A Comparison Between Subjective and Objective Methods of Predicting Health Care Expenses to Support Consumers’ Health Insurance Plan Choice

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.

Predicting Future Utilization Using Self-Reported Health and Health Conditions in a Longitudinal Cohort Study: Implications for Health Insurance Decision Support

“…Decision aids may be quite useful as health insurance literacy interventions, which should focus on communicating the concepts of lowprobability, high-cost risks, and how different types of insurance can mitigate them. 9,27,28 While we find that our SHS-based models perform well in the aggregate, predicting population-level risk well, we also caution that they are not sufficiently accurate to guide individuals' insurance shopping decisions in all cases.…”

“…To gauge the value of adding additional variables to the predictive model, we compared (a) a bare-bones model in which SHS alone predicted expenditures; (b) a “basic” model in which SHS and the health-related variables described above were used; and (c) a “full” model which used all variables that were statistically significant, which specifically included several measures of healthcare access as described above. Because prior work found an interactive effect between age and SHS, 9 we tested various specifications. We assessed the relative performance of the three models both by comparing adjusted-R 2 values and root mean squared errors (MSEs) and by reporting on the predictive accuracy of the model using a new cohort of people.…”

“…One potential way to do this is via the concept of self-reported health status (SHS). 9 SHS, which asks patients to rate their own health on a scale, often from poor to excellent, is a straightforward method for assessing individuals’ physical symptoms, emotional well-being, and functional status. 10 SHS plays a significant role in predicting total healthcare spending.…”

Determinants of rural-urban differences in health care provider visits among women of reproductive age in the United States

“…Inherent differences in the values and health care expectations between individuals from rural and urban areas may affect their responses to MEPS survey questions around care access, as has been described previously, and hence may influence whether rural women seek care regardless of provider availability [ 17 , 47 ]. Patient self-reported data is a theoretical limitation of all survey-based studies, although scholars generally consider such data valid for study and MEPS in particular is considered a methodologically rigorous and reliable source of health care utilization data [ 48 , 49 ]. It was not possible to restrict the data available for analysis to just pregnant women or to be specific for maternal care.…”

Discussing Cost and Value in Patient Decision Aids and Shared Decision Making: A Call to Action

“…We ended up having to pay quite a bit for that, almost $2,000 out-of-pocket, even though the insurance did cover quite a bit. It was still very expensive.” 25 There are some researchers and organizations that have incorporated direct costs into comparator tools and decision aids, 43 – 46 but generating personalized cost estimates and updating them over time is complex and requires substantial resources to maintain.…”