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A service analytic approach to studying patient no-shows

Author

Listed:
  • Murtaza Nasir

    (University of Massachusetts Lowell)

  • Nichalin Summerfield

    (University of Massachusetts Lowell)

  • Ali Dag

    (Creighton University)

  • Asil Oztekin

    (University of Massachusetts Lowell)

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.

Suggested Citation

  • Murtaza Nasir & Nichalin Summerfield & Ali Dag & Asil Oztekin, 2020. "A service analytic approach to studying patient no-shows," Service Business, Springer;Pan-Pacific Business Association, vol. 14(2), pages 287-313, June.
    • Handle: RePEc:spr:svcbiz:v:14:y:2020:i:2:d:10.1007_s11628-020-00415-8
    DOI: 10.1007/s11628-020-00415-8
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