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Decision support techniques and online algorithms aim to help individuals predict costs and facilitate their choice of health insurance coverage. Self-reported health status (SHS), whereby patients rate their own health, could improve cost-prediction estimates without requiring individuals to share personal health information or know about undiagnosed conditions. We compared the predictive accuracy of several models: (1) SHS only, (2) a “basic” model adding health-related variables, and (3) a “full” model adding measures of healthcare access. The Medical Expenditure Panel Survey was used to predict 2015 health expenditures from 2014 data. Relative performance was assessed by comparing adjusted-R 2 values and by reporting the predictive accuracy of the models for a new cohort (2015–2016 data). In the SHS-only model, those with better SHS were less likely to incur expenditures. However, after accounting for health variables, those with better SHS were more likely to incur expenses. In the full model, SHS was no longer predictive of incurring expenses. Variables indicating better access to care were associated with higher likelihood of spending and higher spending. The full model ( R 2 = 0.290) performed slightly better than the basic model ( R 2 = 0.240), but neither performed well at the upper tail of the cost distribution. While our SHS-based models perform well in the aggregate, predicting population-level risk well, they are not sufficiently accurate to guide individuals’ insurance shopping decisions in all cases. Policies that rely heavily on health insurance consumers making individually optimal choices cannot assume that decision tools can accurately anticipate high costs.

Background: Despite recently expanded access to health insurance, consumers still face barriers to using their coverage to obtain needed health care. Objective: To examine the characteristics of those who delay or avoid health care due to costs. Methods: Participants were recruited via Amazon MTurk and completed a survey assessing demographic characteristics, financial toxicity, health care minimizer-maximizer tendencies, health insurance knowledge, numeracy, delaying/avoiding any care, and delaying/avoiding six common health care services (three preventive and three nonpreventive services). Validated measures were used when available. Delay/avoidance behaviors were categorized into delaying/avoiding any care, preventive care, and nonpreventive care. Logistic regression models examined 1) financial toxicity, 2) minimizer-maximizer tendencies, 3) numeracy, 4) health insurance knowledge, and 5) knowledge of preventive care coverage separately on three forms of delay/avoidance behaviors, controlling for chronic conditions, insurance status, and/or income where appropriate. Results: Of 518 respondents, 470 did not fail attention-check questions and were used in analyses. Forty-five percent of respondents reported delaying/avoiding care due to cost. Multivariable analyses found that financial toxicity was related to delaying/avoiding any care (odds ratio [OR] = 0.884, P < 0.001), preventive care (OR = 0.906, P < 0.001), and nonpreventive care (OR = 0.901, P < 0.001). A tendency to minimize seeking health care (OR = 0.734, P < 0.001) and lower subjective numeracy (OR = 0.794, P = 0.023) were related to delaying/avoiding any care. General health insurance knowledge (OR = 0.989, P = 0.023) and knowledge of preventive care coverage (OR = 0.422, P < 0.001) were related to delaying/avoiding preventive care. Conclusions: Many people delay or avoid health care due to costs, even when insured. Results suggest that there may be different reasons individuals delay or avoid preventive and nonpreventive care. Findings may inform interventions to educate consumers and support discussions about health care costs to facilitate appropriate health care utilization.

Introduction: Since the Affordable Care Act was passed, more than 12 million individuals have enrolled in the health insurance marketplace. Without support, many struggle to make an informed plan choice that meets their health and financial needs. Methods: We designed and evaluated a decision aid, Show Me My Health Plans (SMHP), that provides education, preference assessment, and an annual out-of-pocket cost calculator with plan recommendations produced by a tailored, risk-adjusted algorithm incorporating age, gender, and health status. We evaluated whether SMHP compared to HealthCare.gov improved health insurance decision quality and the match between plan choice, needs, and preferences among 328 Missourians enrolling in the marketplace. Results: Participants who used SMHP had higher health insurance knowledge (LS-Mean = 78 vs. 62; P < 0.001), decision self-efficacy (LS-Mean = 83 vs. 75; P < 0.002), confidence in their choice (LS-Mean = 3.5 vs. 2.9; P < 0.001), and improved health insurance literacy (odds ratio = 2.52, P < 0.001) compared to participants using HealthCare.gov . Those using SMHP were 10.3 times more likely to select a silver- or gold-tier plan (P < 0.0001). Discussion: SMHP can improve health insurance decision quality and the odds that consumers select an insurance plan with coverage likely needed to meet their health needs. This study represents a unique context through which to apply principles of decision support to improve health insurance choices.