Predicting total health care costs of medicaid recipients: An artificial neural systems approach

Although medical treatment costs have escalated beyond the reach of many Americans, a thorough total cost model is essential before implementing cost containment strategies. This study offers a prediction model of the total treatment cost for a Mississippi Medicaid patient. Artificial neural systems (ANS) are proposed as a methodology for the prediction of health care costs of postmenopausal women who are Medicaid recipients. The results of the neural networks along with traditional regression analysis are presented. Artificial neural systems overcome many of the problems associated with the estimation of this model, such as the identification of the appropriate functional form and dealing with both qualitative and quantitative aspects of these large claims databases. Neural networks are shown to provide superior forecasts. In addition preliminary results for the presentation of significance tests of individual causal variables using neural networks is presented.