Using data-driven techniques to predict outpatient ultrasound examination time for the multi-clinic outpatient appointment scheduling problem

Abstract

Appointment scheduling for hospital patients is becoming increasingly important because it can reduce patient’s waiting time and enhance hospitals’ medical quality. In the case of ultrasound examinations, multiple radiological technologists use multiple rooms to conduct examinations, which involves the sequencing of examination work, time uncertainty, and operational characteristics. To enable hospital managers to effectively evaluate radiological technologists’ effort when carrying out ultrasound examinations, this study uses classification methods in data exploration, including decision tree (DT), logistic regression (LR), and artificial neural network (ANN), to establish a time prediction model for outpatient ultrasound examination. This study found that the DT model (i.e. C4.5) had the highest classification accuracy. After reviewing and removing rules generated from the model with an accuracy below 70%, the overall accuracy of the model was 76.31%. The classification results of the model are a reference for outpatient ultrasound appointment scheduling. The results of the study can effectively predict the examination time needed for ultrasound outpatients requiring different examination positions, thereby fairly allocating the patients to each room for the examination and reducing patient waiting time and radiological technologists’ idle time. Therefore, hospital managers can improve the hospital’s overall medical quality.