Interview with Veeva Manny Vazquez

This video provides an in-depth exploration of the evolving landscape of clinical data management, particularly in the context of emerging AI technologies and the declining centrality of traditional Electronic Data Capture (EDC) systems. Featuring Manny Vazquez, Senior Director of Clinical Data Strategy at Veeva, the discussion challenges the status quo of fragmented tech stacks in clinical trials, advocating for a platform-centric approach to achieve greater efficiency and scalability across the life sciences. The conversation underscores the urgent need for pharmaceutical companies to rethink their data infrastructure to become "AI-ready" rather than relying on outdated, inefficient workarounds.

Vazquez highlights that many companies, led by long-tenured leadership, have accumulated a "cluster" of custom solutions layered on top of existing systems over decades. While these solutions were once considered "best-of-breed" at the time of their implementation, they have collectively resulted in wholly inefficient processes. He argues that the industry is at a critical turning point, driven primarily by the advent of AI, which is not merely an evolution but a "completely new computing paradigm." This shift necessitates a fundamental reset, forcing organizations to critically examine their processes and data foundations, as being "AI-ready" is paramount for future success and avoiding significant competitive catch-up.

The discussion further clarifies that the push for simplicity in clinical data management does not imply reducing the volume or complexity of data collected. Instead, the focus is on simplifying the processes by which data is collected, processed, and moved through the pipeline. Vazquez emphasizes that stacking complexity on top of existing complexity is unsustainable, especially with the increasing volume of data from diverse sources. The goal is to enhance user experience, leverage better tools, and prioritize configuration over extensive customization, ensuring that the underlying content and data remain robust while interactions with them become more streamlined.

A significant portion of the interview is dedicated to outlining the practical foundation required for AI implementation. Vazquez explains that for AI to function effectively, companies need all their data in one centralized repository, accessible at a steady, high frequency. He cites Veeva's own product, CDB (Clinical Data Backbone), and the Study File Format API as examples of solutions designed to meet these foundational needs. He argues that EDC is no longer the "center of the universe" but merely one of many data sources, with tools like CDB serving as the new "data workbench" hub. This shift is crucial for addressing the pervasive issue of data fragmentation, which currently leads to immense waste in manual data review and inefficient third-party data reconciliation, especially as clinical trials integrate data from wearables, real-world evidence, and genomics.

Key Takeaways:

• Outdated Tech Stacks and Inefficiency: Many pharmaceutical companies operate with inefficient tech stacks built over decades, characterized by layered custom solutions that create a "cluster" rather than a cohesive system. This leads to significant waste in manual data review and reconciliation.
• Platform Approach for Automation: There's a critical need to transition from a "best-of-breed" tool mentality to a "platform approach" across life sciences to achieve end-to-end automation and scale, breaking the cycle of inefficiency.
• AI as a Paradigm Shift: AI represents a "completely new computing paradigm," not just an evolution. It demands a fundamental re-evaluation of existing processes and data infrastructure, making "AI-readiness" an immediate imperative.
• Data as AI's Foundation: Successful AI implementation hinges on having structured, organized data in a unified repository (or multiple accessible repositories). Without this foundational data layer, AI cannot be effectively deployed.
• Simplifying Processes, Not Data Volume: The goal is to simplify the processes of data collection, processing, and movement, not to reduce the volume or underlying complexity of the data itself. This requires better tools, user experience, and configuration over customization.
• Practical AI Foundation: Preparing for AI involves two key steps: consolidating all relevant data into a single, accessible location (e.g., a data workbench like Veeva's CDB) and ensuring high-frequency access to that data (e.g., via APIs like Veeva's Study File Format API).
• EDC's Diminished Role: Traditional EDC systems are no longer the central hub of clinical data management. They are now just one of many data sources, alongside wearables, eCOAs, EMRs, and real-world evidence.
• Data Workbenches as the New Hub: Tools like Veeva's CDB, functioning as a "data workbench," are emerging as the new central hub for ingesting, harmonizing, and managing diverse data sources in clinical trials.
• Combating Data Fragmentation: Fragmentation remains a major risk, especially with the integration of new technologies like wearables and implantable devices. Automated ingestion and harmonization into a single database are crucial to overcome manual, inefficient reconciliation.
• Value of All Data: While protocols may define specific data points for endpoints, all collected data, including massive volumes from wearables, will eventually be valuable for advanced analytics and AI-driven investigations.
• Avoid Complacency: A significant mistake organizations make is not actively preparing for AI or assuming their current infrastructure is an adequate foundation for layering AI solutions. The "sprint has started," and immediate investment in foundational data strategies is necessary.

Tools/Resources Mentioned:

• Veeva CDB (Clinical Data Backbone): A product designed to consolidate all clinical trial data into one place.
• Veeva Study File Format API: An API built to provide steady, high-frequency access to consolidated data for analytics and AI.
• EDC (Electronic Data Capture): Traditional systems for capturing clinical trial data, now seen as one of many data sources.
• ChatGPT: Mentioned as the catalyst that accelerated the AI hype cycle and highlighted its immediate arrival.

Key Concepts:

• AI-Ready: The state of an organization having the necessary data infrastructure, processes, and technological foundation to effectively implement and leverage Artificial Intelligence.
• Platform Approach: A strategy where an integrated suite of tools and services (a platform) is used to manage end-to-end processes, rather than relying on disparate "best-of-breed" solutions that require extensive custom integrations.
• Data Fragmentation: The issue of clinical trial data being scattered across multiple, disconnected systems and repositories, leading to inefficiencies in access, harmonization, and analysis.
• Data Harmonization: The process of standardizing data from various sources into a consistent format and structure, making it compatible for analysis and integration.
• Data Workbench: A centralized environment or tool (like Veeva CDB) that allows users to ingest, manage, process, and analyze diverse datasets from multiple sources.
• eCOA (electronic Clinical Outcome Assessment): Electronic methods for patients to report outcomes.
• EMR (Electronic Medical Record): Digital versions of patient charts from healthcare providers.
• Real-World Evidence (RWE): Clinical evidence derived from sources outside of traditional randomized controlled trials, such as electronic health records, claims data, and patient registries.
• Genomics Data: Information derived from the study of an organism's entire set of DNA.