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Nursing care flexibility in chemotherapy appointment scheduling

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  • Serhat Gul

    (University of Massachusetts Amherst
    TED University)

Abstract

The flexibility level allowed in nursing care delivery and the uncertainty in infusion durations are important factors for chemotherapy scheduling. The nursing care delivery scheme employed in an outpatient chemotherapy clinic (OCC) determines the strictness of the patient-to-nurse assignment policies, while the estimation of infusion durations affects the trade-off between patient waiting time and nurse overtime. We study the problem of daily scheduling of patients, assignment of patients to nurses and chairs in the presence of uncertainty in infusion durations for an OCC that functions according to any of the commonly used nursing care delivery system representing fully, partially, and inflexible care systems. We develop a two-stage stochastic mixed-integer programming model minimizing expected weighted cost of patient waiting time and nurse overtime. We propose multiple variants of a scenario grouping-based decomposition algorithm to solve the model using data from a major university oncology hospital. We compare input-based, solution-based, and random scenario grouping methods within the decomposition algorithm. We obtain near-optimal schedules that are also significantly better than the schedules generated based on the policy used in the clinic. We analyze the impact of nursing care flexibility in order to determine whether a partial or fully flexible delivery system is necessary to adequately improve waiting time and overtime.

Suggested Citation

  • Serhat Gul, 2024. "Nursing care flexibility in chemotherapy appointment scheduling," Flexible Services and Manufacturing Journal, Springer, vol. 36(3), pages 918-945, September.
    • Handle: RePEc:spr:flsman:v:36:y:2024:i:3:d:10.1007_s10696-023-09526-6
    DOI: 10.1007/s10696-023-09526-6
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    References listed on IDEAS

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    1. Nur Banu Demir & Serhat Gul & Melih Çelik, 2021. "A stochastic programming approach for chemotherapy appointment scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 112-133, February.
    2. 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.
    3. Guillaume Lamé & Oualid Jouini & Julie Stal-Le Cardinal, 2016. "Outpatient Chemotherapy Planning: a Literature Review with Insights from a Case Study," Post-Print hal-01324488, HAL.
    4. Karakaya, Sırma & Gul, Serhat & Çelik, Melih, 2023. "Stochastic scheduling of chemotherapy appointments considering patient acuity levels," European Journal of Operational Research, Elsevier, vol. 305(2), pages 902-916.
    5. Alireza F. Hesaraki & Nico P. Dellaert & Ton Kok, 2020. "Integrating nurse assignment in outpatient chemotherapy appointment scheduling," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(4), pages 935-963, December.
    6. Michelle Alvarado & Lewis Ntaimo, 2018. "Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming," Health Care Management Science, Springer, vol. 21(1), pages 87-104, March.
    7. Avishai Mandelbaum & Petar Momčilović & Nikolaos Trichakis & Sarah Kadish & Ryan Leib & Craig A. Bunnell, 2020. "Data-Driven Appointment-Scheduling Under Uncertainty: The Case of an Infusion Unit in a Cancer Center," Management Science, INFORMS, vol. 66(1), pages 243-270, January.
    8. Ryan F. Slocum & Herbert L. Jones & Matthew T. Fletcher & Brandon M. McConnell & Thom J. Hodgson & Javad Taheri & James R. Wilson, 2021. "Improving chemotherapy infusion operations through the simulation of scheduling heuristics: a case study," Health Systems, Taylor & Francis Journals, vol. 10(3), pages 163-178, July.
    9. Mike Hewitt & Janosch Ortmann & Walter Rei, 2022. "Decision-based scenario clustering for decision-making under uncertainty," Annals of Operations Research, Springer, vol. 315(2), pages 747-771, August.
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