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Activity Number: 421
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: Health Policy Statistics Section
Abstract #322441
Title: Medicare Risk Adjustment with Systematically Missing Data
Author(s): Savannah Bergquist* and Tim Layton and Thomas G. McGuire and Sherri Rose
Companies: Harvard University and Harvard Medical School and Harvard Medical School and Harvard Medical School
Keywords: Ensemble learning ; Propensity score ; Prediction ; Risk adjustment ; Medicare
Abstract:

Currently, CMS pays Medicare Advantage (MA) plans, the private option in Medicare, based on cost patterns generated by Traditional Medicare beneficiaries. Individuals who choose to enroll in MA differ from Traditional Medicare enrollees in terms of health status, race/ethnicity, age, and income. MA comprises 31% of total Medicare enrollment. This paper proposes a new empirical strategy to address the problem of systematically missing MA enrollee diagnoses data by drawing a sample of Traditional Medicare beneficiaries who resemble MA enrollees. We employ a two stage machine-learning-based propensity score matching approach that relies on a common source of survey data across MA and Traditional Medicare enrollees for the first stage and Medicare claims data for the second stage. In both stages we use an ensemble machine learner to estimate the propensity score. We then estimate the MA risk adjustment formula in the matched Traditional Medicare sample and full Traditional Medicare sample to predict costs and assess the impact of sample selection on risk adjustment. To date, propensity scores have almost exclusively been used for causal inference and not prediction.


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

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