Article
Clinical Trial Data Strategy
Read the latest article published on Hit Consultant Publishing, outlining how shifting to an innovative data collection and flow while reducing burden on your sites leads to heighten trial efficiencies
Dec 12, 2023

In drug development, there is a critical focus on protocol design, because it is a key component to running a successful clinical trial. And, in traditional trial design, developing a data management strategy often comes after the protocol is finalized. This can leave study teams juggling between operationalizing the protocol and mapping out optimal data collection and flow in parallel. In today's complex trial landscape, streamlining planning and processes is top of mind for all stakeholders, and accounting for a thorough data management strategy earlier in trial planning is one key way for trial sponsors and study teams to heighten efficiencies.

For one, as the industry emphasizes the value of patient-centered drug development, we see the value of data insights made available to us due to the increased use of connected devices, wearables, electronic diaries and other decentralized trial solutions. Add the ability of advanced artificial intelligence-driven methodologies to extract meaningful insights from lab work, patient-reported data and imaging, and the breadth of data available within one study can grow exponentially.

By reviewing multiple data points from various sources and angles, we can capture richer insights and patient behavioral trends to inform smarter decisions in drug development, but this happens with intent, not by accident. Given the massive amounts of data acquired in clinical tests, it is simply not efficient to manually collect, monitor, clean and analyze large data volumes without a meaningful data strategy. To ensure data insights are appropriately leveraged to inform decisions with patient safety and data quality in mind, sponsors and clinical research organization partners must define the data strategy before protocol design. This strategy will map out optimal data collection from a growing mix of traditional and digital data sources during trial planning, including the notable considerations for managing data flow

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