This paper explores how algorithm-assisted patient identification addresses chronic trial under-enrollment. Traditional manual screening is slow and error-prone, often leading to delays, higher costs, and underpowered studies. By using query-driven algorithms on electronic health records (EHRs), research sites can quickly identify eligible patients, even for rare diseases. Benefits include faster feasibility assessments, larger candidate pools, reduced site burden, and improved trial performance. Automated extraction also ensures compliance with privacy rules and supports more diverse, representative recruitment.

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