Decentralized Clinical Trials (DCTs) introduce new complexities in ensuring data integrity due to distributed data sources, remote patient interactions, and increased reliance on digital technologies. This session will explore strategies to maintain ALCOA+ principles across diverse data streams. It will also address regulatory expectations and risk-based approaches to governing data collection, transfer, and storage in a decentralized model.

• Understand the unique data integrity risks associated with decentralized clinical trial models.
• Apply regulatory expectations (e.g., ALCOA+, GxP, and data governance principles) to ensure accuracy, reliability, and traceability of decentralized data.
• Identify risk-based controls and strategies to maintain data integrity across multiple technologies.

With the final release of ICH E6(R3) in January 2025, sponsors and CROs are shifting from conceptual understanding of Risk-Based Quality Management (RBQM) to embedding it into day-to-day clinical trial execution. A persistent challenge remains the effective, practical use of Key Risk Indicators (KRIs) and Quality Tolerance Limits (QTLs) linked to Critical-to-Quality (CtQ) factors—particularly in detecting and interpreting early risk signals before they impact patient safety or data integrity.

• Define CtQ factors with intent: Translating patient safety, data integrity, and protocol compliance requirements into clear, actionable CtQs.
• Design fit-for-purpose KRIs and QTLs: Establishing meaningful thresholds and control limits, and integrating them into data analytics dashboards that support timely, risk-based decision-making.
• Respond to CtQ excursions: Defining monitoring approaches and applying critical thinking to interpret potential risk-signals, assess root cause, and drive proportionate, effective responses.

Fraud and misconduct represent significant risks to data integrity, patient safety, and regulatory compliance in clinical research. While Good Clinical Practice guidelines provide a global standard for conducting research, the lack of a strict framework for managing research fraud and misconduct leaves critical gaps. Studies have shown that over 40% of researchers are aware of misconduct but choose not to report it, further complicating efforts to maintain integrity. How can your team implement effective preventive measures and foster a culture of transparency to address these issues?

• Reveal the best ways to detect and prevent fraud
• Understand what other countries are doing to prevent fraud

Automation and AI are no longer incremental improvements; they are fundamentally reshaping the drug development lifecycle. From experiment design to data capture and decision-making, end-to-end automation is enabling faster timelines, higher-quality data, and more reproducible outcomes. How can organizations leverage these technologies to transform operating models while improving quality? Does this have an impact on the quality of organizations?

• Understand what it takes to operationalize automation at scale, embedding it into workflows, ensuring traceability, and aligning SOPs to support audits and compliance
• Learn how automation can shift the focus from volume of experiments to reliability of evidence and better decision-making

Highlight how Merck's Quality Assurance organization adopts a deliberate strategy to integrate Generative AI into the Clinical Quality Assurance space, with the goal of enhancing auditor effectiveness, augmenting data-driven insights, and increasing organizational capacity. It delivered immediate workflow efficiency while building foundational AI literacy and confidence across the team. Beyond tool development, the QA team is leading an enterprise-level workstream to establish clear guardrails for AI usage in GxP-regulated environments, providing the organization with the clarity, controls, and the governance framework needed to adopt AI responsibly and at speed.

• Share the rationale and implementation of key tools that enable QA auditors to rapidly navigate and surface relevant citations from internal policies, procedures, and external regulatory guidance.
• Highlight how our AI capabilities have evolved to a more sophisticated capability that assists auditors in generating first drafts of audit reports from their field notes, including AI-recommended severity classifications, finding categorizations, and draft finding language — significantly accelerating the report-writing cycle while preserving auditor judgment and oversight.
• Show how these efforts position our colleagues to work smarter, move faster, and maintain the quality standards our patients depend on

As central labs consolidate and specialty testing is being heavily impacted, organizations are facing new risks to data integrity and operational alignment. How can quality and business functions work together to strengthen vendor relationships, improve communication across sponsors and partners, and proactively embed quality into sample and vendor management processes to ensure reliable, compliant trial execution?

• Address data integrity risks emerging from central lab consolidation and fragmented communication
• Strengthen collaboration with sample management and specialty testing partners
• Embed quality into vendor management strategies by aligning quality and business functions

CRO partnerships can accelerate study execution, or become a critical bottleneck when roles, expectations, and performance metrics are unclear. In order to find the bottleneck, there need to be performance measurements in place. How can you measure the performance of a CRO and capture that data? In addition, how can you determine the scope of the CROs responsibility vs the sponsor's responsibility?

• Implement consistent methods to capture and track performance data across CRO partners
• Define the boundaries between sponsor oversight responsibilities and CRO operational execution
• Establish meaningful performance metrics to identify risks, address bottlenecks, and drive CRO accountability