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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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    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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