Biobanking informatics infrastructure to support clinical and translational research.

The University of Utah Health Sciences (UUHSC) and Intermountain Healthcare (IH) support high value clinical and translational research programs. The Utah Biohealth Initiative will facilitate next generation research by leveraging the combined resources of both institutions through an infrastructure which links biospecimens and electronic health records (EHR). During phase I of the Utah BioHealth Initiative (UBI) the participating institutions developed a legal, regulatory and information technology infrastructure that supports clinical and translational research, and advances our understanding of health and disease, improves healthcare value and health for current and future generations of Utahns.

Assessing clinical researchers' information needs to create responsive portals and tools: my Research Assistant (MyRA) at the University of Utah: a case study.

Question: How can health sciences librarians and biomedical informaticians offer relevant support to Clinical and Translational Science Award (CTSA) personnel?

Setting: The Spencer S. Eccles Health Sciences Library and the associate vice president for information technology for the health sciences office at the University of Utah conducted a needs assessment.

Methods: Faculty and staff from these two units, with the services of a consultant and other CTSA partners, employed a survey, focus groups, interviews, and committee discussions.

Federated querying architecture with clinical & translational health IT application.

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.