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A hybrid data mining/simulation approach for modelling outpatient no-shows in clinic scheduling

Author

Listed:
  • K J Glowacka

    (McGill University)

  • R M Henry

    (Clemson University)

  • J H May

    (University of Pittsburgh)

Abstract

This paper considers the outpatient no-show problem faced by a rural free clinic located in the south-eastern United States. Using data mining and simulation techniques, we develop sequencing schemes for patients, in order to optimize a combination of performance measures used at the clinic. We utilize association rule mining (ARM) to build a model for predicting patient no-shows; and then use a set covering optimization method to derive three manageable sets of rules for patient sequencing. Simulation is used to determine the optimal number of patients and to evaluate the models. The ARM technique presented here results in significant improvements over models that do not employ rules, supporting the conjecture that, when dealing with noisy data such as in an outpatient clinic, extracting partial patterns, as is done by ARM, can be of significant value for simulation modelling.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:8:d:10.1057_jors.2008.177
    DOI: 10.1057/jors.2008.177
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    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. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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).
    13. 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.
    14. 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.
    15. 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.

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