Episode 9: How Can We Prepare for the Future of Clinical Trials?

This podcast episode, hosted by Richard Young of Veeva Vault CDMS and featuring Rhona O’Donnell, VP of Data Management Systems and Standards at Novo Nordisk, delves into the evolving landscape of clinical trial data management and how the industry can prepare for its future. The discussion traces the journey of data management from its early, paper-based days to the complex, technology-driven environment of today, highlighting the significant increase in data volume and the expanded responsibilities of data managers. A central theme is the urgent need for the pharmaceutical industry to embrace risk-based approaches in data management to enhance efficiency, ensure data quality, and address talent shortages.

The conversation begins by contrasting the past, where data management involved manual tracking of paper CRFs and double data entry into systems like OC, with the present challenges. Rhona O’Donnell shares anecdotes from her early career, illustrating a slower pace and more centralized teams, which offered a longer grounding for new data managers. Today, data managers are expected to quickly become functional leads, involved in all project meetings, and manage risks and issues—a far cry from the "back-end services" role of the past. This evolution, coupled with a persistent talent shortage, underscores the necessity for innovative training programs, such as industry-wide academies, to equip the next generation with the skills to navigate increasingly complex technological landscapes.

A significant portion of the discussion focuses on the industry's slow adoption of risk-based data management (RBDM), despite the successful implementation of risk-based quality management (RBQM) in clinical monitoring. The speakers argue that the pursuit of "perfection" for every data point is unsustainable and inefficient, especially given the exponential increase in data volume (from 10 data points per page to potentially millions per patient per day). They advocate for a shift in mindset to accept a certain tolerance for errors in less critical data, allowing technology to surface true signals for investigation. This strategic change is presented as crucial for making trials more sustainable, improving data quality where it matters most, and alleviating pressure on overstretched teams.

The episode also highlights Novo Nordisk's "Study Builder" project as a forward-thinking initiative. This system aims to create a metadata repository that defines standards from the protocol all the way through data collection to Tables, Figures, and Listings (TFLs). The goal is to solidify standardization, enable end-to-end efficiency, and provide a framework for automation opportunities across the entire study lifecycle, from CRF build in EDC to data cleaning. This project exemplifies how a major pharmaceutical company is proactively addressing the challenges discussed, emphasizing that significant time and cost savings can be achieved by optimizing processes at the study's outset, rather than solely focusing on accelerating the end-of-study submission. The speakers conclude by stressing the importance of greater collaboration among CROs, sponsors, regulators, and technology companies to develop shared roadmaps and standards, leveraging collective intelligence to drive the industry forward.

Key Takeaways:

• Elevated Role of Data Management: Data managers have transitioned from a "back-end services" role to critical "functional leads" involved in all project phases, including risk and issue management, demanding a broader skill set and earlier engagement in trial design.
• Talent Shortage and Training Imperative: The industry faces a significant talent shortage in data management, necessitating investment in academies and graduate programs to prepare the next generation, who possess innate technological savviness, for the speed and complexity of modern clinical trials.
• Urgency for Risk-Based Data Management (RBDM): While risk-based quality management (RBQM) is adopted in monitoring, RBDM is lagging. The industry must move away from the unsustainable pursuit of "perfection" for all data points, tolerating minor errors in less critical data to focus resources on key efficacy and safety data.
• Leveraging Technology for Data Flow Oversight: With the proliferation of external data sources, decentralized clinical trials (DCTs), and hybrid models, there's a critical need for integrated technology solutions and dashboards to oversee complex data flows and identify issues proactively.
• Standardization through Metadata Repositories: Novo Nordisk's "Study Builder" project demonstrates the power of a metadata repository to enforce end-to-end standardization from protocol definition to TFLs, enabling greater efficiency and automation across the entire study lifecycle.
• Significant Automation Opportunities: Defining standards within a system like "Study Builder" creates numerous opportunities for automation, such as automated CRF building in EDC, streamlining processes, and reducing manual effort.
• Focus on Upfront Efficiency: The greatest opportunities for time and cost savings lie in optimizing the initial phases of a study, specifically from protocol finalization to first patient in, rather than solely pushing for faster database lock or submission.
• Industry-Wide Collaboration is Key: Progress requires enhanced collaboration among sponsors, Contract Research Organizations (CROs), regulators, and technology companies to develop shared roadmaps, standards, and resources, avoiding duplicated efforts.
• Learning from Other Industries: The pharmaceutical industry can benefit from studying risk-based approaches and large data set management strategies employed by other conservative sectors, such as the finance industry, to accelerate its own evolution.
• Critique of Current Inefficiencies: Practices like programming numerous EDC edit checks that never fire and painful, often unnecessary, data reconciliation processes are highlighted as wasteful and ripe for elimination through risk-based approaches.
• Regulator's Role in Modernization: Regulators are encouraged to provide clearer guidance on digital transactions and local/regional differences to facilitate the adoption of modern, efficient processes in clinical trials.
• Embrace Next-Generation Problem Solving: The inherent ability of younger generations to quickly adapt to and leverage technology should be harnessed to bring fresh ideas and innovative solutions to clinical data management challenges.

Tools/Resources Mentioned:

• Veeva Vault CDMS (Clinical Data Management System)
• MS Access (Microsoft Access)
• OC (Oracle Clinical)
• EDC (Electronic Data Capture)
• Study Builder (Novo Nordisk internal project for metadata repository and standardization)

Key Concepts:

• Risk-Based Data Management (RBDM): An approach to data management that prioritizes data cleaning and quality control efforts based on the criticality of the data, moving away from the traditional "perfect data" expectation for all data points.
• Risk-Based Quality Management (RBQM): A broader quality management approach in clinical trials that focuses resources on preventing and detecting errors that are most critical to patient safety and data integrity, often applied to monitoring.
• Metadata Repository: A centralized database that stores metadata (data about data), used in the "Study Builder" project to define and manage standards from protocol to TFLs.
• Decentralized Clinical Trials (DCTs) / Hybrid Trials: Clinical trial models that incorporate virtual elements and remote data collection, leading to more diverse and complex data sources.
• CRF (Case Report Form): A document (paper or electronic) used in clinical trials to record patient data.
• TFLs (Tables, Figures, Listings): The final outputs of statistical analysis in clinical trials, used for regulatory submissions.
• STDM (Study Data Tabulation Model): A standard for organizing and formatting clinical trial data for submission to regulatory authorities.

Examples/Case Studies:

• Novo Nordisk's "Study Builder" Project: An initiative to create a system with a metadata repository to define and link standards from the clinical trial protocol through data collection to TFLs, aiming for end-to-end efficiency and automation.
• Historical Cardiovascular Trial: An anecdote from the guest's early career involving a large cardiovascular trial that used paper CRFs, MS Access for tracking, and double data entry into OC, highlighting the manual and centralized nature of data management in the early 2000s.