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

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 biobanks, and clinical studies involving large patient cohorts.

LC Data QUEST: A Technical Architecture for Community Federated Clinical Data Sharing.

The University of Washington Institute of Translational Health Sciences is engaged in a project, LC Data QUEST, building data sharing capacity in primary care practices serving rural and tribal populations in the Washington, Wyoming, Alaska, Montana, Idaho region to build research infrastructure. We report on the iterative process of developing the technical architecture for semantically aligning electronic health data in primary care settings across our pilot sites and tools that will facilitate linkages between the research and practice communities. Our architecture emphasizes sustainable technical solutions for addressing data extraction, alignment, quality, and metadata management. The architecture provides immediate benefits to participating partners via a clinical decision support tool and data querying functionality to support local quality improvement efforts. The FInDiT tool catalogues type, quantity, and quality of the data that are available across the LC Data QUEST data sharing architecture. These tools facilitate the bi-directional process of translational research.

Facilitating health data sharing across diverse practices and communities.

Health data sharing with and among practices is a method for engaging rural and underserved populations, often with strong histories of marginalization, in health research. The Institute of Translational Health Sciences, funded by a National Institutes of Health Clinical and Translational Science Award, is engaged in the LC Data QUEST project to build practice and community based research networks with the ability to share semantically aligned electronic health data. We visited ten practices and communities to assess the feasibility of and barriers to developing data sharing networks. We found that these sites had very different approaches and expectations for data sharing. In order to support practices and communities and foster the acceptance of data sharing in these settings, informaticists must take these diverse views into account. Based on these findings, we discuss system design implications and the need for flexibility in the development of community-based data sharing networks.