Leaf -- An open-source, model-agnostic, data-driven web application for cohort discovery and translational biomedical research

ObjectiveAcademic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of data that originate from multiple sources. Enterprise Data Warehouses (EDWs) have emerged as central resources for these data, but they often require an informatician to extract meaningful information, thereby limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying and extracting clinical data from heterogeneous data sources.\n\nMaterials and MethodsLeaf utilizes a flexible biomedical concept system to define hierarchical items and ontologies. Each Leaf concept contains both textual representations and associated SQL query building blocks, exposed by a simple drag-and-drop user interface. Leaf generates abstract syntax trees which are compiled into dynamic SQL queries.\n\nResultsLeaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an EDW with over 300 active users. Through the support of UW Medicine (https://uwmedicine.org), the Institute of Translational Health Sciences (https://www.iths.org) and the National Center for Data to Health (https://ctsa.ncats.nih.gov/cd2h/), Leaf source code has been released into the public domain at https://github.com/uwrit/leaf.\n\nDiscussionLeaf allows the querying of single or multiple clinical databases simultaneously, even those of different data models. This enables fast installation without costly extraction or duplication from existing databases.\n\nConclusionLeaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly integrate with existing enterprise user authentication systems and clinical databases in situ. We demonstrate its unique technical strengths and success alongside its friendly user interface. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine biobanks and clinical studies involving large patient cohorts.