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A service analytic approach to studying patient no-shows

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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.

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Acknowledgements

Authors are thankful to Neha Ajgaonkar and Rekha Balan for their help and contribution to an earlier version of this work. They are also grateful to the two anonymous referees for their valuable comments and suggestions which helped improve the paper significantly. Finally, they appreciate the Editor-in-Chief, Dr. Sang M. Lee, for his timely management of this manuscript.

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Authors and Affiliations

  1. University of Massachusetts Lowell, Lowell, MA, USA

    Murtaza Nasir, Nichalin Summerfield & Asil Oztekin

  2. Creighton University, Omaha, NE, USA

    Ali Dag

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  1. Murtaza Nasir
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  2. Nichalin Summerfield
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  4. Asil Oztekin
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Correspondence to Asil Oztekin.

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Nasir, M., Summerfield, N., Dag, A. et al. A service analytic approach to studying patient no-shows. Serv Bus 14, 287–313 (2020). https://doi.org/10.1007/s11628-020-00415-8

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