Abstract
We examine the effectiveness of alternate choice architectures for health plan choice in US marketplaces under the Affordable Care Act (ACA) using three experiments based on the Health Reform Monitoring Survey: two experiments tested how choice architectures used in presenting information on health plans influenced plan choices and how existing designs could be improved; the third experiment checked the robustness of the choice architecture effects to more naturalistic choice scenarios in which consumers select plans when future medical spending is uncertain. More vulnerable consumers (e.g., worse health, lower literacy) experienced the largest relative improvements when ACA marketplace plans were displayed and sorted by total expected costs for the year rather than premiums (Experiment 1). The benefits of sorting plans by total expected costs was not improved further by making the importance of total expected costs more salient or by providing just-in-time education about such costs (Experiment 2). However, just-in-time education increased the likelihood consumers did not choose a plan, suggesting they may be in the process of updating their plan selection strategy given the new information. Broadly, these results were consistent across alternative scenarios where total expected costs were subject to uncertainty and consistent with expected patterns of consumer behavior under risk aversion (Experiment 3). Thus, a policy-feasible mechanism—sorting health plan options by and highlighting total expected costs—may improve health plan choices, saving money for consumers and the government.
DOI
10.1016/j.obhdp.2019.02.002
Publication Date
2019-03-08
Publication Title
Organizational Behavior and Human Decision Processes
Publisher
Elsevier
ISSN
0749-5978
Embargo Period
2024-11-22
Recommended Citation
Barnes, A., Karpman, M., Long, S., Hanoch, Y., & Rice, T. (2019) 'More Intelligent Designs: Comparing the Effectiveness of Choice Architectures in US Health Insurance Marketplaces', Organizational Behavior and Human Decision Processes, . Elsevier: Available at: https://doi.org/10.1016/j.obhdp.2019.02.002
