[gov3009-l] Applied Stats - 4/8 - Sherri Rose
astrezhnev at fas.harvard.edu
Mon Apr 6 11:39:17 EDT 2015
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
As usual, we will meet in CGIS Knafel Room 354 and lunch will be provided.
See you all there!
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.
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