IDEAS home Printed from https://ideas.repec.org/a/igg/jhisi0/v15y2020i4p18-37.html
   My bibliography  Save this article

Last Minute Medical Appointments No-Show Management

Author

Listed:
  • Daniel M. Sousa

    (Instituto Superior Técnico, Lisbon University, Portugal)

  • André Vasconcelos

    (Instituto Superior Tecnico, University of Lisbon, Portugal)

Abstract

A no-show occurs when a client has an appointment of some sort with another entity, and voluntarily or not, the client does not show up to that appointment. A patient missing an appointment will mean that the clinic's and health professional's time slot will be wasted. The goal of this research is to find a solution that minimizes no-shows, detecting when a patient is not going to come to the appointment and finding an appropriate replacement. The authors propose a hybrid solution which combines two different behavior prediction techniques: population-based behavior and individual-based behavior. The algorithm starts by computing a no-show probability based on the population's behavior using a logistic regression model. After that, using Bayesian inference, that probability is personalized for each patient. After computing the no-show probabilities for every candidate patient, the algorithm checks if any of them are interested on taking the appointment. The proposed algorithm was assessed using lab data and healthcare provider data.

Suggested Citation

  • Daniel M. Sousa & André Vasconcelos, 2020. "Last Minute Medical Appointments No-Show Management," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 15(4), pages 18-37, October.
    • Handle: RePEc:igg:jhisi0:v:15:y:2020:i:4:p:18-37
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJHISI.2020100102
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. K J Glowacka & R M Henry & J H May, 2009. "A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1056-1068, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adel Alaeddini & Kai Yang & Chandan Reddy & Susan Yu, 2011. "A probabilistic model for predicting the probability of no-show in hospital appointments," Health Care Management Science, Springer, vol. 14(2), pages 146-157, June.
    2. 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).
    3. M. M. Malik & S. Abdallah & M. Ala’raj, 2018. "Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review," Annals of Operations Research, Springer, vol. 270(1), pages 287-312, November.
    4. 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.
    5. Dina Bentayeb & Nadia Lahrichi & Louis-Martin Rousseau, 2019. "Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center," Health Care Management Science, Springer, vol. 22(4), pages 768-782, December.
    6. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G. & Foster, Krista M., 2020. "The effect of cancelled appointments on outpatient clinic operations," European Journal of Operational Research, Elsevier, vol. 284(3), pages 847-860.
    7. Kuiper, Alex & de Mast, Jeroen & Mandjes, Michel, 2021. "The problem of appointment scheduling in outpatient clinics: A multiple case study of clinical practice," Omega, Elsevier, vol. 98(C).
    8. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).
    9. Ruijie Zhang & Xiaohua Han & Rowan Wang & Jianghua Zhang & Yinghao Zhang, 2023. "Please don't make me wait! Influence of customers' waiting preference and no‐show behavior on appointment systems," Production and Operations Management, Production and Operations Management Society, vol. 32(6), pages 1597-1616, June.
    10. Meisel, Stephan & Mattfeld, Dirk, 2010. "Synergies of Operations Research and Data Mining," European Journal of Operational Research, Elsevier, vol. 206(1), pages 1-10, October.
    11. Samorani, Michele & LaGanga, Linda R., 2015. "Outpatient appointment scheduling given individual day-dependent no-show predictions," European Journal of Operational Research, Elsevier, vol. 240(1), pages 245-257.
    12. Harris, Shannon L. & May, Jerrold H. & Vargas, Luis G., 2016. "Predictive analytics model for healthcare planning and scheduling," European Journal of Operational Research, Elsevier, vol. 253(1), pages 121-131.
    13. Sharan Srinivas & A. Ravi Ravindran, 2020. "Designing schedule configuration of a hybrid appointment system for a two-stage outpatient clinic with multiple servers," Health Care Management Science, Springer, vol. 23(3), pages 360-386, September.
    14. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jhisi0:v:15:y:2020:i:4:p:18-37. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.