Leveraging Advanced Analytics And AI To Expand Clinical Trial Recruitment Beyond Conventional Diagnoses

Clinical trial recruitment often misses a critical group of potential participants by focusing solely on patients with a formal diagnosis. However, emerging research suggests that many conditions previously considered distinct may share significant genetic and phenotypic overlaps. Clinical operations leaders are growing to believe that advanced analytics and artificial intelligence can transform study recruitment by moving beyond these rigid boundaries.

This article outlines how leveraging diverse, large-scale patient datasets enables the identification of individuals who may be at risk for a condition, or who have related comorbidities, even without a formal diagnosis. Learn how technologies, such as machine learning and natural language processing, can discern patterns and assign a “likelihood score” to potential candidates. By adopting this innovative approach, operations leaders can significantly broaden their recruitment funnel, leading to earlier patient intervention and accelerated research timelines. This not only enhances enrollment speed but also improves the external validity of study findings by including a more representative patient population.