AI-Powered Clinical-Trial Recruitment: Protocol-to-Claims NLP, Geospatial Density Modeling, and Feasibility Outcomes

Abstract

This technical article examines the transformative role of artificial intelligence in revolutionizing clinical trial recruitment and site selection processes. The traditional approach to trial site selection, historically dependent on enrollment reports and feasibility surveys, is being supplanted by a sophisticated data-driven methodology that integrates multiple dimensions of healthcare information. The article details a multi-layered AI solution that leverages longitudinal claims data, clinical pattern recognition, influence mapping, and geographic intelligence to identify optimal research sites and healthcare professionals. Through a systematic workflow that includes market basket creation, claims analysis, network mapping, and composite ranking, the system creates targeted outreach strategies that significantly improve enrollment efficiency. A case study of a chronic obstructive pulmonary disease trial across numerous sites demonstrates the practical effectiveness of this approach, with substantial enrollment increases at participating research centers. The article argues that this paradigm shift from intuition-led to evidence-optimized strategies represents a fundamental advancement in clinical operations with profound implications for accelerating therapeutic development and expanding patient access to clinical trials.