School of Medicine

Biomedical Informatics Research

Overview

Research Categories:

The first category is the development and analysis of foundational technologies. These include studies in medical terminologies, data modeling, and the unique characteristics of the service layers required for effective clinical information systems.

The second research category is the development and evaluation of technologies that, through interactions with caregivers or patients, may alter patient care activities. These include decision support applications, the use of the InfoButton model to bring clinical references to the point of care, and a variety of applications and tools designed to streamline and focus clinical workflows to standardize and rationalize therapies at the point of care.

A third research category focuses on the evaluation of existing systems. A student undertaking a project here would identify an existing approach to clinical care, determine how to instrument the care process in such a way that data useful for critical analysis could be collected, and collect and evaluate this data. This activity is key to identifying new opportunities for informatics interventions in health care.

The fourth research category involves the identification and evaluation of novel technologies that can contribute to reaching the over arching goals of Biomedical Informatics. Often these technologies are borrowed from realms such as artificial intelligence, mathematics, computational linguistics, statistics, etc. New opportunities also may be in the form of new hardware or software products, which are configured to fit health care.

The sites where much of this research is done are rich in clinical data collected as a part of the ongoing operation of three health care networks’ medical information systems over many decades. Data mining activities make up a part of many of the research and may be used to help identify a problem, to get baseline data, to create a research sandbox, to train decision support systems, or as a basis for the development of new techniques. The opportunity to access and analyze these data is a critical part of the training environment provided to students and NLM trainees at the University of Utah.