The utility of probabilistic models to identify individuals with future high medical expenditures

Abstract

In this paper, an evaluation model is presented to assess the utility of probabilistic models in terms of their capacity to successfully oversample policy relevant population subgroups that are subject to transitions. Examples of these applications are drawn from the Medical Expenditure Panel Survey(MEPS). Given the high concentration of health care expenditures in a given year among a relatively small percentage of the population, a prediction model that can accurately identify the persistence of high levels of expenditures is an important analytical tool. This type of modeling effort also enhances the ability to discern the causes of high health care expenses and the characteristics of the individuals who incur them. This feature also applies to prediction models that can accurately identify those individuals with persistently low or average levels of expenditures. The models that are presented have particular relevance as statistical tools to facilitate efficient sampling strategies that permit the selection of an over-sample of individuals likely to incur high levels of medical expenditures in the future.