DATA-DRIVEN APPROACHES TO IMPROVING PATIENT RECRUITMENT AND RETENTION IN U.S. CLINICAL TRIALS: CURRENT STRATEGIES AND FUTURE DIRECTIONS

Recruiting and retaining patient’s remains one of the most important issues in clinical trials, where about 80% of the trials fail to achieve their enrolment schedule, and 30 percent of the recruited subjects do not see through the end of the trial period. The impact of these challenges is a delay in the development of drugs, a rise in the cost, and scientific invalidity. The appearance of data-centered strategies, such as artificial intelligence (AI), machine learning (ML), big data analytics, and integration of electronic health record (EHR), has given hope towards finding solutions to these time-old hindrances. The paper is a critical review of existing evidence-based practices for enhancing patient recruitment and retention in U.S. clinical trials and analyzes their successes and future research and practice directions. Some of the methodologies that we analyzed are natural language processing, predictive analytics, goal programming, deep learning models, and HER-based surveillance systems.
The current approaches indicate that their efficiency is surprisingly high in recruitment, and AI-driven systems yielded results of 40-70% faster screening time, 23-fold more valid patients, and varied enrollment rates up to 15-45%. The main technologies are NLP-based eligibility screening, enrollment success predictive modeling, automated systems of patient-trial matches, and Real-Time EHR. Nevertheless, equity and diversity challenges, interoperability of data, regulations, and practical use continue to be experienced. Data-driven approaches are a paradigm shift in the domain of clinical trial recruitment and retention, and they provide tangible advances in the areas of efficiency, cost-effectiveness, and identification of patients. In line with future developments, it is possible to estimate more profound integration of large language models, privacy-preserving analytics by using federated learning, adaptive recruitment policies in real-time, and frameworks to assure equitable representations. It needs interdisciplinary cooperation, effective regulation mechanisms, and further advancement of AI/ML approaches to achieve successful implementation