Veeva CDB: A Clinical Data Platform for Complete and Concurrent Data

This video provides an in-depth exploration of Veeva CDB, a clinical data platform designed to aggregate, harmonize, and prepare clinical trial data for analysis and regulatory submission. The presenter, a Data Manager, demonstrates the platform's capabilities through the Veeva CDMS workbench, showcasing how it helps manage the complexities of clinical data, from initial collection to final export. The core objective is to deliver clean, well-organized, and ready-to-use data faster, thereby optimizing clinical operations and ensuring compliance.

The demonstration begins with an overview of the workbench interface, which provides data managers with a consolidated view of multiple studies. Key panels display progress in data collection, alongside critical study health indicators such as adverse events, unresolved queries, and overdue visits, enabling managers to prioritize their attention. The video then drills down into specific study data, illustrating how to navigate and interact with detailed data listings. These listings are designed to resemble spreadsheets, offering familiar concepts while incorporating "decorations" that highlight open queries, intentionally left blank fields, or data changes, facilitating efficient review and discrepancy identification.

A significant portion of the presentation focuses on data reconciliation and discrepancy management. The presenter shows how to customize listings by adding columns from various data sources, such as central lab data and demographics, to perform side-by-side comparisons. This capability is crucial for identifying inconsistencies, like mismatched dates of birth from different sources. For more advanced data manipulation and complex queries, the platform integrates CQL (Clinical Query Language), allowing users with appropriate permissions to write custom queries directly, which then dynamically update the UI. The video also highlights real-time data synchronization with EDC (Electronic Data Capture) systems, ensuring that data managers are working with the most current information and can quickly route queries back to the source for resolution, reducing communication delays.

Finally, the video details the process of creating export definitions, a critical step for preparing data for downstream systems and regulatory submissions. The platform offers a wizard-driven approach to build these definitions, with specialized augmentations for standards like SDTM (Study Data Tabulation Model). This includes automated mapping of study design terms to variable names, ensuring correct data types and date formats. Users can schedule exports in various formats (e.g., CSV, SAS) and inspect the technical properties of data columns, including variable names, data types, and code list transformations. This comprehensive approach to data management, reconciliation, and export underscores Veeva CDB's role in streamlining clinical data workflows, enhancing data quality, and supporting regulatory compliance.

Key Takeaways:

• Veeva CDB serves as a centralized clinical data platform that aggregates and harmonizes diverse data sources, ensuring data is clean, well-organized, and readily available for use.
• The CDMS workbench provides data managers with a holistic view of clinical trials, offering insights into data collection progress and study health metrics like adverse events, unresolved queries, and overdue visits.
• Interactive data listings are designed for intuitive review, resembling spreadsheets but enhanced with visual "decorations" that indicate open queries, intentionally left blank fields, or recent data changes.
• The platform facilitates efficient discrepancy management by allowing users to filter, sort, and customize data listings, integrating data from multiple sources (e.g., central lab, demographics) for direct comparison and reconciliation.
• Advanced data manipulation is supported through CQL (Clinical Query Language), enabling users to write complex queries for identifying discrepancies and performing transformations, with immediate reflection in the user interface.
• Real-time data synchronization with EDC systems is a core feature, ensuring that data managers always work with the most up-to-date information and can quickly route queries back to the source, minimizing communication gaps.
• Users can drill down from any data cell directly into the EDC system to review the context of the data within its original form, enhancing the efficiency of query resolution.
• Automated query routing ensures that any new queries raised within Veeva CDB are automatically sent back to the relevant source system or site for prompt action.
• Export definitions provide a structured and wizard-driven approach to prepare data for external systems and regulatory submissions, streamlining the process of data delivery.
• The platform includes specialized augmentations for regulatory standards like SDTM, automatically applying correct variable names, data types, and date formats based on study design, significantly reducing manual effort and ensuring compliance.
• Export jobs can be scheduled to run regularly and support various output formats, including CSV and SAS, catering to different downstream system requirements.
• An "inspect mode" allows for a technical review of data properties within export definitions, enabling users to modify variable names, data types, and code list labels directly for precise transformations.
• The entire process, from data review to export, follows a controlled workflow, moving from draft mode through inspection and readiness to final publication and job execution, ensuring data quality and governance.
• Veeva CDB aims to reduce the time and effort traditionally associated with clinical data management, enhancing data quality, accelerating data readiness, and ensuring regulatory compliance.

Tools/Resources Mentioned:

• Veeva CDB (Clinical Data Platform)
• Veeva CDMS (Clinical Data Management System)
• Workbench application (within CDMS)
• EDC (Electronic Data Capture)
• CQL (Clinical Query Language)
• SDTM (Study Data Tabulation Model)
• CSV (Comma Separated Values)
• SAS (Statistical Analysis System)

Key Concepts:

• Clinical Data Management (CDM): The process of collecting, managing, and analyzing data from clinical trials.
• Data Aggregation & Harmonization: Combining data from disparate sources and standardizing its format and content for consistent analysis.
• Discrepancy Management: The process of identifying, tracking, and resolving inconsistencies or errors within clinical data.
• Data Listings: Tabular presentations of clinical data, often used for review, analysis, and reporting.
• Data Reconciliation: Comparing data from different sources to identify and resolve discrepancies, ensuring data accuracy.
• Export Definitions: Configurable templates or settings that define how clinical data should be prepared and exported for specific purposes, such as regulatory submissions or further analysis.
• SDTM Transformation: The process of mapping and converting clinical trial data into the Study Data Tabulation Model (SDTM) format, a standard required by regulatory bodies like the FDA.
• Real-time Data Updates: The capability of a system to receive and reflect changes in data as they occur, minimizing delays in information availability.
• Query Management: The system and process for generating, tracking, and resolving queries related to clinical data discrepancies or missing information.