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Service Business
Erschienen in: Service Business 2/2020

12.05.2020 | Theoretical article

A service analytic approach to studying patient no-shows

verfasst von: Murtaza Nasir, Nichalin Summerfield, Ali Dag, Asil Oztekin

Erschienen in: Service Business | Ausgabe 2/2020

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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.
Vorheriger Artikel Analysis of the relationship between the perceived extent of a tourist destination and smartphone use

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Metadaten
Titel
A service analytic approach to studying patient no-shows
verfasst von
Murtaza Nasir
Nichalin Summerfield
Ali Dag
Asil Oztekin
Publikationsdatum
12.05.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
Service Business / Ausgabe 2/2020
Print ISSN: 1862-8516
Elektronische ISSN: 1862-8508
DOI
https://doi.org/10.1007/s11628-020-00415-8

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