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A service analytic approach to studying patient no-shows
Service Business  (IF2.791),  Pub Date : 2020-05-12, DOI: 10.1007/s11628-020-00415-8
Murtaza Nasir, Nichalin Summerfield, Ali Dag, Asil Oztekin

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.