Designing a clinical trial protocol without early, meaningful input from both patients and sites risks inefficiencies, delays, and avoidable amendments. In this session, we’ll explore how incorporating structured patient insights alongside site engagement before protocol finalisation can lead to more operationally feasible, patient-friendly studies. We’ll share practical tools and approaches for gathering and integrating this feedback—ranging from digital platforms to advisory boards—and outline a replicable process that bridges scientific intent with real-world execution. Attendees will gain a clear understanding of how early insights shape better protocols, reduce downstream burdens, and ultimately support faster, more inclusive trial execution.

Effective Program, Study, and Site Engagement is critical to successfully deliver clinical research projects. Especially in rare diseases and hard-to-recruit patient populations, it is key to define strategies to support patients and sites in any possible way. This session will highlight how CSL aligns engagement strategies across the organisation and a way of bringing clinical trials closer to the patients and expanding CSL’s site network. Attendees will gain practical insights and inspiration to rethink their own engagement approaches across the clinical development lifecycle.

In this talk, we explore innovative approaches to streamline feasibility processes and reduce costs in clinical trial execution. We focus on how data science and generative AI can simplify workflows, enhance efficiency, and lower expenses. Automating data analysis and site selection reduces time and resources, while predictive analytics identify high-performing sites early, minimising costs from trial delays and underperforming sites.

In the rapidly evolving landscape of clinical development, leveraging data-driven methodologies has become paramount for enhancing feasibility assessments and optimising trial designs. This presentation explores an end-to-end data-driven approach that integrates machine learning techniques, external vendor data, and artificial intelligence (AI) to streamline clinical development processes. We begin by discussing the importance of feasibility studies in clinical trials and how traditional methods often fall short in accurately predicting site performance and patient recruitment. By incorporating machine learning algorithms, we can analyse vast datasets to uncover patterns and insights that inform strategic decision-making. Furthermore, the utilisation of external vendor data, such as electronic health records, claims data, and patient registries, enriches our understanding of patient populations and geographic considerations. This multifaceted approach allows for a more comprehensive evaluation of trial feasibility, ultimately leading to more efficient resource allocation and improved patient outcomes. We will also highlight the successful application of AI-driven analytics in identifying suitable trial sites, optimising patient recruitment strategies, and enhancing overall trial efficiency. The integration of these advanced technologies not only mitigates risks associated with clinical development but also fosters innovation in the design and execution of trials. In conclusion, this presentation aims to showcase the transformative potential of an end-to-end data-driven framework in clinical development, emphasising the synergistic role of machine learning, external vendor data, and AI in achieving successful trial outcomes.

Explore new strategies and a data-driven approach by leveraging AI to improve site selection and diversity in trial feasibility, helping to reduce bottlenecks, improve recruitment readiness, and enhance overall study execution.

A year has passed since AstraZeneca implemented enhancements to our feasibility process, aimed at fostering active engagement from the initial interaction through site selection. Over the past year, we’ve had the opportunity to test these improvements, gather insights, and refine our approach. In this session, we will share the valuable learnings from this journey, discuss the additional updates we’ve made, and outline our future plans to further optimize the process.

FORCE (Optimising Operational Study Design Feasibility and Site Identification for Reliable Clinical Study Execution) revolutionises oncology feasibility frameworks. By enhancing processes, technology, and role accountability, FORCE improves study predictability and site engagement. It integrates operational feasibility insights, aligns therapeutic area strategies, and streamlines execution, fostering a sponsor-preferred choice. Join us to explore how FORCE has transformed feasibility, driving quicker, reliable study execution in oncology clinical trials.

During this discussion, you will hear how Boehringer Ingelheim’s Clinical Development Operations Feasibility and Planning combines data-driven analyses with early local country feedback to reduce protocol amendments, reduce timelines, maximise protocol optimisation and enroll-ability, and develop robust Monte Carlo simulations for timeline planning.

How can we harness our growing understanding of the environmental impact of clinical trials to drive meaningful change? This session will delve into the challenges and innovative strategies that empower organisations to integrate environmental metrics into clinical trial design. It will explore the opportunities to lower the environmental impact while simultaneously unlocking significant co-benefits to elevate the entire clinical trial process.