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Related Experiment Video

Updated: May 20, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

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

Kari A Stephens¹, Ching-Ping Lin, Laura-Mae Baldwin

¹Institute of Translational Health Sciences, University of Washington, Seattle, WA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|July 11, 2012

Summary

This summary is machine-generated.

The LC Data QUEST project developed a technical architecture for sharing electronic health data in rural primary care settings. This infrastructure supports data alignment and linkage, benefiting both research and quality improvement initiatives.

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Last Updated: May 20, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

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.