[gov3009-l] Applied Stats - 4/8 - Sherri Rose

Hi everyone! This week at the Applied Statistics Workshop we will be welcoming *Sherri Rose*, a Professor of Biostatistics at Harvard Medical School. She will be presenting work entitled *Rethinking Plan Payment Risk Adjustment with Machine Learning*. Please find the abstract below and on the website <http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/sherri-rose-havard-medical-school> . As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided. See you all there! -- Anton 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.