Protocol Optimization
Studies Designed to be Operationally Efficient
Protocol Optimization, part of the Study Experience, helps clinical operations teams design smarter, more efficient trials using AI that leverages standardized, cross-industry clinical trial data. By comparing planned protocols to how similar studies have performed and modeling how design choices affect sites and patients, teams can spot inefficiencies early. This allows sponsors and CROs to refine trial procedures, visit schedules, and endpoints, ensuring trials can both achieve scientific targets and be operationally executable before they even start.
Benefits of Protocol Optimization
Balance Scientific Rigor and Operational Execution
Design smarter studies by leveraging cross-industry benchmarks and predictive modeling to evaluate protocol decisions before your trial begins. Simulate design changes to understand their impact on enrollment, retention, cost, site burden, and patient burden, so that you can strike the right balance between scientific goals and operational success.
Mitigate Risk Before You Begin
Confidently assess the operational feasibility of your protocol using standardized, cross-sponsor data. Identify risk factors early and evaluate the operational impact of design changes before protocol finalization to significantly reduce costly amendments.
Reduce patient and site-burden
Benchmark protocols against real trial data to accurately predict enrollment performance and assess patient and site burden at the activity and visit level. This enables you to present leaner, less burdensome trials to sites, directly improving recruitment and retention outcomes.
Key Features
One of the Industry’s Largest Clinical Trial Datasets
Gain insights from cross-industry, global clinical trial data to design and run more effective studies. Our AI-powered solutions transform site-, patient-, and indication-level data into clear, actionable insights, helping you model scenarios, mitigate risks, and make confident decisions at every stage of your trial.
Activity-Level Metrics
Quantify patient and site burden at the protocol and activity level, along with likely cost drivers, to intelligently guide protocol refinement and manage site expectations and budgets.
AI-Powered Predictive Modeling
Run simulations that forecast the impact of design elements (like procedures and frequency) on key outcomes such as enrollment rates, dropout rates, and timelines. This helps proactively identify and mitigate risks.
Scenario Planning
Model different design options to compare predicted performance across multiple outcomes, including retention, enrollment, costs, and operational complexity, allowing you to optimize design iteratively.
Learn More
Featured Whitepapers and Reports
The State of Black Participation in Clinical Trials
In our recent research report, we used Medidata’s industry-leading clinical trial data repository to assess the current state of racial diversity beyond the FDA Snapshot.
Featured Case Studies
Medidata Selected by Worldwide Clinical Trials to Accelerate Trials and Transform Patient Experience
Medidata and Worldwide Clinical Trials have expanded their partnership to leverage Medidata’s advanced solutions and extensive data set, enhancing Worldwide’s clinical operations with data-driven insights to accelerate trial timelines, improve site selection, and advance the life sciences industry.
Featured Podcasts and Webinars
ASCO Education: Racial Disparities in Clinical Trial Participation
Learn about the state of clinical trial diversity in clinical trials and what clinicians and trial sponsors can do to improve participant diversity.
Featured Blogs
3 Ways to Approach Challenges During Study Feasibility Processes
Industry data and predictive models, in real time, enable pharmaceutical companies and CROs to improve clinical trial site selection, forecast enrollment timelines, and optimize study feasibility through accurate scenario analysis and site performance predictions.
