadequate processes for study execution to overly complex protocols. Current thinking suggests that at least some of the problem stems from continued reliance by many sites on paper-based or simple spreadsheet methods for numerous aspects of study conduct.1 The problem is compounded by sponsors and contract research organizations (CROs) who operate in a similar manner—using older methods to track site performance, resulting in a lack of transparency as to what is happening in real-time and an inability to mitigate risk that could stall clinical trial conduct. This scenario is played out as clinical trials unfold, with the notoriously slow study startup (SSU) phase particularly hard hit. Sites are selected during SSU, a task that is best performed when sponsors or CROs use a data-driven approach to create a target site profile based on an algorithm that uses a weighted average of feasibility, SSU metrics, and site experience. Fortunately, this approach is now possible with the recent launch of purpose-built technologies designed to consolidate data from multiple sources that point sponsors and CROs toward the right sites, increasing the chances for better study execution.