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

Abstract

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-R2 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 ( R2 = 0.290) performed slightly better than the basic model ( R2 = 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.

Association between Johns Hopkins Adjusted Clinical Groups risk scores and self-reported outcome measures: an observational study among individuals with complex or long-term conditions in Norway

“…It has been found that self-rated health predicts high healthcare costs in the following year better than administrative data alone, 34 and that it predicts population level risk of high expenditure. 35 This is a likely explanation for the association with the ACG variable concurrent risk. This explanation is also likely to hold for the association with unscaled total cost predicted risk, as SRH has been reported to remain fairly stable over time.…”

“…Our findings showed that high scores in the ACG variables unscaled concurrent risk and unscaled total cost predicted risk and receiving a positive frailty flag were associated with reporting poor SRH. It has been found that self-rated health predicts high healthcare costs in the following year better than administrative data alone,34 and that it predicts population level risk of high expenditure 35. This is a likely explanation for the association with the ACG variable concurrent risk.…”