Applied Statistics Workshop (Gov3009)

The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers. There is a free lunch provided. Sherri Rose (Harvard Medical School) presents Title: Rethinking Plan Payment Risk Adjustment with Machine Learning Abstract: Risk adjustment models for plan payment are typically estimated using classical linear regression models. These models are designed to predict plan spending, often as a function of age, gender, and diagnostic conditions. The trajectory of risk adjustment methodology in the federal government has been largely frozen since the 1970s, failing to incorporate methodological advances that could yield improved formulas. The use of novel machine learning techniques may improve estimators for risk adjustment, including reducing the ability of insurers to "game" the system with aggressive diagnostic upcoding. This upcoding has been recently estimated to cost over $11 billion in excess payments in Medicare Advantage, annually. We present a nonparametric machine learning framework for risk adjustment in the Truven MarketScan database, and assess whether use of these procedures improves risk adjustment.