• Leveraging AI to automate data management activities for clinical study teams, database programmers, and data managers 
• Redesigning clinical data flow for agentic AI monitoring, orchestration, and workflow automation 
• Automating anomaly detection, data reconciliation, and query management to accelerate clinical database locks​

Clinical and operational data are usually managed as separate streams, each feeding different teams and different reports. But the real advantage comes from converging them at the moment decisions are actually made: trial design, interim go/no-go, and program continuation. This session explores why fragmented data doesn't just slow trials down—it degrades the quality of the decisions themselves. Drawing on real-world examples from feasibility planning, enrollment-risk prediction, and real-time trial oversight, we'll offer a practical framework for identifying exactly where your clinical and operational data need to converge.

Every protocol amendment triggers hours of manual rework across specifications, data definitions, and analysis plans, rework that quietly drives trial delays, added cost, and last-minute quality risk. CDISC's 360i initiative is building a connected data framework that links protocol design through analysis and submission, so a single change updates everywhere it matters instead of being rediscovered downstream. This session gives clinical operations leaders a plain-language look at what 360i is building, why it should matter to their timelines and budgets, and what to watch for as it heads toward broader adoption.

SAP2TLFis a human-in-the-loop agentic AI system that accelerates clinical trial statistical programming for Tables, Listings, and Figures. It generates study-aware SAS analytics code grounded in sources such as SAPs, TLF shells, SDTM/ADaM metadata, and curated examples, with syntax and executability checks before human review. Built-in provenance, audit logging, versioning, and end-to-end traceability enable governed, evidence-based code generation that shortens timelines, reduces rework, and supports responsible, auditable AI adoption.

As pharmaceutical organizations accelerate the adoption of AI and GenAI, the success of these initiatives increasingly depends on the quality, accessibility, governance, and interoperability of enterprise data. This presentation explores the key dimensions of AI data readiness, including modern data platforms, data products, semantic layers, data governance, and operationalization strategies required to transform fragmented data assets into AI-ready capabilities. Attendees will gain a practical framework for assessing current maturity and prioritizing investments that enable scalable, trusted, and business-impacting AI solutions.

Eligibility criteria and Schedules of Activities sit in every major registry as free text, so no system can query them. This session introduces the Clinical Trial Distribution Network (CTDN), a neutral, open-access augmented registry operated by The Synergist that turns registry data into computable HL7 FHIR and CDISC USDM assets at the source. We'll structure a live protocol on stage and show what ships by March.

Digital protocols have the potential to reduce manual study setup and improve interoperability across clinical trial systems, but what is possible today? The SCOPE 365 “Automating the Configuration of Data Acquisition” challenge helped answer that question by bringing together innovators from across the clinical research ecosystem to demonstrate how standards-based digital protocols can automate real-world study configuration activities. Hear directly from winning challenge participants as they share their use cases, how the Unified Study Definitions Model (USDM) supported their solutions, and lessons learned through implementation. Attendees will see how structured protocol data can move the industry beyond manual configuration, improve consistency across systems, and accelerate study start-up while advancing a more automated and interoperable approach to clinical trial execution.

Making the best use of AI in clinical analytics requires more than setting up an MCP server. A strong governance strategy; a well-designed semantic layer; a flexible, dynamic user interface; a thoughtful approach to integration into user workflows; and a pre-established evaluation plan are all essential for any project's success. Providing the strategy and tools to show end users "what's in it for them" leads to faster uptake and impact.

The promise of AI for clinical trial statistical analysis is significant, yet most organizations struggle to move from compelling demos to production-grade capability. The barrier is rarely the model—it is the surrounding infrastructure and domain-specific context needed to make AI trustworthy, reproducible, and fit for regulated work. We share a proof-of-concept MVP of an AI-assisted statistical analysis platform, covering secure back-end infrastructure with human-in-the-loop review and traceable outputs, context engineering grounded in validated, version-controlled statistical modules, and a parallel validation framework. Throughout, the guiding principle is to augment, not replace, statisticians—who retain full accountability for interpretation and decisions.

Clinical studies are foundational to drug development programs, and Tables, Listings, and Figures (TLFs) constitute the primary statistical reporting outputs of these studies. The first step in producing TLFs is compiling a List of Tables, Listings, and Figures (LoT) – a structured inventory specifying every output to be generated – from the study's Statistical Analysis Plan (SAP). As study designs grow in complexity, timely and consistent drafting of LoTs becomes increasingly challenging and labor-intensive, requiring statistical programming leads to manually interpret lengthy and complex SAPs. This manual effort is time-consuming, typically 2-5+ days, and prone to inconsistencies. To address this, we developed an automated agentic system that generates a draft LoT directly from a SAP document. The system uses semantic similarity search over a curated corpus of historical studies to retrieve relevant few-shot exemplars, which – together with domain-specific mapping rules and therapeutic area templates – guide agentic generation of LoT entries by output type. To improve robustness, multiple independent generation iterations are aggregated through a self-consistency step, followed by a second correction pass informed by accumulated meta-review rules. In evaluations across multiple studies, the system achieved an average concordance of 70% — in both precision and recall — at the individual TLF level when compared against finalized LoTs, comparable to the initial draft a statistical programming lead produces before broader study team review and refinement. The system has been deployed and used to accelerate 30+ studies to date, across therapeutic areas and phases, including non-standard phase 3 studies. End-to-end processing typically completes in approximately 10 minutes per study. This approach shows AI-driven automation can meaningfully improve efficiency, consistency, and standardization in clinical trial reporting workflows, supporting scalable and harmonized statistical deliverables across the drug development lifecycle while enabling statistical programmers to focus their expertise on higher-value analytical tasks.