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.
Onega T, Alford-Teaster J, Leggett C, Loehrer A, Weiss JE, Moen EL, Pollack CC, Wang F
J Rural Health|2022 Jul 12
Zipkin RJ, Schaefer A, Wang C, Loehrer AP, Kapadia NS, Brooks GA, Onega T, Wang F, O'Malley AJ, Moen EL
Ann Surg Oncol|2022 Sep
Leyenaar JK, Schaefer AP, Freyleue SD, Austin AM, Simon TD, Van Cleave J, Moen EL, O'Malley AJ, Goodman DC
JAMA Pediatr|2022 Jun 1
Nemesure MD, Schwedhelm TM, Sacerdote S, O'Malley AJ, Rozema LR, Moen EL
Appl Netw Sci|2021
Levy JJ, Chen Y, Azizgolshani N, Petersen CL, Titus AJ, Moen EL, Vaickus LJ, Salas LA, Christensen BC
NPJ Syst Biol Appl|2021 Aug 20
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 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.