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Appointment and patient scheduling in chemotherapy: a case study in Chilean hospitals

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  • Camila Ramos

    (Pontificia Universidad Católica de Chile)

  • Alejandro Cataldo

    (Pontificia Universidad Católica de Chile)

  • Juan–Carlos Ferrer

    (Pontificia Universidad Católica de Chile)

Abstract

This research addresses a scheduling problem for chemotherapy patients, which is divided in two subproblems: patient scheduling on an infinite horizon and daily patient scheduling. We consider the requirement for available laboratory hours to prepare the medicine for every patient as an additional complexity. A methodology was formulated that addresses the problem in two stages. The first one is based on previous research and implements a scheduling policy for chemotherapy. The result of this first stage is the input for the second stage, which is addressed by generating treatment patterns. The benefits of both stages of the proposed methodology are evaluated for two real cases, one of them in the Chemotherapy Division of Hospital Salvador, and the other in the Cancer Center, of the Clinical Hospital of the Pontificia Universidad Católica de Chile, both in Santiago, Chile. Regarding the costs’ impact, the method proposed manages to reduce 20% and 17% the operational costs in these cases, due to less extra treatment hours needed. On the other hand, the proposed daily scheduling method for patients presents an improvement of 21% and 9% in care slots usage and 22% and 17% in laboratory slots usage for an average demand day, which translates in a reduction of both extra hours used and workday duration, for the case of the Chemotherapy Division and Cancer Center, respectively.

Suggested Citation

  • Camila Ramos & Alejandro Cataldo & Juan–Carlos Ferrer, 2020. "Appointment and patient scheduling in chemotherapy: a case study in Chilean hospitals," Annals of Operations Research, Springer, vol. 286(1), pages 411-439, March.
    • Handle: RePEc:spr:annopr:v:286:y:2020:i:1:d:10.1007_s10479-018-3085-7
    DOI: 10.1007/s10479-018-3085-7
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    Cited by:

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    3. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.

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