Annals of Operations Research

, Volume 263, Issue 1–2, pp 479–499 | Cite as

Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network

  • Kazim Topuz
  • Hasmet Uner
  • Asil Oztekin
  • Mehmet Bayram Yildirim
Data Mining and Analytics
First Online:

Abstract

No-shows are becoming a major problem in primary care facilities, creating additional costs for the facility while adversely affecting the quality of patient care. Accurately predicting no-shows plays an important role in the overbooking strategy. In this study, a hybrid probabilistic prediction framework based on the elastic net (EN) variable-selection methodology integrated with probabilistic Bayesian Belief Network (BBN) is proposed. The study predicts the “no-show probability of the patient(s)” using demographics, socioeconomic status, current appointment information, and appointment attendance history of the patient and the family. The proposed framework is validated using ten years of local pediatric clinic data. It is shown that this EN-based BBN framework is a comparable prediction methodology regarding the best approaches found in the literature. More importantly, this methodology provides novel information on the interrelations of predictors and the conditional probability of predicting “no-shows.” The output of the model can be applied to the appointment scheduling system for a robust overbooking strategy.

Keywords

No-show prediction Elastic net Bayesian belief networks Healthcare analytics 
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Notes

Acknowledgements

We are grateful to the two anonymous reviewers for their constructive comments and suggestions.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Kazim Topuz
    • 1
  • Hasmet Uner
    • 2
  • Asil Oztekin
    • 3
  • Mehmet Bayram Yildirim
    • 1
  1. 1.Department of Industrial and Manufacturing EngineeringWichita State UniversityWichitaUSA
  2. 2.Department of PediatricsKansas University School of MedicineWichitaUSA
  3. 3.Operations and Information Systems, Biomedical Engineering and Biotechnology ProgramUniversity of Massachusetts LowellLowellUSA

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