Clinical trials optimization was facilitated by developing DeepMatch (DM), a novel approach based on the recent advances in deep learning. DM was designed to learn from both investigator and trial-related heterogeneous data sources and rank investigators based on their expected enrollment performance on new clinical trials.

A large-scale evaluation was conducted on 2618 studies in which the proposed ranking-based framework improved the current state-of-the-art by up to 19% on ranking investigators and up to 10% on detecting top/bottom performers when recruiting investigators for new clinical trials. These findings indicated that DM can provide substantial improvement over current industry standards in several regards: (1) the enrollment potential of the investigator list, (2) the time it takes to generate the list, and (3) data-informed decisions about new investigators.