Predicting Outpatient Process Flows to Minimise the Cost of Handling Returning Patients: A Case Study

Abstract

We describe an application of process predictive monitoring at an outpatient clinic in a large hospital. A model is created to predict which patients will wrongly refer to the outpatient clinic, instead of directly to other departments, when returning to get treatment after an initial visit. Four variables are identified to minimise the cost of handling these patients: the cost of giving appropriate guidance to them, the cost of handling patients taking a non-compliant flow by wrongly referring to the outpatient clinic, and the false positive/negative rates of the predictive model adopted. The latter determine the situations in which patients have not received guidance when they should have had or have been guided even though not necessary, respectively. Using these variables, a cost model is built to identify which combinations of process intervention/redesign options and predictive models are likely to minimise the cost overhead of handling the returning patients.