AI?Native Patient Recruitment Linked to Decentralized Clinical Research Phlebotomy: Trial Diversity, Enrollment Speed, and Biospecimen Quality

Purpose: Persistent underrepresentation of racially, ethnically, and socioeconomically diverse populations, coupled with protracted enrollment timelines, continues to compromise the generalizability, external validity, and equity of clinical research. Blood sampling, among the most frequently mandated in-person procedures, presents logistical barriers that inadvertently impede participation, retention, and biospecimen quality. Travel requirements, scheduling constraints, and inconsistent specimen handling disproportionately burden older adults, rural populations, and participants with limited temporal or transportation flexibility. We describe a novel, AI-integrated patient recruitment paradigm operationally coupled with decentralized clinical research phlebotomy and supported by digital chain-of-custody documentation and quality oversight. We present key operational outcomes associated with this integrated approach. Design: We conducted a comprehensive operational evaluation of an AI-enabled recruitment workflow integrated with mobile and community-based clinical research phlebotomy across multiple trial sites. Outcomes were compared descriptively against historical controls utilizing conventional, site-centric recruitment and phlebotomy workflows, facilitating assessment of enrollment efficiency, demographic representativeness, and operational performance. Result: The AI-enabled recruitment system identified 2,350 potential candidates and facilitated enrollment of 320 participants, representing 64% of the total study population (N = 500). Compared with historical controls, time to complete enrollment decreased from 12 months to 8 months 33% reduction. Concurrently, the proportion of enrolled participants from underrepresented populations increased from 22% to 38%, demonstrating substantial enhancement in trial representativeness without compromising operational feasibility. An AI-integrated recruitment strategy coupled with decentralized clinical research phlebotomy can substantially accelerate enrollment while enhancing demographic representativeness. When combined with standardized digital workflows and rigorous chain-of-custody oversight, this model preserves biospecimen integrity and data reliability, positioning phlebotomy as a strategic enabler of equitable, efficient, and high-quality clinical research.