Leveraging machine learning for efficient clinical trials

Summary

A global pharmaceutical company transformed its clinical trial operations by developing a machine learning-powered design hub to optimize country allocation and site selection. This innovation led to faster decision-making, reduced costs, and greater operational efficiency in trial management.

Challenge

The pharmaceutical company was grappling with inefficiencies in its clinical trial design process, particularly in the time-consuming and resource-intensive steps of choosing trial countries and site locations. These delays negatively impacted trial timelines, inflated operational costs, and slowed the delivery of critical treatments to market. With increasing pressure to accelerate drug development, the company recognized the need to modernize and streamline its approach to trial planning.

Solution

To address these challenges, the organization partnered with an intelligent automation team to build a machine learning-based design hub that could modernize and optimize the clinical trial design process.

Phase 1: Data migration and integration

The first phase focused on consolidating data from multiple sources into a single, scalable platform. This included migrating internal and external datasets to a unified cloud environment, which improved data accessibility and laid the groundwork for advanced analytics. The team carried out extensive work in data ingestion, cloud migration, and integration, ensuring the infrastructure could support real-time insights and machine learning capabilities. This foundational step was critical to enabling the design hub’s full potential.

Phase 2: Machine learning model development

In the second phase, the team developed a custom machine learning model tailored to predict trial-related parameters. This model played a central role in optimizing the selection of trial countries and sites, significantly reducing the time and cost associated with these decisions. Leveraging AWS Lambda and Python, the team trained, validated, and deployed the model to deliver reliable predictions that guided strategic planning. The machine learning model served as the intelligence engine behind faster and more informed decision-making.

Phase 3: User interface development and adoption

The final phase involved creating a user-friendly interface that would make predictive analytics easily accessible to stakeholders across departments. This interface included a well-designed dashboard and incorporated key elements of UI/UX best practices to ensure usability. Alongside the technical development, the team conducted comprehensive training sessions to drive adoption and ensure that users could effectively leverage the platform’s capabilities. This combination of intuitive design and strong onboarding helped embed the solution within the organization’s workflow.

Results

By deploying the machine learning-enabled design hub, the company experienced a transformative improvement in its clinical trial planning. Operational costs were significantly lowered, and the speed of trial start-ups improved due to quicker and smarter decision-making. Automated processes and optimized resource allocation further enhanced the efficiency and accuracy of trial design. Ultimately, the company gained a competitive edge in bringing treatments to market faster.

Impact

• Enabled faster trial start-ups and shortened timelines
• Reduced operational costs and improved budget efficiency
• Streamlined country and site selection with data-driven insights
• Increased cross-departmental adoption through intuitive tools and training
• Enhanced organizational agility with real-time decision-making capabilities