Turnaround Time Prediction for a Medical Laboratory Using Artificial Neural Networks

Abstract

Turnaround time (TAT) or duration between different stages in medical and healthcare services is accepted to be one of the most significant performance measures that can have a great impact on service quality, change management, costs, and strategic decisions. Accurate and reliable prediction or estimation of the turnaround times or elicitation of the underlying causes that affect TAT is known to be a difficult problem. In this study, a heuristic prediction approach is used by designing and implementing a special artificial neural network (ANN) model in order to predict TAT of a specific process in a private hospital. The prediction performance of our ANN model is comparatively analyzed with some alternative linear and nonlinear numerical prediction algorithms. The results show that ANN surpasses all of the other numerical prediction algorithms and ANN might be used by the decision makers as a reliable model to estimate TAT within acceptable error rates.