By Jeffrey G Klann , Michael D Buck , Jeffrey Brown , et al.
Understanding population-level health trends is essential to effectively monitor and improve public health. The Office of the National Coordinator for Health Information Technology (ONC) Query Health initiative is a collaboration to develop a national architecture for distributed, population-level health queries across diverse clinical systems with disparate data models. Here we review Query Health activities, including a standards-based methodology, an open-source reference implementation, and three pilot projects.
Materials and methods
Query Health defined a standards-based approach for distributed population health queries, using an ontology based on the Quality Data Model and Consolidated Clinical Document Architecture, Health Quality Measures Format (HQMF) as the query language, the Query Envelope as the secure transport layer, and the Quality Reporting Document Architecture as the result language.
We implemented this approach using Informatics for Integrating Biology and the Bedside (i2b2) and hQuery for data analytics and PopMedNet for access control, secure query distribution, and response. We deployed the reference implementation at three pilot sites: two public health departments (New York City and Massachusetts) and one pilot designed to support Food and Drug Administration post-market safety surveillance activities. The pilots were successful, although improved cross-platform data normalization is needed.
This initiative resulted in a standards-based methodology for population health queries, a reference implementation, and revision of the HQMF standard. It also informed future directions regarding interoperability and data access for ONC’s Data Access Framework initiative.
Query Health was a test of the learning health system that supplied a functional methodology and reference implementation for distributed population health queries that has been validated at three sites.
Query Health: standards-based, cross-platform population health surveillance Jeffrey G Klann , Michael D Buck , Jeffrey Brown , Marc Hadley , Richard Elmore , Griffin M Weber , Shawn N Murphy Journal of the American Medical Informatics Association Jul 2014, 21 (4) 650-656; DOI: 10.1136/amiajnl-2014-002707