Choosing where to set the threshold between low- and high-risk patients: Evaluating a classification tool within a simulation

Abstract

Health service providers must balance the needs of high-risk patients who require urgent medical attention against those of lower-risk patients who require reassurance or less urgent medical care. Based on their characteristics, we developed a tool to classify patients as low- or high-risk, with correspondingly different patient pathways through a service. Rather than choosing the threshold between low- and high-risk patients solely considering classification accuracy, we demonstrate the use of discrete-event simulation to find the best threshold from an operational perspective as well. Moreover, the predictors in classification tools are often categorical, and may be inter-dependent. Defining joint distributions of these variables from empirical data assumes that missing combinations are impossible. Our new approach involves using Poisson regression to estimate the joint distributions in the underlying population. We demonstrate our methods on a practical example: setting the threshold between low- and high-risk patients with proposed different pathways through a breast diagnostic clinic.