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Preferences and Trade-offs for Health Insurance Plan Decisions: You Can’t Always Get What You Want

  • 1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
  • 2Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts
  • 3Division of General Pediatrics, Boston Children’s Hospital, Boston, Massachusetts

Approximately 1 in 3 of commercially insured Americans change health plans annually.1 While those with employer coverage may have limited choices, a large proportion of Americans, including those in the nongroup market, Medicare, and in private health insurance exchanges have dozens of health plan options from which to choose.2 Choosing a health insurance plan can be challenging, especially for those with limited health insurance literacy and numeracy.3,4 Choosing the wrong plan can lead to a significant financial burden and unmet health care needs.3 Therefore, we need to develop and implement strategies for improving health insurance plan choices.

While neoclassical economic theory predicts that individuals faced with many plan options would consider a health plan’s attributes along with their own characteristics (eg, health status, income) to make a choice that maximizes expected utility (ie, “happiness”), evidence reveals this is often not the case. Health insurance choices are complex, involving trade-offs between now and the future and requiring consumers to assess health risks. Choices are thus susceptible to consumer biases and the use of heuristics, resulting in suboptimal plan selection.3 Policy efforts to help consumers optimize health plan choices include standardizing plan information, reframing how information is presented, and offering personalized help through navigators.4,5 Decision support tools, such as out-of-pocket cost estimators and tools that compare drug formularies, have been developed. Quality “star” ratings provide a broad range of information on plan attributes. Although these strategies can be helpful, they can also lead consumers to focus on 1 plan feature, such as premium, network breadth, or drug coverage.3,5 This can result in poor outcomes; for example, making a decision primarily based on premiums can lead to plan choices with higher total costs.2

In order to improve the quality of health insurance decisions, we need to design decision support tools to help consumers understand their preferences for plan features, weigh trade-offs among multiple plan features, and clarify outcomes resulting from each. Decision aids are often used in clinical settings to help patients make informed choices.6 There is an opportunity to use decision aids to improve health plan choice.

How do we do that? Decision aid development is complex, but ideally, aids will help consumers to prioritize what matters most to them.6 There are many ways to clarify values,6 but decision aid developers should use stated preference methods. Specifically, discrete choice experiments and multicriteria decision analysis approaches should be used to provide insight into how consumers value trade-offs among competing priorities for decisions.7 In contrast to approaches that try to optimize a single plan feature, these methods can help optimize a consumer’s health insurance choice over a range of features.

Stated preference methods break down complex decisions into pieces, or “attributes.” Relevant to a health insurance plan decision, attributes would include costs (including premiums and cost sharing), provider network, drug formulary, quality, and reputation. In the stated preference exercises within the decision aid, consumers would make judgements about their preferences for choices with varying attributes. These judgements confer information about how consumers weight each attribute of a decision and the trade-offs consumers are willing to make between attributes (eg, “I’m willing to pay more to get my desired provider,” or “I’m okay with an HMO’s restricted network because I am paying less”). Ultimately, researchers using these methods can produce a scoring system that can be integrated into a decision aid to recommend a plan, taking into account consumer preferences and constraints (eg, that a premium must be below a certain amount, or that the consumer’s preferred doctor is in the plan’s network).6 These methods could also be used by ACA marketplaces and health plans to prioritize attributes to be presented in a decision aid, highlighting the things patients value most.7 Brokers and navigators could be trained to use decision aids to facilitate their work with consumers. As with other decision aids and navigation tools, presenting clear explanations of complex insurance terms while avoiding information overload may be a challenge. Simulated and real-world research and usability testing are needed to implement these tools and achieve their potential.

What outcomes will use of these tools improve? Some health insurance decision aids that incorporate consumer preferences have been experimentally shown to improve decision self-efficacy and confidence in health plan choice.3 However, merely knowing that a choice aligns with an individual consumer’s values around health plan options at the time of the choice does not mean that the best plan has been identified. Further research is needed to determine the effect of decision support tools on access to and utilization of desired and high-quality health care services, health outcomes, and financial burden while covered under a plan. Researchers also need to examine whether outcomes vary across consumer groups, particularly low-income consumers and consumers with 1 or more serious chronic diseases.

Policy makers will need to know whether plan choices that align with patient values also improve population health, maximize high-value care, and contain costs. For example, a plan choice that maximizes a consumer’s preference for a particular high-priced but low-value provider might be at odds with society’s interest in reducing costly low-value care. When individual consumer wants and societal outcomes conflict, policy makers must come together to decide the optimal balance between consumer satisfaction and societal outcomes.

Decision support tools that utilize stated preference methods to clarify consumer values have great potential to help consumers choose better health insurance plans. Decision-making researchers should work with health plans and policy makers to develop and test tools that enable consumers to make trade-offs in a way that enables them to get what they value most in a health plan. This choice should in turn improve health, health care utilization, and health care expenditures on a population level.

Article Information

Corresponding author: Davene R. Wright, PhD, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215 (davene_wright@harvardpilgrim.org).

Conflicts of Interest: None reported.

References
1.
Graves  JA, Nikpay  SS.  The changing dynamics Of US health insurance and implications for the future of the Affordable Care Act.   Health Aff (Millwood). 2017;36(2):297-305. doi:10.1377/hlthaff.2016.1165PubMedGoogle ScholarCrossref
2.
Taylor  EA, Saltzman  E, Bauhoff  S, Pacula  RL, Eibner  C.  More choice in health insurance marketplaces may reduce the value of the subsidies available to low-income enrollees.   Health Aff (Millwood). 2015;34(1):104-110. doi:10.1377/hlthaff.2014.0763PubMedGoogle ScholarCrossref
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Politi  MC, Kuzemchak  MD, Liu  J,  et al.  Show me my health plans: using a decision aid to improve decisions in the federal health insurance marketplace.   MDM Policy Pract. 2016;1(1). doi:10.1177/2381468316679998PubMedGoogle Scholar
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Wong  CA, Ellsworth  E, Madanay  F,  et al.  The roles of assisters and automated decision support tools in consumers’ marketplace choices: room for improvement.   Health Aff (Millwood). 2019;38(3):473-481. doi:10.1377/hlthaff.2018.05021PubMedGoogle ScholarCrossref
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Ericson  KMM, Starc  A.  How product standardization affects choice: Evidence from the Massachusetts Health Insurance Exchange.   J Health Econ. 2016;50(C):71-85. doi:10.1016/j.jhealeco.2016.09.005PubMedGoogle ScholarCrossref
6.
Fagerlin  A, Pignone  M, Abhyankar  P,  et al.  Clarifying values: an updated review.   BMC Med Inform Decis Mak. 2013;13(suppl 2):S8. doi:10.1186/1472-6947-13-S2-S8PubMedGoogle ScholarCrossref
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Marsh  K, IJzerman  M, Thokala  P,  et al; ISPOR Task Force.  Multiple criteria decision analysis for health care decision making—emerging good practices: report 2 of the ISPOR MCDA Emerging Good Practices Task Force.   Value Health. 2016;19(2):125-137. doi:10.1016/j.jval.2015.12.016PubMedGoogle ScholarCrossref
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