Erika Moen’s research program uses state-of-the-art biomedical informatics and data science methods to study variation in cancer care delivery and patient outcomes. Specific interests include evaluating dynamic national patient-sharing networks to examine variation in cancer care care coordination and diffusion of novel cancer tests and treatments. The ultimate goal of Dr. Moen’s research is to contribute to efforts in streamlining high quality cancer care to optimize patient outcomes.
She holds a BA in biology from Brown University and an MS in translational science and PhD in cancer biology from the University of Chicago.
Schwedhelm TM, Rees JR, Onega T, Zipkin RJ, Schaefer A, Celaya MO, Moen EL
BMC Cancer|2020 Sep 3
Basson AR, LaSalla A, Lam G, Kulpins D, Moen EL, Sundrud MS, Miyoshi J, Ilic S, Theriault BR, Cominelli F, Rodriguez-Palacios A
Sci Rep|2020 Mar 19
O'Malley AJ, Moen EL, Bynum JPW, Austin AM, Skinner JS
Stat Med|2020 Apr 15
Lee JW, Moen EL, Punshon T, Hoen AG, Stewart D, Li H, Karagas MR, Gui J
Comput Biol Med|2019 Nov
Moen EL, Bynum JPW
J Gen Intern Med|2019 Nov
Advanced Health Services Research (elective)
This course will develop student analytic competencies to the level necessary to conceptualize, plan, carry out, and effectively communicate small research projects in patient care, epidemiology, or health services. Lectures, demonstrations, and labs will be used to integrate and extend methods introduced in other TDI courses. The course will also cover new methods in epidemiology, health services and data science. The students will use national publicly available data and synthetic research datasets resembling Medicare claims and electronic health record data in classroom lab exercises and course assignments. Course topics focus on key aspects observational research including cohort derivation, multilevel analyses, small area analysis, and network analysis. Practical skill areas will include programming in STATA and/or R, developing an analytic workflow, data visualization (designing tables and figures), and data structure and management. Emphasis is on becoming independent in research processes. The instructors will mentor students as they develop their own analytic projects.
The Integrative Learning Experience (ILE) component can be met in this course.