Neural network cost estimates for heart bypass surgery

This paper reports on the results of using artificial neural network (ANN) technology to estimate treatment costs of heart bypass patients based on their diagnostic condition and clinical criteria. Our applications include: (1) predicting total episode cost using clinical data; (2) a method for providing rapid feedback to assess change in total costs within a turbulent environment; and (3) a procedure for identifying activity-based cost driver candidates that would normally not surface from an analysis of accounting data. Clinical data were collected on 250 heart bypass patients at the University of Ottawa Heart Institute. The data analysed support the following conclusions: (1) clinical and diagnostic indicators obtained before surgery for individual patients can be used to estimate the total cost of their heart bypass surgery; (2) the average cost estimate error decreases as we add clinical information available during and after the surgical event; (3) the procedure used to estimate individual patient cost does not require access to accounting records; (4) the forecasting system we describe may improve exception reporting for individual patients by tracking costs and clinical criteria on a real-time basis throughout the treatment episode.