Medicare Part D could have saved more than $5 billion in 2009 if it used an intelligent algorithm to assign beneficiaries to plans instead of doing so randomly, according to a Health Affairs study.
The study — conducted by University of Pittsburgh researchers — focused on Part D beneficiaries with incomes below 150 percent of the poverty level. These enrollees qualify for subsidies from the federal government, which covers their premiums up to a benchmark amount. The benchmark is calculated individually for each of the 34 Part D regions based on the average premium for the basic Part D benefit in the region. The government randomly assigns new beneficiaries who qualify for the subsidy or who successfully apply for it without indicating a preferred plan to a stand-alone Part D plan with a premium equal to or less than the average premium for the basic Part D benefit in the region. Enrollees with low-income subsidies accounted for 75 percent of $60 billion in Part D spending in 2013, according to the study.
Using a 5 percent random sample of Medicare beneficiaries who qualified for the low-income subsidy from 2008 to 2009, the researchers simulated Part D plan assignments based on beneficiaries' drug consumption data and all available plans in 2009. They found that if beneficiaries were reassigned to the least expensive stand-alone Part D plan in their region based on their actual drug usage in 2008, there could have been a mean total savings to the government of $710 per enrollee.
Overall, the study states random plan assignment doesn't produce optimal results, and an intelligent reassignment algorithm based on beneficiaries' actual medication needs could lead to substantial savings.
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