Archives: Agenda

• Explores a “site ID in one click” roadmap that combines deep industry expertise with a first-of-its-kind semantic knowledge platform.
• Demonstrates how 30 years of operational data, paired with external insights from 500k+ institutions and 3M sites, strengthens feasibility decision-making.
• Shows how expertise-driven processes and advanced data integration reduce the industry’s 50% non-enrollment rate.
• Highlights how this methodology enables sponsors to accelerate timelines, reduce costs, and improve overall trial efficiency.

Everyone talks about AI, but few are showing production use cases in Quality Management. In this session, Roche shares a practical look at their strategy for integrating GenAI into the Quality workflow. We will discuss the methodology behind using AI to enhance Risk-Based Quality Management (RBQM) and how we are leveraging unstructured data—like historical scribe notes—to modernize inspection readiness.

• AI in RBQM: How AI supports the shift from reactive issue management to proactive risk identification, allowing teams to spot signals earlier in the trial lifecycle.
• The “Mock Auditor” (Inspection Defense): A case study on analyzing unstructured data (such as scribe notes and deviation text) to predict auditor behavior and prepare teams for questioning.
• The Trust Factor: Best practices for governance, validation, and maintaining a “Human-in-the-loop” when deploying GenAI in a GxP environment.

• How much data is good enough to make the decisions that we need?
• Defining the role of real-world data in clinical research to assess the effectiveness of treatments
• Prioritizing the timeliness of data to identify safety signals early and increase trial credibility: how are companies balancing their needs vs the requirements of the patients?
• Ongoing debate for in-house vs external data managers: when is it best to outsource?

• Outlining key differences in company budgets and pipelines
• How to align financial incentives between CROs and pharma so that both sides win
• Uniting in common challenges amongst all sponsors
• Lessons learned from partnerships that improved performance
• Balancing trust and transparency across the ecosystem
• Patient safety, innovation and adaptability as shared goals

• AI is poised to streamline the design, deployment, and monitoring of clinical trials.
• Clinical ink has developed AI solutions for every trial stage, all within a robust governance framework for safety and compliance.

• Challenges of data silos and interoperability across research and clinical
• Strategies for creating a cohesive data ecosystem that supports translational research
• Case examples of successful data integration improving patient outcomes or accelerating drug development
• Predictive and prognostic biomarker discovery through advanced analytics.
• Regulatory and validation considerations for clinical implementation

• Future-facing models: What will define a ‘remarkable’ trial in the next decade?
• Leveraging adaptive and decentralized trial designs to improve flexibility and speed
• Empowering sites and CROs as partners to improve patient outcomes
• Strengthening communication to avoid misaligned expectations in the face of global disruptions
• Reducing costs and environmental impact while maintaining scientific integrity

• Reframe patient centricity from “initiative” → “operating model”
• Patient centricity is no longer a differentiator
  → It is a baseline expectation from regulators, investors, and patients
• Yet most organizations still treat it as:
  • A set of tools
  • A functional workstream
  • Or a late-stage overlay

• Patient centricity only scales when it is embedded into how we select, contract, govern, and operate with vendors

• Outsourcing decisions shape: study design feasibility, site experience, patient burden, speed, quality, and retention
• If patient centricity is not designed into outsourcing, it will not survive execution