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Federated Querying Architecture with Clinical & Translational Health IT Application

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Abstract

We present a software architecture that federates data from multiple heterogeneous health informatics data sources owned by multiple organizations. The architecture builds upon state-of-the-art open-source Java and XML frameworks in innovative ways. It consists of (a) federated query engine, which manages federated queries and result set aggregation via a patient identification service; and (b) data source facades, which translate the physical data models into a common model on-the-fly and handle large result set streaming. System modules are connected via reusable Apache Camel integration routes and deployed to an OSGi enterprise service bus. We present an application of our architecture that allows users to construct queries via the i2b2 web front-end, and federates patient data from the University of Utah Enterprise Data Warehouse and the Utah Population database. Our system can be easily adopted, extended and integrated with existing SOA Healthcare and HL7 frameworks such as i2b2 and caGrid.

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Abbreviations

AOP:

Aspect-Oriented Programming

CCTS:

Center for Clinical and Translational Science

DQC:

Data source QueryContext

DS:

Data Source

DTS:

Distributed Terminology Server

ESB:

Enterprise Service Bus

ID:

Identifier

FQC:

Federated QueryContext

FQE:

Federated Query Engine

FURTHeR:

Federated Utah Research & Translational Health e-Repository

i2b2:

Informatics for Integrating Biology and the Bedside

JMS:

Java Message Service

MDR:

Metadata Repository

OSGi:

Open Services Gateway Initiative

QC:

QueryContext

SOA:

Service-Oriented Architecture

Spring DM:

Spring Dynamic Modules

UUEDW:

University of Utah Enterprise Data Warehouse

UPDB:

Utah Population Database

UPDBL:

Utah Population Database Light

VR:

Virtual Repository

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Acknowledgments

The authors would like to acknowledge Susan Matney for her work on the FURTHeR terminology server, and Richard Bradshaw for his work on the FURTHeR metadata repository. The present manuscript is an extension of our ACM IHI 2010 paper entitled “Federated Querying Architecture for Clinical & Translational Health IT” [16]; see Appendix for more details.

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Authors and Affiliations

  1. Office of AVP for Health Sciences IT, University of Utah, 26S 2000E, Salt Lake City, UT, 84112, USA

    Oren E. Livne, N. Dustin Schultz & Scott P. Narus

Authors
  1. Oren E. Livne
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  2. N. Dustin Schultz
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  3. Scott P. Narus
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Corresponding author

Correspondence to Oren E. Livne.

Additional information

This investigation was supported by Public Health Services research grant UL1-RR025764 from the National Center for Research Resources and by the National Library of Medicine grant 1RC2LM010798 from the National Institutes of Health. The authors wish to thank the Pedigree and Population Resource and Research Informatics Groups at the Huntsman Cancer Institute and the University of Utah Enterprise Warehouse Team.

Appendix: New material included in this paper

Appendix: New material included in this paper

The present manuscript is an extension of our ACM IHI 2010 paper entitled “Federated Querying Architecture for Clinical & Translational Health IT” [16]. We included over 30% of new content. The main additions are

  • Section “Existing Federation Solutions”: discussion and comparison of our system with the Garlic engine.

  • Section “Aggregation via an In-memory Database”: describes in more detail the methodology of result set aggregation from multiple data sources and duplicate record resolution.

  • Section “Query Context”: a discussion of supported query expressions and specific health care applications.

  • Section “Data Source Flow”: The DS Flow was updated to support result set paging critical to large result set processing.

  • Section “Cancelling a Query”: we added a new command type called CANCEL for cancelling a query and modified the DS flow to support in-vivo cancellation requests.

Section “Implementation”: an entirely new section describing the application of our system to querying two live data sources, the University of Utah Data Warehouse and the Utah Population Database, and our integration with the i2b2 web front-end.

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Livne, O.E., Schultz, N.D. & Narus, S.P. Federated Querying Architecture with Clinical & Translational Health IT Application. J Med Syst 35, 1211–1224 (2011). https://doi.org/10.1007/s10916-011-9720-3

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