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
  • Ensembles for vulnerable unprofitable health care enrollees
  • Computational health economics with normative data 
  • Risk adjustment with systematically missing data 
  • Risk adjustment for mental health spending 

RECENT RELATED NEWS

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

RECENT PUBLICATIONS

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]