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Patient scheduling based on a service-time prediction model: a data-driven study for a radiotherapy center

Author

Listed:
  • Dina Bentayeb

    (Ecole Polytechnique de Montréal)

  • Nadia Lahrichi

    (Ecole Polytechnique de Montréal)

  • Louis-Martin Rousseau

    (Ecole Polytechnique de Montréal)

Abstract

With the growth of the population, access to medical care is in high demand, and queues are becoming longer. The situation is more critical when it concerns serious diseases such as cancer. The primary problem is inefficient management of patients rather than a lack of resources. In this work, we collaborate with the Centre Intégré de Cancérologie de Laval (CICL). We present a data-driven study based on a nonblock approach to patient appointment scheduling. We use data mining and regression methods to develop a prediction model for radiotherapy treatment duration. The best model is constructed by a classification and regression tree; its accuracy is 84%. Based on the predicted duration, we design new workday divisions, which are evaluated with various patient sequencing rules. The results show that with our approach, 40 additional patients are treated daily in the cancer center, and a considerable improvement is noticed in patient waiting times and technologist overtime.

Suggested Citation

  • 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.
    • Handle: RePEc:kap:hcarem:v:22:y:2019:i:4:d:10.1007_s10729-018-9459-1
    DOI: 10.1007/s10729-018-9459-1
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    References listed on IDEAS

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    1. 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.
    2. Brian Denton & James Viapiano & Andrea Vogl, 2007. "Optimization of surgery sequencing and scheduling decisions under uncertainty," Health Care Management Science, Springer, vol. 10(1), pages 13-24, February.
    3. Golmohammadi, Davood & Radnia, Naeimeh, 2016. "Prediction modeling and pattern recognition for patient readmission," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 151-161.
    4. Conforti, D. & Guerriero, F. & Guido, R., 2010. "Non-block scheduling with priority for radiotherapy treatments," European Journal of Operational Research, Elsevier, vol. 201(1), pages 289-296, February.
    5. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    6. Song-Hee Kim & Ward Whitt & Won Chul Cha, 2018. "A Data-Driven Model of an Appointment-Generated Arrival Process at an Outpatient Clinic," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 181-199, February.
    7. 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.
    8. Antoine Legrain & Marie-Andrée Fortin & Nadia Lahrichi & Louis-Martin Rousseau, 2015. "Online stochastic optimization of radiotherapy patient scheduling," Health Care Management Science, Springer, vol. 18(2), pages 110-123, June.
    9. Lotfi, Vahid & Torres, Edgar, 2014. "Improving an outpatient clinic utilization using decision analysis-based patient scheduling," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 115-126.
    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.
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    Cited by:

    1. 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).

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