COMPUTATIONAL HEALTH ECONOMICS

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

ONGOING PROJECTS

  • Variable importance of medical conditions in health spending
  • Computational health economics with normative data 
  • Risk adjustment with systematically missing data 
  • Risk adjustment for mental health spending 

RECENT RELATED NEWS

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

Harvard Medical School Health Care Policy Advisory Board: Reflecting on Research and Planning Ahead

RECENT PUBLICATIONS

S. Rose, S. Bergquist, T. Layton (2017). Computational health economics for identification of unprofitable health care enrollees. Biostatistics, in press. 

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]