ABSTRACT
Objective. Numerous electronic tools help consumers select health insurance plans based on their estimated health care utilization. However, the best way to personalize these tools is unknown. The purpose of this study was to compare two common methods of personalizing health insurance plan displays: 1) quantitative healthcare utilization predictions using nationally representative Medical Expenditure Panel Survey (MEPS) data and 2) subjective-health status predictions. We also explored their relations to self-reported health care utilization. Methods. Secondary data analysis was conducted with responses from 327 adults under age 65 considering health insurance enrollment in the Affordable Care Act (ACA) marketplace. Participants were asked to report their subjective health, health conditions, and demographic information. MEPS data were used to estimate predicted annual expenditures based on age, gender, and reported health conditions. Self-reported health care utilization was obtained for 120 participants at a 1-year follow-up. Results. MEPS-based predictions and subjective-health status were related (P < 0.0001). However, MEPS-predicted ranges within subjective-health categories were large. Subjective health was a less reliable predictor of expenses among older adults (age × subjective health, P = 0.04). Neither significantly related to subsequent self-reported health care utilization (P = 0.18, P = 0.92, respectively). Conclusions. Because MEPS data are nationally representative, they may approximate utilization better than subjective health, particularly among older adults. However, approximating health care utilization is difficult, especially among newly insured. Findings have implications for health insurance decision support tools that personalize plan displays based on cost estimates.
ABSTRACT
BACKGROUND: The Affordable Care Act allows uninsured individuals to select health insurance from numerous private plans, a challenging decision-making process. This study examined the effectiveness of strategies to support health insurance decisions among the uninsured. METHODS: Participants (N = 343) from urban, suburban, and rural areas were randomized to 1 of 3 conditions: 1) a plain language table; 2) a visual condition where participants chose what information to view and in what order; and 3) a narrative condition. We administered measures assessing knowledge (true/false responses about key features of health insurance), confidence in choices (uncertainty subscale of the Decisional Conflict Scale), satisfaction (items from the Health Information National Trends Survey), preferences for insurance features (measured on a Likert scale from not at all important to very important), and plan choice. RESULTS: Although we did not find significant differences in knowledge, confidence in choice, or satisfaction across condition, participants across conditions made value-consistent choices, selecting plans that aligned with their preferences for key insurance features. In addition, those with adequate health literacy skills as measured by the Rapid Estimate of Adult Literacy in Medicine-Short Form (REALM-SF) had higher knowledge overall ([Formula: see text] = 6.1 v. 4.8, P < 0.001) and preferred the plain language table to the visual (P = 0.04) and visual to narrative (P = 0.0002) conditions, while those with inadequate health literacy skills showed no preference for study condition. A similar pattern was seen for those with higher subjective numeracy skills and higher versus lower education with regard to health insurance knowledge. Individuals with higher income felt less confident in their choices ([Formula: see text] = 28.7 v. 10.0, where higher numbers indicate less confidence/more uncertainty; P = 0.004). CONCLUSIONS: Those developing materials about the health insurance marketplace to support health insurance decisions might consider starting with plain language tables, presenting health insurance terminology in context, and organizing information according to ways the uninsured might use and value insurance features. Individuals with limited health literacy and numeracy skills and those with lower education face unique challenges selecting health insurance and weighing tradeoffs between cost and coverage.
