518 Improving Clinical Trial Activation Timelines through Parallel Processing and Key Stakeholder Involvement

Abstract

OBJECTIVES/GOALS: A strategic initiative of Mayo Clinic is to decrease clinical trial activation timelines by 25% from 2022 levels. The team’s goal is to streamline activation via parallel processing, improved collaboration with business units, project manager facilitation, and early study coordinator involvement. METHODS/STUDY POPULATION: The workgroup targeted industry trials, focusing on key financial, regulatory, and operational elements. Current state process workflows and pain points were prepared and opportunities for concurrent activities or automation identified. The scope of the project manager role from the Office of Clinical Trials was extended to guide each activation team, who have varying levels of experience, to maintain timelines until the trial is opened. Coordinators responsible for study conduct engaged in key operational discussions earlier to ensure the trial will be run successfully. A Pilot program is utilizing the identified concurrent activities, project manager support, and earlier coordinator involvement to gauge effectiveness of the proposed solutions. RESULTS/ANTICIPATED RESULTS: The goal is for 120 industry clinical trials to join the Pilot program and to open enrollment in less than 30 weeks from being document ready. As of Q3 2023, 109 clinical trials across multiple Mayo sites have enrolled in the pilot. Thirty-five (85 percent) of 41 Pilot trials have opened to enrollment and have met the goal, with a median timeline of 24 weeks. Twenty-one (21) trials opened to enrollment in Q3 2023 with a median timeline of 23 weeks, representing 24% of all industry clinical trials opened that quarter. Opening trials and monitoring is ongoing and PI and study team feedback is positive. DISCUSSION/SIGNIFICANCE: Using a team-based approach, we identified key areas for optimization and parallel processing. The solution reduces trial activation timelines, increases patient access to experimental therapies, and has been positively received by study staff. Future projects will focus on enterprise implementation, optimization, and automation.