Health Plan Payment & Risk Adjustment
S. Bergquist, T. Layton, T. McGuire, and S. Rose. Data transformations to improve the performance of health plan payment methods. Journal of Health Economics, 2019, 66:195-207. [Link; NBER Working Paper]
S.L. Bergquist, T.G. McGuire, T.J. Layton, and S. Rose. Sample selection for Medicare risk adjustment due to systematically missing data. Health Services Research, 2018.
A. Shrestha, S.L. Bergquist, E.M. Montz, and S. Rose. Mental health risk adjustment with clinical categories and machine learning. Health Services Research, 2018.
S. Rose, S.L. Bergquist, and T.J. Layton. Computational health economics for identification of unprofitable health care enrollees. Biostatistics, 2017.
Cancer Staging in Claims Data
G. Brooks, S. Bergquist, M.B. Landrum, S. Rose, and N. Keating. Classifying stage 4 lung cancer from health care claims data: a comparison of multiple analytic approaches. JCO Clinical Cancer Informatics, 2019.
S.L. Bergquist, G.A. Brooks, N.K. Keating, M.B. Landrum, and S. Rose. Classifying lung cancer severity with ensemble machine learning in health care claims data. Proceedings of Machine Learning Research, 2017.
Long-Term and Post-Acute Care
S. Bergquist, J. Costa-Font, and K. Swartz. Long-term care partnerships: are they fit for purpose? The Journal of the Economics of Ageing, 2018.
C.H. Colla, V.A. Lewis, S.L. Bergquist, and S.M. Shortell. Accountability across the continuum: the participation of postacute care providers in accountable care organizations. Health Services Research, 2016.
S. Bergquist, J. Costa-Font, and K. Swartz. Partnership program for long-term care insurance: the right model for addressing uncertainties with the future? Ageing & Society, 2016.