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Leveraging AI Solutions for Clinical Trial Efficiencies

Discover how innovative tools like AI and machine learning are revolutionizing critical challenge areas of trial management and operations.

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As clinical trials become increasingly complex, particularly in decentralized trials and rare disease studies, sponsors experience increased challenges in site selection, forecasting and resourcing, and patient recruitment and enrollment. Advanced artificial intelligence (AI) and machine learning solutions are revolutionizing critical challenge areas. These innovative tools are essential for effectively addressing and managing clinical trial complexities, optimizing key aspects of trial management and ensuring streamlined operations.

Keep reading to learn key considerations for leveraging AI and machine learning to enable faster, more accurate and confident decision-making processes for improved study outcomes.

AI and machine learning considerations for effective clinical trial management

Six AI and machine learning considerations for effective clinical trial management

1. Accelerate customer speed to market

With a modern and integrated user experience, AI solutions put the right data and insights into the right hands in real time. As a result, drug developers make better decisions more quickly (removing 50% of study timeline whitespace) to bring new therapies to market faster.

2. Predict activation timelines

A data-driven, automated process to predict activation dates simplifies complex variability into standard practices. AI tools create unbiased assessments of predicted activation dates favoring proven influencing factors over intuition. Creating a standardized formula and process for determining predicted activation dates leads to greater transparency, accuracy and trust.

3. Proactively resolve issues

Automated issue detection and strengthened mitigation strategies enable improved monitoring of changes as they occur in the system. As a result, negotiations are completed almost a month faster, leading to reduced sponsor escalations and an accelerated contracting pipeline.

4. Focus on enrollment improvement efforts

Recruitment is vital to trial success but the ability to recruit varies greatly; some clinical trial sites fail to recruit a single patient, while others underperform. Enrollment improvement interventions vary – the key is finding the sites at risk and creating action plans.

Early detection of enrollment risk is critical for successful mitigation. AI tools better predict which sites will encounter issues before a human can conclude there is trouble. As risks are identified, customized action plans are created and provided to clinical teams to keep studies on track.

5. Modernize resource planning

Automating resource demand forecasting for studies using machine learning and inputs from AI-driven solutions helps increase productivity and ensures timely and accurate data. An innovative approach to capacity management involves enhanced forecasts and leveraging AI for capacity predictions and hiring and turnover considerations. Redesigned reporting helps streamline and merge current solutions with persona-based views for a more comprehensive perspective.

6. Proactively reforecasting enrollment activation for Phase I cohorts and Phase II-III studies

Using the combined power of machine learning, real-time actuals and subject matter expertise, project delivery teams gain a more holistic approach to forecasting/reforecasting site activation and patient enrollment. This functionality better facilitates recruitment reforecasting and provides project teams with more robust data to drive client discussions and identify mitigation strategies.

About us

The PPD™ clinical research business of Thermo Fisher Scientific, the world leader in serving science, enables our customers to accelerate innovation and increase drug development productivity. Using patient-centered strategies and data analytics, our capabilities cover multiple therapeutic areas and include early development, all phases of clinical development, peri- and post-approval, novel approaches to patient recruitment and investigator sites and comprehensive laboratory services.

Ready to learn more about how to leverage AI and machine learning to maximize your clinical trial efficiency? Get in touch.

Discover how AI is used to optimize key aspects of clinical trial management.

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