COMPUTATIONAL HEALTH ECONOMICS
Computational health economics brings statistical advances for big data and data science to answer critical questions in health economics.
- Intervening on the data to improve plan payment for disparities
- Medicare risk adjustment with systematically missing data
- Algorithmic fairness in the policy evaluation of health plan payment systems
RECENT RELATED NEWS
S. Rose (2018). Robust machine learning variable importance analyses of medical conditions for health care spending. Health Services Research, in press.
C. Carroll, M. Chernew, A.M. Fendrick, J. Thompson, S. Rose (2017). Effects of episode-based payment on health care spending and utilization: Evidence from perinatal care in Arkansas. [NBER Working Paper #23926]
A. Shrestha, S. Bergquist, E. Montz, S. Rose (2017). Mental health risk adjustment with clinical categories and machine learning. Health Services Research. Advance online publication. doi:10.1111/1475-6773.12818. [PDF]
Z. Song, S. Rose, M. Chernew, D. Gelb Safran (2017). Lower versus higher income populations in the Alternative Quality Contract: Improved quality and similar spending. Health Affairs, 36(1):74-82. [Link]
S. Rose (2016). A machine learning framework for plan payment risk adjustment. Health Services Research, 51(6):2358-74. [Link]
A. Mirelman, S. Rose, J. Khan, S. Ahmed, D. Peters, L. Niessen, A. Trujillo (2016). The relationship between noncommunicable disease occurrence and poverty: Evidence from demographic surveillance in Matlab, Bangladesh. Health Policy and Planning, 31(6):785-92. [PDF]