Using information on clinical conditions to predict high-cost patients

Abstract

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.