Biomedical Informatics PhD & MS
Methods Track: Data Analytics & Decision Support
Data Analytics and Decision Support teaches knowledge and skills of data analytics in healthcare, focused on applications that can help clinical decision-making. Students will demonstrate competency in understanding and applying (a) high level mathematical analysis of large data sets, including images, (b) predictive and prescriptive analytic methods and tools, (c) optimal clinical decision support (CDS) interventions for specific healthcare problems, (d) optimal clinical knowledge management principles at health care organizations, including standards for CDS, (e) best practices in the development and evaluation of CDS interventions, and (f) information visualization for CDS. Students with interest in extended training in big data analytics may apply courses required for the School of Computing Big Data Certificate toward their track elective hours.
Recommended Course of Study
This is a recommended schedule for this track (numbers in parentheses indicate credit hours). Courses can be waived or tested out of with permission of the course instructor and the student's advisor. Ultimately, the courses a student takes should be determined and approved by the student and the graduate committee.
Grey = bridge course for those without background in genomics and medicine; light blue = DBMI core course; dark blue = DBMI course for this track; white = electives and research hours.
PhD students are required to attend at least one SIG (all students are encouraged), which bring people with similar interests together to learn, share, and experiment.
Sign up for at least one practicum to gain hands-on experience and work with a team on a project.
Students have the opportunity to apply to work for a semester with the SmartEHR team or the Sociotechnical or NLP service lines on an existing project. Must be coordinated with the team/service line director before registering:
Review and discuss literature in this area.
If there is not a current journal club, encourage your SIG to host one.
Feel free to meet individually with faculty and/or to attend their weekly lab meetings.
Samir Abdelrahman; Bruce Bray; Mollie Cummins, NI; Jennifer Garvin, VA; Guilherme Del Fiol; Scott Evans, Intermountain; Julio Facelli; Bryan Gibson; Peter Haug, Intermountain; Kensaku Kawamoto; Gang Luo; Scott Narus, Intermountain; Dennis Parker; Catherine Staes; Kathy Sward, NI; Charlene Weir, VA.
Laura Heermann-Langford, Nursing Informatics, Intermountain; Jeff Phillips, School of Computing Big Data Science; Heather Sobkho, 3M.