期刊
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
卷 27, 期 1, 页码 109-118出版社
OXFORD UNIV PRESS
DOI: 10.1093/jamia/ocz165
关键词
biomedical informatics; leaf; cohort discovery; observational health data sciences and informatics; data integration; cloud computing
类别
资金
- National Institutes of Health (NIH), the National Center for Advancing Translational Sciences (NCATS), NIH/NCATS UW-CTSA grant [UL1 TR002319]
- National Institutes of Health (NIH), the National Center for Advancing Translational Sciences (NCATS), NIH/NCATS CD2H grant [U24TR002306]
Objective: Academic medical centers and health systems are increasingly challenged with supporting appropriate secondary use of clinical data. Enterprise data warehouses have emerged as central resources for these data, but often require an informatician to extract meaningful information, limiting direct access by end users. To overcome this challenge, we have developed Leaf, a lightweight self-service web application for querying clinical data from heterogeneous data models and sources. Materials and Methods: Leaf utilizes a flexible biomedical concept system to define hierarchical concepts and ontologies. Each Leaf concept contains both textual representations and 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. Results: Leaf is a successful production-supported tool at the University of Washington, which hosts a central Leaf instance querying an enterprise data warehouse 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. Discussion: Leaf 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. Conclusions: Leaf differs from existing cohort discovery tools because it does not specify a required data model and is designed to seamlessly leverage existing user authentication systems and clinical databases in situ. We believe Leaf to be useful for health system analytics, clinical research data warehouses, precision medicine bio-banks, and clinical studies involving large patient cohorts.
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