No-Show in Medical Appointments with Machine Learning Techniques: A Systematic Literature Review

Abstract

No-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients’ behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient’s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient’s scheduling.