Patient Recruitment and Community Engagement Issues in the Spotlight from ACRP 2026

Blog

Of all the “chicken or egg” conundrums inherent in the conduct of clinical research, perhaps the toughest for professionals at study sites to crack concerns which comes first—thoughtful community engagement that leads to robust patient recruitment, or successful patient recruitment that leads to rewarding community engagement. However one chooses to answer the riddle, more than a few presenters at the recent ACRP 2026 gathering in Orlando served up wisdom from the lessons they’ve learned in their research settings about getting recruitment and engagement right.

Leveraging Artificial Intelligence for Clinical Trial Recruitment and Retention

Clinical Researcher

This article examines how artificial intelligence (AI) can supplement human expertise to optimize clinical trial protocols and improve study completion rates by targeting subject recruitment and retention. The integration of AI represents not a replacement of human judgment but a critical bridge—linking innovation with the efficiency and adaptability that modern clinical research urgently requires.

Reimagining the Clinical Research Coordinator in the Age of AI: A Commentary

Clinical Researcher

Increasingly complex and costly clinical trials mean significant increases in workload for frontline clinical research staff such as clinical research coordinators (CRCs). For CRCs, this means higher cognitive load, increased per-patient workload, and a greater probability of errors that can compromise both patient safety and data integrity. This commentary illustrates how the CRC role necessitates a more data-driven and strategic function, while simultaneously preserving core competencies in patient care and clinical judgment. Furthermore, it identifies existing gaps and potential areas for future exploration regarding the practical implementation of artificial intelligence (AI) tools within clinical research workflows.

Bridging the Gap in Clinical Research: A Theory-Driven, Site-Centric Framework for AI Adoption and Literacy

Clinical Researcher

Artificial intelligence (AI) is rapidly transforming clinical research, yet its successful adoption depends on aligning technological innovation with workforce readiness and site-level realities. This article applies the Technology Acceptance Model as a theoretical foundation to examine how clinical research coordinators and clinical research associates perceive the integration of AI into their workflows. It also examines the role of AI literacy as a determinant of preparedness for technology-driven workflows.

Implementation of AI for Future Clinical Data Review: A Literature Review

Clinical Researcher

With an eye toward designing a practical artificial intelligence (AI) architecture for clinical data review (among other goals), this literature review summarizes findings about AI techniques, statistical surveillance, and the regulation of clinical data review. It compares generative AI, natural language processing, discriminatory models, and anomaly detection with reinforcement learning and outlines the purposes of validation and regulation.