A service analytic approach to studying patient no-shows

Murtaza Nasir, Nichalin Summerfield, Ali Dag and Asil Oztekin ()
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Murtaza Nasir: University of Massachusetts Lowell
Nichalin Summerfield: University of Massachusetts Lowell
Ali Dag: Creighton University
Asil Oztekin: University of Massachusetts Lowell

Service Business, 2020, vol. 14, issue 2, No 5, 287-313

Abstract: 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.

Keywords: Behavioral healthcare; Patient no-shows; Support vector machines; Artificial neural networks; Random forest; Service analytics (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11628-020-00415-8

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