Trial Site Selection

Trial site selection for clinical trials is evolving to prioritize both efficient patient recruitment and equitable representation of diverse populations. Research focuses on developing machine learning models, including deep reinforcement learning and fair policy learning approaches, to optimize site selection by simultaneously considering factors like patient demographics, site capabilities, and projected enrollment rates. These advancements aim to improve the efficiency and inclusivity of clinical trials, leading to more generalizable and reliable treatment outcomes across various patient groups. Improved recruitment prediction models further enhance trial design by providing more accurate enrollment timelines.