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

Area of Science:

• Health Informatics
• Translational Science
• Rural Health

Background:

• Primary care practices in rural and tribal areas often lack robust research infrastructure.
• Limited capacity for electronic health data sharing hinders translational research in these underserved populations.
• The University of Washington Institute of Translational Health Sciences initiated LC Data QUEST to address these challenges.

Purpose of the Study:

• To develop and implement a sustainable technical architecture for semantically aligning electronic health data.
• To create tools facilitating linkages between research and practice communities.
• To enhance data sharing capacity in primary care settings serving rural and tribal populations.

Main Methods:

• Iterative development of a technical architecture for data extraction, alignment, quality control, and metadata management.
• Implementation of a clinical decision support tool and data querying functionality.
• Development of the FInDiT tool to catalogue data type, quantity, and quality.

Main Results:

• A sustainable technical architecture for electronic health data sharing was established.
• Participating partners gained immediate benefits through clinical decision support and data querying tools.
• The FInDiT tool provides comprehensive data cataloguing within the architecture.

Conclusions:

• The developed architecture effectively addresses technical challenges in electronic health data sharing.
• The project facilitates bi-directional translational research by linking data and communities.
• This initiative builds essential research infrastructure for primary care practices in underserved regions.