White Paper

Minimizing Luck In Study Feasibility Part 2: Predictive Analytics

Source: Citeline

By Ashley Schwalje, Head of Commercialization for Patient Recruitment & Engagement, Citeline and Daniel Chancellor, Thought Leadership & Consulting, Citeline

AI Data iStock-1223789411

Study feasibility is a fundamental contributor to the overall success of a clinical development program. However, selecting the right countries, sites, and investigators and starting up as quickly as possible remains a challenge, leading to greatly variable performance and inaccurate predictions for important trial milestones. Current approaches can best be described as a blend of art and science, requiring time-intensive data analysis, deep contextual understanding, and a healthy slice of good fortune to meet enrollment targets. In this article, the second of a two-part series, Citeline introduces a first-of-its-kind artificial intelligence (AI) solution for this critical industry problem.

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