Computational health economics brings statistical advances for big data and data science to answer critical questions in health economics.


  • Variable importance of medical conditions in health spending
  • 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 


NIH Makes $8.5M Investment in Promising Projects

Deep Dive: Machine Learning Tools Search Vast Oceans of Data for Insights on Health Economics

Sherri Rose Awarded Harvard Data Science Initiative Grant

Drug Classes Remain Predictive of Insurer Losses Even After Risk Adjustment

Sherri Rose Uses Computational Health Economics to Bring Insight to Risk Adjustment


A. Shrestha, S. Bergquist, E. Montz, S. Rose (2018). Mental health risk adjustment with clinical categories and machine learning. Health Services Research. Advance online publication. doi:10.1111/1475-6773.12818. [PDF]

S. Rose, S. Bergquist, T. Layton (2017). Computational health economics for identification of unprofitable health care enrollees. Biostatistics, 18(4):682-94 [Link][Code]

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]