Health Policy PhD Candidate, Harvard University
Savannah Bergquist is a doctoral student in health policy at Harvard University concentrating in evaluative science and statistics, and is currently supported by a Harvard Data Science Initiative research grant (PI: Sherri Rose). She was awarded a 2017 Harvard Graduate Society Research Fellowship for her dissertation work, as well as a student travel award from the 2018 International Conference on Health Policy Statistics.
Savannah is working on machine learning projects related to assessing risk adjustment methods applied in the ACA Marketplaces and Medicare Advantage with Sherri Rose. She is interested in how sample selection affects prediction, particularly for subgroups such as users of mental health services. Savannah is also interested in reproducible research.
Savannah graduated magna cum laude from Georgetown University with an AB in Political Economy. Following graduation, Savannah pursued an MSc in Health, Population, and Society from the London School of Economics. Her graduate thesis evaluated the Partnership for Long-Term Care Insurance Program. She received the Brian Abel Smith Prize for best dissertation in her degree program. After her MSc, Savannah joined The Dartmouth Institute for Health Policy and Clinical Practice as a Health Policy Fellow, where her work focused on accountable care organizations.
Bergquist S, Layton T, McGuire T, Rose S. Data Transformations to Improve the Performance of Health Plan Payment Methods. Journal of Health Economics, in press. [NBER Working Paper #24491]
Brooks G, Bergquist S, Landrum MB, Rose S, Keating N. Classifying stage IV lung cancer from health care claims: A comparison of multiple analytic approaches. JCO Clinical Cancer Informatics, 2019. [Link]
Bergquist S, McGuire T, Layton T, Rose S. Sample selection for Medicare risk adjustment due to systematically missing data. Health Services Research, 2018. [PDF]
Bergquist S, Costa-Font J, and Swartz K. Long-term care partnerships: are they fit for purpose? The Journal of the Economics of Ageing, 2018. [Link]
Shrestha A, Bergquist S, Montz E, Rose S. Mental health risk adjustment with clinical categories and machine learning. Health Services Research, 2018. [PDF]
Bergquist S, Brooks G, Keating N, Landrum MB, Rose S. Classifying lung cancer severity with ensemble machine learning in health care claims data. Proceedings of Machine Learning Research, 2017. [PDF] [CancerCLAS.org]
Colla C, Lewis V, Bergquist S, and Shortell S. Accountability across the continuum: The participation of postacute care providers in accountable care organizations. Health Services Research, 2016. [Link]
Bergquist S, Costa-Font J, and Swartz K. Partnership program for long-term care insurance: the right model for addressing uncertainties with the future? Ageing & Society, 2016. [Link]