  Identifying patient cohorts is fundamental to numerous healthcare tasks,
including clinical trial recruitment and retrospective studies. Current cohort
retrieval methods in healthcare organizations rely on automated queries of
structured data combined with manual curation, which are time-consuming,
labor-intensive, and often yield low-quality results. Recent advancements in
large language models (LLMs) and information retrieval (IR) offer promising
avenues to revolutionize these systems. Major challenges include managing
extensive eligibility criteria and handling the longitudinal nature of
unstructured Electronic Medical Records (EMRs) while ensuring that the solution
remains cost-effective for real-world application. This paper introduces a new
task, Automatic Cohort Retrieval (ACR), and evaluates the performance of LLMs
and commercial, domain-specific neuro-symbolic approaches. We provide a
benchmark task, a query dataset, an EMR dataset, and an evaluation framework.
Our findings underscore the necessity for efficient, high-quality ACR systems
capable of longitudinal reasoning across extensive patient databases.
