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Using Information on Clinical Conditions to Predict High-Cost Patients

Objective. To compare the ability of different models to predict prospectively whether someone will incur high medical expenditures.

Data Source. Using nationally representative data from the Medical Expenditure Panel Survey (MEPS), prediction models were developed using cohorts initiated in 1996–1999 (N=52,918), and validated using cohorts initiated in 2000–2003 (N=61,155).

Study Design. We estimated logistic regression models to predict being in the upper expenditure decile in Year 2 of a cohort, based on data from Year 1. We compared a summary risk score based on diagnostic cost group (DCG) prospective risk scores to a count of chronic conditions and indicators for 10 specific high-prevalence chronic conditions. We examined whether self-rated health and functional limitations enhanced prediction, controlling for clinical conditions. Models were evaluated using the Bayesian information criterion and the c-statistic.

Principal Findings. Medical condition information substantially improved prediction of high expenditures beyond gender and age, with the DCG risk score providing the greatest improvement in prediction. The count of chronic conditions, self-reported health status, and functional limitations were significantly associated with future high expenditures, controlling for DCG score. A model including these variables had good discrimination (c=0.836).

Conclusions. The number of chronic conditions merits consideration in future efforts to develop expenditure prediction models. While significant, self-rated health and indicators of functioning improved prediction only slightly.

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