JSM 2015 Preliminary Program

Online Program Home
My Program

Abstract Details

Activity Number: 631
Type: Topic Contributed
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #316528
Title: Machine Learning Techniques for Plan Payment Risk Adjustment
Author(s): Sherri Rose*
Companies: Harvard Medical School
Keywords: machine learning ; risk adjustment ; electronic health data ; ensembling ; prediction ; health care policy

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 in general, as well as in upcoding settings. This framework accommodates variable screening as well as effect measures when causal inference is of primary research interest instead of prediction.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2015 program

For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

2015 JSM Online Program Home