Overcoming issues of non-enrolling sites in clinical trials
Navigating non-enrolling site challenges to improve trial outcomes
Discover how proactive machine learning approaches allow sponsors to identify sites at risk. These insights improve site performance, effectively allocate resources and reduce delays.
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Address and navigate non-enrolling clinical trial site challenges
Non-enrolling clinical trial sites increase costs, delay development timelines and impact trial success. Yet conventional site-management approaches frequently rely on lagging indicators such as missed enrollment targets, meaning problems may not become visible until they are already affecting the study.
Machine learning reduces the risk of non-enrolling clinical trial sites. Predictive analytics approaches analyze historical enrollment, site performance, investigator experience, demographics, site infrastructure and trial-specific variables to identify sites at risk of underperformance. Sponsors may then use those insights to focus resources and tailor interventions before enrollment problems become critical.
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Moving from reactive site management to proactive outreach
Non-enrolling sites contribute to costly delays and inefficient resource allocation. Instead of waiting for sites to miss enrollment targets, machine learning allows clinical teams to identify potential risks earlier and take more targeted action.
This white paper highlights three models to assess non-enrollment risk before the site initiation visit, at site activation and 60 days after activation, providing corresponding action plans for clinical teams. Additionally, it explores how a proactive, data-driven approach enables sponsors to:
- Identify sites at risk of non-enrollment at multiple points in the study lifecycle using predictive analytics.
- Tailor site support based on specific challenges, from patient recruitment and community engagement to training and data management.
- Continuously refine interventions using ongoing monitoring and feedback to help keep sites on track.
- Focus resources where they are needed most by giving clinical teams actionable insights to guide site engagement.
An included real-world case study demonstrates how predictive analytics and proactive outreach were used to identify and support high-risk sites, with identified sites enrolling subjects earlier than expected.
Machine learning provides study teams with an earlier view of enrollment risk and helps transform that insight into targeted action. Instead of waiting for missed targets, sponsors can focus attention and resources where they may have the greatest impact.
Discover predictive approaches to make clinical trial site management more proactive and see how machine learning-driven site outreach works in practice.
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