A service analytic approach to studying patient no-shows

Abstract

Patients who fail to show up for an appointment are a major challenge to medical providers. Understanding no-shows and predicting them are keys to developing a proactive strategy in healthcare operations. In this study, we propose a data analytics framework to explore the underlying factors of no-shows via various machine learning models to predict whether a patient is a no-show. The analytics results reveal key patterns in no-show patients. We also propose a methodology to integrate the prediction model with a Bayesian inference system to create an overbooking decision support tool that allows variable overbooking rates in different time windows.