Preparing for Your Oracle, Medidata, and Veeva CTMS Migration Project

This webinar provides an in-depth exploration of preparing for a Clinical Trial Management System (CTMS) migration project, focusing on strategies, considerations, and technical approaches for moving data between systems like Oracle Siebel CTMS, Medidata Rave CTMS, and Veeva Vault CTMS. Presented by Bunim Singh, Director of Clinical Operations Solution Practice at Perficient Life Sciences, the session aims to equip organizations with a framework for analyzing their own CTMS migration needs, whether driven by mergers and acquisitions (CTMS consolidation) or a desire to switch vendors. The discussion covers critical questions such as whether to migrate, what data to migrate, how to execute the migration, and the optimal timing for such projects, emphasizing both business and technical perspectives.

The presentation systematically breaks down the decision-making process for CTMS migration, starting with a cost-benefit analysis. It highlights the advantages of migration, including enabling comprehensive reporting across all studies, providing a complete live picture of studies in one system, streamlining operations by having a single set of business processes, and reducing IT support and maintenance costs by decommissioning legacy systems. Conversely, the speaker addresses potential risks such as loss of functionality or data in the new system, lag time between migration and user access, and data disconnects if users operate in both old and new systems concurrently. The video then delves into scoping, advising a two-tiered approach: first, identifying which studies (e.g., long-duration active studies) are good candidates for migration, and second, determining which specific data types or records within those studies are essential to transfer, considering business needs and target system capabilities.

A significant portion of the webinar is dedicated to the "how" of migration, exploring various tools and technical approaches. It discusses manual data entry as a potentially cost-effective method for low-volume data, contrasting it with automated options utilizing embedded system tools (like Oracle Siebel's Enterprise Integration Manager or CSV/XML imports for Veeva Vault and Medidata), existing ETL tools (e.g., Informatica, SSIS), or custom-built migration routines. The speaker illustrates three common technical architectures: migrating from an in-house CTMS to another in-house solution (typically database-to-database), migrating from an in-house CTMS to a standard cloud CTMS (requiring data formatting to vendor-prescribed import standards), and migrating to a customized cloud CTMS solution, which can involve building reusable migration solutions for ongoing data transfers like CRO feeds. Finally, timing considerations are discussed, weighing "big bang" deployments against phased, study-by-study rollouts, and emphasizing alignment with training and legacy system decommissioning strategies.

Key Takeaways:

• Purpose-Driven Migration: The primary driver for any CTMS migration should be clearly defined business benefits, such as enabling comprehensive reporting, consolidating operations into a single system, or reducing IT support costs by decommissioning legacy applications.
• Weighing Benefits Against Risks: Organizations must carefully assess the value of migration benefits against potential risks, including loss of functionality or data, operational lag time post-migration, and data inconsistencies if users access both legacy and new systems.
• Strategic Study Scoping: When deciding which studies to migrate, prioritize long-duration active studies that will run significantly past the new system's go-live date. Short-term studies or those ending soon may be better left in the legacy system, while new studies can start directly in the new CTMS.
• Data Type Scoping: Define the scope of data types by identifying what information has a clear target in the new system and what is critical for business needs (e.g., reporting, current operations). Consider if certain data can remain in a legacy system or be archived elsewhere if not essential for the new CTMS.
• Inevitable Data Cleansing and Standardization: Data migration almost always requires a significant data cleansing effort to transform or translate legacy data into the new system's defined standards (e.g., address formats, list of values). This effort must be factored into project timelines and costs.
• Managing Multiple Data Sources: The number and variety of legacy data sources (e.g., multiple CTMS, spreadsheets, custom trackers) can dramatically increase migration complexity and effort. Consider a data consolidation effort to combine data into a single format before building migration routines.
• Manual vs. Automated Migration: For low volumes of data, manual data entry can sometimes be a more cost-effective option than developing and validating complex automated migration routines, especially if the migration is a one-time event.
• Leveraging Existing Tools: Utilize embedded system tools (e.g., Oracle Siebel EIM, Veeva Vault CSV imports, Medidata XML imports) or existing ETL tools (e.g., Informatica, SSIS) for automated migrations, which can significantly reduce custom development.
• Adherence to Cloud Vendor Formats: When migrating to standard cloud CTMS solutions (like Veeva Vault or Medidata), data must be formatted precisely to the vendor's prescribed import specifications, as these are typically standardized and not easily customizable.
• Reusable Migration Solutions: For organizations with ongoing data transfer needs (e.g., CRO feeds, M&A integrations), building a customized cloud CTMS solution with a reusable migration framework can provide significant long-term cost and effort savings.
• Validation is Key: Validation efforts constitute a substantial portion of the cost and effort for automated data migration projects, ensuring data integrity and regulatory compliance.
• Strategic Timing and Rollout: The timing of data migration (e.g., "big bang" vs. phased approach) must align with user training schedules and the legacy system decommissioning strategy to minimize operational disruption and maximize user adoption.
• Regulatory Compliance: All CTMS implementations and data migrations must adhere to industry and regulatory standards and guidelines, such as those from the FDA and EMA, ensuring data integrity and auditability.

Tools/Resources Mentioned:

• CTMS Platforms: Oracle Siebel CTMS, Medidata Rave CTMS, Veeva Vault CTMS
• ETL Tools: Informatica, SSIS
• Embedded Migration Tools: Oracle Siebel Enterprise Integration Manager (EIM), CSV import formats (for Veeva Vault CTMS), XML import formats (for Medidata Rave CTMS)
• Technology & Platform Vendors: AWS, Microsoft, Adobe (Perficient's strategic partners)

Key Concepts:

• CTMS (Clinical Trial Management System): A software system used to manage and track various aspects of clinical trials, including study planning, site management, patient enrollment, and regulatory compliance.
• Data Migration: The process of transferring data from one system (legacy) to another (new) due to system upgrades, consolidation, or vendor changes.
• ETL (Extract, Transform, Load): A three-phase data integration process used to extract data from a source system, transform it into a format suitable for the target system, and load it into the target system.
• Data Cleansing: The process of detecting and correcting (or removing) corrupt or inaccurate records from a record set, table, or database.
• Data Consolidation: The process of combining data from multiple sources into a single, unified data store.
• Big Bang Migration: A migration strategy where all selected data is moved from the legacy system to the new system simultaneously.
• Phased Migration: A migration strategy where data is moved in stages, often study-by-study or by user group, allowing for incremental rollout and issue resolution.
• Validation: The process of ensuring that a system or data migration process meets specified requirements and is fit for its intended use, particularly critical in regulated environments like life sciences.

Examples/Case Studies:

• Growing CRO: A scenario where a growing Contract Research Organization with limited resources might choose to launch a new CTMS for planned studies rather than migrating existing data, especially if current studies are short-term.
• Oncology Company: An example of an oncology company managing long-term trials with significant legacy data, where the decision to migrate is complex, especially if a data warehouse already provides consolidated reporting.
• Manual Data Entry Cost Savings: A historical case where an organization hired temporary data entry personnel to manually key in thousands of contacts, saving money compared to building complex automated migration routines for a one-time transfer.
• Reusable Custom Cloud Solution: A large global pharma company deployed a customized cloud CTMS solution with a reusable migration framework, which was used for an initial one-time CTMS migration and subsequently for ongoing daily CRO data feeds, demonstrating significant cost and effort savings.