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

Author

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  • Kazim Topuz

    (Wichita State University)

  • Hasmet Uner

    (Kansas University School of Medicine)

  • Asil Oztekin

    (University of Massachusetts Lowell)

  • Mehmet Bayram Yildirim

    (Wichita State University)

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.

Suggested Citation

  • Kazim Topuz & Hasmet Uner & Asil Oztekin & Mehmet Bayram Yildirim, 2018. "Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network," Annals of Operations Research, Springer, vol. 263(1), pages 479-499, April.
    • Handle: RePEc:spr:annopr:v:263:y:2018:i:1:d:10.1007_s10479-017-2489-0
    DOI: 10.1007/s10479-017-2489-0
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    2. Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.
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    5. F. Benedetto & L. Mastroeni & P. Vellucci, 2021. "Modeling the flow of information between financial time-series by an entropy-based approach," Annals of Operations Research, Springer, vol. 299(1), pages 1235-1252, April.
    6. Dominik Schreyer & Sascha L. Schmidt & Benno Torgler, 2019. "Football Spectator No-Show Behavior," Journal of Sports Economics, , vol. 20(4), pages 580-602, May.
    7. Wang, Qiang & Zhang, Wen & Li, Jian & Ma, Zhenzhong, 2023. "Complements or confounders? A study of effects of target and non-target features on online fraudulent reviewer detection," Journal of Business Research, Elsevier, vol. 167(C).
    8. J. Dunstan & F. Villena & J.P. Hoyos & V. Riquelme & M. Royer & H. Ramírez & J. Peypouquet, 2023. "Predicting no-show appointments in a pediatric hospital in Chile using machine learning," Health Care Management Science, Springer, vol. 26(2), pages 313-329, June.
    9. 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.
    10. Simsek, Serhat & Dag, Ali & Tiahrt, Thomas & Oztekin, Asil, 2021. "A Bayesian Belief Network-based probabilistic mechanism to determine patient no-show risk categories," Omega, Elsevier, vol. 100(C).
    11. Cheng Wang & Runhua Wu & Lili Deng & Yong Chen & Yingde Li & Yuehua Wan, 2020. "A Bibliometric Analysis on No-Show Research: Status, Hotspots, Trends and Outlook," Sustainability, MDPI, vol. 12(10), pages 1-17, May.
    12. Cankaya, Burak & Topuz, Kazim & Delen, Dursun & Glassman, Aaron, 2023. "Evidence-based managerial decision-making with machine learning: The case of Bayesian inference in aviation incidents," Omega, Elsevier, vol. 120(C).
    13. Rukiye Kaya & Said Salhi & Virginia Spiegler, 2023. "A novel integration of MCDM methods and Bayesian networks: the case of incomplete expert knowledge," Annals of Operations Research, Springer, vol. 320(1), pages 205-234, January.

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