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

Kazim Topuz, Hasmet Uner, Asil Oztekin and Mehmet Bayram Yildirim ()
Additional contact information
Kazim Topuz: Wichita State University
Hasmet Uner: Kansas University School of Medicine
Asil Oztekin: University of Massachusetts Lowell
Mehmet Bayram Yildirim: Wichita State University

Annals of Operations Research, 2018, vol. 263, issue 1, No 22, 479-499

Abstract: 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 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-017-2489-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-017-2489-0

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-017-2489-0

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().