A postdoctoral fellow at The Dartmouth Institute, Erika Moen’s research aims to use biostatistics, bioinformatics, and data science methods to identify novel factors associated with adoption of new therapies and technologies and patient outcomes. Physician network analysis provides new methodological tools to evaluate how physician relationships impact important clinical measures, such as adherence to new clinical guidelines and adoption of biomedical innovations. Her current work focuses on using social network analysis to uncover interpersonal and organizational factors among physicians and hospitals that influence decisions regarding patient outcomes, with the ultimate goal of improving patient outcomes by optimizing diffusion of high-quality care.
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, Kapadia NS, O'Malley AJ, Onega T
Cancer Epidemiol Biomarkers Prev|2018 Oct 30
Poghosyan H, Moen EL, Kim D, Manjourides J, Cooley ME
Am J Health Promot|2018 Aug 2
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
Lee JW, Punshon T, Moen EL, Karagas MR, Gui J
Comput Biol Chem|2017 Dec
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.