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Plan Selection in Medicare Part D: Evidence from Administrative Data
Author(s) -
Florian Heiß,
Adam Leive,
Daniel McFadden,
Joachim Winter
Publication year - 2012
Language(s) - English
Resource type - Reports
DOI - 10.3386/w18166
Subject(s) - selection (genetic algorithm) , plan (archaeology) , medicare part d , computer science , operations research , data mining , business , data science , geography , artificial intelligence , engineering , medicine , prescription drug , nursing , archaeology , medical prescription
We study the Medicare Part D prescription drug insurance program as a bellwether for designs of private, non-mandatory health insurance markets, focusing on the ability of consumers to evaluate and optimize their choices of plans. Our analysis of administrative data on medical claims in Medicare Part D suggests that less than 10 percent of individuals enroll in plans that are ex post optimal with respect to total cost (premiums and co-payments). Relative to the benchmark of a static decision rule, similar to the Plan Finder provided by the Medicare administration, that conditions next year’s plan choice only on the drugs consumed in the current year, enrollees lost on average about $300 per year. These numbers are hard to reconcile with decision costs alone; it appears that unless a sizeable fraction of consumers value plan features other than cost, they are not optimizing effectively.

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