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.
Moen EL, Bynum JP, Skinner JS, O'Malley AJ
Health Serv Res|2019 Apr 1
Moen EL, Kapadia NS, O'Malley AJ, Onega T
Cancer Epidemiol Biomarkers Prev|2019 Mar
Poghosyan H, Moen EL, Kim D, Manjourides J, Cooley ME
Am J Health Promot|2019 May
Moen EL, Bynum JP, Austin AM, Skinner JS, Chakraborti G, O'Malley AJ
Med Care|2018 Apr
Fricano-Kugler CJ, Getz SA, Williams MR, Zurawel AA, DeSpenza T Jr, Frazel PW, Li M, O'Malley AJ, Moen EL, Luikart BW
Biol Psychiatry|2018 Aug 15
Advanced Methods in Health Services Research
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.