Seniors may benefit from decision support tools when choosing Part D plans—but they may need a nudge to jump into the tech, according to a new study.
Researchers led by a team at Stanford University conducted a random trial and invited nearly 30,000 people to participate in testing the tools. However, just over 1,180 agreed to do so, which signals hesitancy among Medicare beneficiaries to use emerging tech tools.
M. Kate Bundorf, the study’s lead author and associate professor of health policy research at Stanford, told FierceHealthcare that the people who agreed to join the pilot differed notably from those who chose not to. Those who enrolled in the study were about two years younger, on average, than those that didn’t, and they were generally healthier.
Men were also more likely to participate, according to the study.
“We think there is potential for a broader set of people to use these tools,” Bundorf said. “That means we may have to think of what is the best way to reach these folks and the best way to make these tools more attractive.”
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About a third of the study participants used a tool with a built-in mechanism to recommend plans, while one-third used a tool without a recommendation algorithm. The remaining enrollees were given spending and quality data but were left to analyze that information themselves.
The study also identified other trends in how Medicare beneficiaries were using these tools. The researchers were surprised to find that using the support tools increased the length of time needed to choose a plan, Bundorf said.
Three-quarters of participants spent more than an hour selecting a plan, according to the study. About 40% eventually choose the plan that the tool’s algorithms suggested.
That points to a need to refine the tools to better meet the expectations of beneficiaries and provide more tailored results, Bundorf said. Recommendations also need to be clear and concise, the researchers found.
“That kind of points to the potential role for more sophisticated expert recommendations and a more comprehensive algorithm,” she said. “At the same time, I think we have to think about transparency, and make sure folks know what type of information algorithms are using.”
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In addition, enrollees who were recommended plans by the tool were more likely to be satisfied with the plan they ultimately chose. Just less than 10% of these participants said they were “very satisfied” with their plans, compared to the other two study groups.
The algorithm's recommendations were also more likely to push the participants to switch plans. Eight percent more of these enrollees switched compared to the other groups. However, the study didn’t report significant findings to indicate the tools led to lower spending or improved outcomes.
Even without the built-in recommendations, enrollees testing decision tools were more likely to switch to a new plan than they would if they had jumped into open enrollment otherwise.
“Even in our control arm, rates of switching are higher than they are in the population in general,” Bundorf said.