COMPUTATIONAL HEALTH ECONOMICS

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

ONGOING PROJECTS

  • Medicare risk adjustment with systematically missing data 
  • Algorithmic fairness in the policy evaluation of health plan payment systems 
  • Machine learning for difference-in-differences designs

RECENT RELATED NEWS

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

SELECTED RECENT PUBLICATIONS

S. Bergquist, T. Layton, T. McGuire, S. Rose (2018). Intervening on the data to improve the performance of health plan payment methods. [NBER Working Paper]

S. Rose (2018). Stacked propensity score functions for observational cohorts with oversampled exposed subjects. [arXiv]

S. Rose, T. McGuire (2018). Limitations of p-values and R-squared for stepwise regression building: A fairness demonstration in health policy risk adjustment, The American Statistician. [arXiv]

S. Rose (2018). Robust machine learning variable importance analyses of medical conditions for health care spending, Health Services Research. [PDF]

A. Shrestha, S. Bergquist, E. Montz, S. Rose (2018). Mental health risk adjustment with clinical categories and machine learning, Health Services Research. [PDF]

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

S. Rose (2016). A machine learning framework for plan payment risk adjustment, Health Services Research. [Link]