CDISC U.S. Interchange 2025: Connecting Standards, Strategy, and the Future of Clinical Research
CDISC CEO and President, Chris Decker, kicking off the CISC U.S. Interchange 2025 in Nashville, TN.
At Orizaba Solutions, we believe data is only as powerful as the strategy and standards behind it. That’s why the CDISC U.S. Interchange 2025, held in Nashville this October, felt like both a reunion and a reminder of how far the data standards community has come.
I’ve attended CDISC U.S. Interchanges periodically since my first in 2005, when I had just transitioned from the genomics world and was astonished by how differently clinical trial data was being managed. The last Interchange I attended was in 2019, when Real-World Data (RWD) first appeared on the agenda, alongside early discussions about CDISC 360 and the CDISC Library.
Much has changed since then. The world, and CDISC, has evolved significantly under new leadership, now guided by Chris Decker as CEO. The 2025 Interchange brought together thought leaders, innovators, and data standards experts from across the clinical research ecosystem to explore the future of connected, standards-driven science.
Over two days, sessions covered digital study design, regulatory submissions, TMF modernization, AI-driven automation, and real-world data. The program reflected CDISC’s 25-year legacy of bringing together a global community of experts to develop and advance data standards of the highest quality to create clarity in clinical research.
The opening session honored pioneers who built the foundation for today’s standards and were instrumental in supporting their adoption, including Dr. Lilliam Rosario, former FDA Director of the Office of Computational Science, and Stephen Wilson, former USPHS Director of the Division of Biometrics III, among others from FDA and industry whose work and championship has shaped the submission data landscape.
Keynote: Creating an AI-Enabled Learning Health and Research System — Now It’s Personal
One of the most powerful sessions was the keynote by Dr. Peter J. Embí, Professor of Biomedical Informatics and Medicine at Vanderbilt University and Founding Director of the ADVANCE AI Center. His presentation, “Creating an AI-Enabled Learning Health and Research System — Now It’s Personal,” combined personal experience with a vision for transforming healthcare.
Dr. Embí recounted his years-long struggle with misdiagnosis before finally learning he had a rare adrenal cancer—an experience that exposed the deep challenges of diagnostic accuracy and data fragmentation. Citing the Institute of Medicine’s report To Err Is Human (2000) and other research, he reminded the audience that diagnostic errors remain one of the most under-addressed threats to patient safety. “People are suffering needlessly,” he said. “We can and must do better.”
Central to his message was the concept of the Learning Health System (LHS)—an adaptive framework where data from every patient encounter informs continuous improvement in care, research, and policy. The traditional separation between research and clinical care, he noted, has created “silos of excellence” that hinder progress. Local learning systems, designed to “learn from every patient,” can bridge those divides.
While AI has immense potential to accelerate this transformation, Dr. Embí cautioned that “AI is not magic—it is only as good as the data it is trained on.” He warned of algorithmic bias, citing an example where healthcare costs were used as a proxy for health, failing to take into account socio economic status—leading to inequitable outcomes for marginalized populations.
To mitigate these risks, he introduced VAMOS (Vigilant AI Monitoring and Operation System)—a socio-technical framework for real-time AI oversight that integrates governance, team-based monitoring, and analytics to evaluate accuracy, drift, fairness, and equity.
His closing message was both technical and moral: creating AI-enabled learning systems requires trustworthy data, strong governance, and continuous feedback loops. This resonated deeply with the CDISC community’s mission—and with Orizaba Solutions’ commitment to create clarity in data and ensure it is trusted, interoperable, and used responsibly enabling more efficient research and a greater impact on global health.
Plenary: 360i Vision & Roadmap — Accelerating Standards-Driven Automation
The opening plenary, “360i Vision & Roadmap,” captured CDISC’s ambitious journey toward a fully digital and automated clinical trial ecosystem. As clinical research grows increasingly data-intensive—today’s trials collect an average of 3.6 million data points, roughly three times more than a decade ago[1]—the need for smarter, connected data processes has never been greater.
Chris Decker began with a simple but powerful reminder: “AI cannot fix a broken process.” Before we can realize the promise of automation, we must modernize the way research data is structured and shared. The 360i initiative is CDISC’s answer—digitizing study definitions, making metadata interoperable, and enabling machine-driven traceability across the research lifecycle. “CDISC is building models, not monoliths,” Decker explained, emphasizing a modular approach where standards are seamlessly embedded in the tools researchers already use.
Peter Van Reusel showcased the “art of the possible,” illustrating how CDISC standards can support an integrated digital environment from protocol through to submission. Through conceptual models and automation use cases—from protocol optimization to automated eCTD population—he demonstrated how the Unified Study Definition Model (USDM), developed with TransCelerate as part of the Digital Data Flow (DDF) initiative and aligned with ICH M11, can make research faster, more transparent, and more reproducible.
Julie Smiley closed with real-world progress: pilot projects and community collaborations showing that this transformation is already underway. Together, these initiatives are building a foundation for end-to-end automation. This echoed the vision articulated by Dr. Michelle Longmire, CEO of Medable, at an earlier conference on AI in Drug Development (see my previous blog), of the “1:1:1” model: one day to start a study, one day to enroll, and one year to complete.
The 2025 Interchange reinforced why it is imperative that data standards and strategy evolve together. CDISC’s focus on digital transformation, automation, and interoperability mirrors the challenges Orizaba Solutions helps our clients address every day: ensuring that data is high-quality, governed, and ready for AI and analytics.
At Orizaba Solutions, we work with companies, federal agencies, and research organizations to realize the full value of their data. Our mission is to help organizations build trust in their data, modernize processes, and create connected, standards-driven ecosystems that support discovery, compliance, and innovation.
As CDISC enters its next chapter, bridging standards, strategy, intelligent automation, and trustworthy AI, we’re reminded that the future of research depends on one thing above all: enabling data that is accessible, interoperable, and trusted.
Written by Ingeborg Holt, Founder and Principal, Orizaba Solutions
Orizaba Solutions helps organizations unlock the full value of their data through strategy, governance, and data-centric solutions that drive mission success.
[1] Studna, A. Complexities in Data Collection. Applied Clinical Trials, October 2, 2024. https://www.appliedclinicaltrialsonline.com/view/complexities-data-collection