Digital Data Flow

This initiative aims to advance the drug development process by enabling clinical trial protocol digitalization, helping to move the industry from a document-centric to a data-centric approach.  With this change, Digital Data Flow has the potential to reduce cycle times, improve data reliability and quality for sponsors, third party providers, sites and regulators.

Rationale

Clinical trial protocols serve as the anchor for specifying clinical trial requirements and parameters. Currently, protocols do not exist in a common, machine-readable format that can be easily exchanged among disparate systems. An average lag time of 4 months exists between protocol approval and study start up. Furthermore, converting data to Study Data Tabulation Model (SDTM) by third parties lengthens cycle time and limits traceability and re-use. Finally, working in a document-based environment results in significant manual duplication of effort.

By starting with the Unified Study Definitions Model (USDM), an industry standard data format used during study design, the Digital Data Flow initiative offers a mechanism to digitize clinical study components to enable automation, interoperability, and reuse across the study lifecycle.

The Digital Data Flow initiative will help modernize clinical trials by enabling a digital workflow. Such a workflow will enable automated study asset creation and study system configuration to support clinical trial execution. This initiative will establish a foundation for a future state of automated and dynamic readiness that can transform the drug development process.

How We’re Enabling Clinical Trial Protocol Digitalization

TransCelerate has collaborated with CDISC and other stakeholders to develop a standard data model for specifying protocol information, as well as a demonstrated way to connect systems that produce, exchange, or consume this information.

Our approach centers around 3 key principles:

Digital Data Flow Initiative Roadmap

Benefits

  • Eliminate non value-added activities through automation: “Work Smarter Not Harder”
  • Enable flexible company and/or industry-wide interoperability by applying clinical protocol standards
  • Create foundation for study design analytics insights

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