Bio
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.
Published Research
Ran X, Meara E, Morden NE, Moen EL, Rockmore DN, O'Malley AJ
Res Sq|2024 Mar 26
Scodari BT, Schaefer AP, Kapadia NS, Brooks GA, O'Malley AJ, Moen EL
Ann Surg Oncol|2024 Mar 27
Liu YC, Schmidt RO, Kapadia NS, Phillips JD, Moen EL
Int J Radiat Oncol Biol Phys|2024 Mar 13
Scodari BT, Schaefer AP, Kapadia NS, O'Malley AJ, Brooks GA, Tosteson ANA, Onega T, Wang C, Wang F, Moen EL
JCO Oncol Pract|2024 Feb 22
Arakelyan M, Freyleue SD, Schaefer AP, Austin AM, Moen EL, O'Malley AJ, Goodman DC, Leyenaar JK
J Rural Health|2024 Mar
Courses Taught
PH 147:
Advanced Health Services Research (elective)
(1.5 units)
This course will develop student analytic competencies to the level necessary to conceptualize, plan, carry out, and effectively communicate small research projects in health services or epidemiology. Lectures, demonstrations, and labs will be used to integrate and extend methods introduced in other QBS and TDI courses. The students will leverage synthetic electronic health record data provided by the Analytics Institute at Dartmouth-Hitchcock and publicly available data in classroom lab exercises and course assignments. Many of the labs build on one another, and the aim is that the skills developed in the labs will assist the students with their own student-led projects. The instructors will mentor students as they develop their own analytic projects. Practical skill areas include programming in R, developing an analytic workflow, data visualization, and data structure and management. The main goal of the course is to firmly ground students in the scientific process of observational research.
The Integrative Learning Experience (ILE) component can be met in this course.