“Shifting to Innovative Data Collection and Flow While Reducing Burden On Your Sites” by Sabrina Steffen, Head of Data Strategy and Innovation for Data Management at IQVIA
In the world of drug development, protocol design plays a crucial role in the success of clinical trials. However, in traditional trial design, the development of a data management strategy often takes a backseat and comes into play after the protocol is finalized. This approach can lead to inefficiencies and challenges for study teams as they try to operationalize the protocol while also mapping out optimal data collection and flow.
In today’s complex trial landscape, streamlining planning and processes is a top priority for all stakeholders. One way to achieve this is by incorporating a thorough data management strategy earlier in the trial planning process. This strategy allows trial sponsors and study teams to enhance efficiencies and make better use of the valuable data insights made possible by the increased use of connected devices, wearables, electronic diaries, and other decentralized trial solutions.
The integration of advanced artificial intelligence-driven methodologies further enhances the value of these data insights. By extracting meaningful insights from lab work, patient-reported data, and imaging, researchers can gain a deeper understanding of patient behavior and make smarter decisions in drug development. However, harnessing the full potential of this data requires a deliberate and well-defined data strategy.
With the massive amounts of data acquired in clinical trials, manually collecting, monitoring, cleaning, and analyzing large volumes of data is simply not efficient. To ensure that data insights are appropriately leveraged while prioritizing patient safety and data quality, sponsors and clinical research organization partners must define the data strategy before protocol design. This strategy will outline the optimal data collection from a mix of traditional and digital sources, taking into account the considerations for managing data flow.
Today, most clinical trials involve multiple data sources, with some trials incorporating up to 20 different sources. This is three times the amount of data captured in trials a decade ago. On average, a single study can generate approximately 8.1 million data points from all raw data sources. For studies that integrate connected devices with streaming data collection, such as continuous glucose monitors, the volume of data can increase exponentially.
Establishing an effective data flow strategy is essential for sponsors and clinical research organizations to collect, monitor, and analyze this large volume of incoming data. A comprehensive data management strategy serves as the foundation for end-to-end digitized data collection and flow processes. It includes elements such as data governance, data quality management, data integration, and data security.
By implementing a robust data strategy early on, trial sponsors and study teams can streamline processes, reduce the burden on trial sites, and make better use of the wealth of data available to them. This approach not only enhances efficiencies but also improves patient-centered drug development by leveraging insights from various data sources and angles.
In conclusion, incorporating a data management strategy earlier in the trial planning process is crucial for successful drug development. By prioritizing data collection and flow, trial sponsors and study teams can harness the power of connected devices, wearables, and other digital solutions to gain valuable insights and make informed decisions. With the right data strategy in place, the potential for innovation in clinical trial data collection and analysis is limitless.
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