Simulated neural networks to predict outcomes, costs, and length of stay among orthopedic rehabilitation patients.

Abstract

Our purpose was to develop a set of simulated neural networks that would predict functional outcomes, length of stay, and costs among orthopedic patients admitted to an inpatient rehabilitation hospital. We used retrospective data for a sample of 387 patients between the ages of 60 and 89 who had been admitted to a single rehabilitation facility over a period of 12 months. Using age and data on functional capacity at admission from the Functional Independence Measure, we were successful in constructing networks that were 86%, 87%, and 91% accurate in predicting functional outcome, length of stay, and costs to within +/- 15% of the actual value. In each case the accuracy of the network exceeded that of a multiple regression equation using the same variables. Our results show the feasibility of using simulated neural networks to predict rehabilitation outcomes, and the advantages of neural networks over conventional linear models. Networks of this kind may be of significant value to administrators and clinicians in predicting outcomes and resource usage as rehabilitation hospitals are faced with capitation and prospective payment schemes.