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Joint Patient Selection and Scheduling under No-Shows: Theory and Application in Proton Therapy

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  • Saghafian, Soroush

    (Harvard Kennedy School)

  • Trichakis, Nikolaos

    (Massachusetts Institute of Technology)

  • Zhu, Ruihao

    (Massachusetts Institute of Technology)

  • Shih, Helen A.

    (Massachusetts General Hospital)

Abstract

Motivated by operational challenges facing adopters of new technologies in the service industry, we study how to admit and schedule customers from a pool of heterogeneous potential users when capacity is scarce. We model schedule-dependent no-show behavior and overtime costs as two important features that can significantly affect operational performance. We start by formulating the problem as a nonlinear integer optimization problem. However, since the solution to this formulation lacks both tractability and interpretability, to be relevant to practice, we limit our study to simple and interpretable policies that can be implemented in practice. In particular, we propose a simple index-based rule and derive analytical performance guarantees for it, which reveal its strong performance compared to the optimal solution. Our analytical performance analysis also demonstrates the robustness of the proposed policy to potential misspecification of no-show probabilities which are hard to accurately estimate in practice. Importantly, we test the validating of our approach through partnership with the proton therapy center of Massachusetts General Hospital (MGH), which offers a new radiation technology for cancer patients. We calibrate our model using empirical data from our partner hospital, and conduct a series of experiments to evaluate the performance of our proposed policy under practical circumstances. Put together, these experiments show that our proposed policy, despite being a simple and interpretable index-based rule, is capable of improving performance by about 20% at an organization such as MGH, and of delivering results that are not far from being optimal across a wide range of parameters that might vary between organizations. This suggests that the proposed policy can be viewed as an effective "one-fits-all" capacity allocation rule that can be used in a variety of environments in which operational challenges such as no-shows and overtime costs need to be navigated using simple and interpretable rules.

Suggested Citation

  • Saghafian, Soroush & Trichakis, Nikolaos & Zhu, Ruihao & Shih, Helen A., 2019. "Joint Patient Selection and Scheduling under No-Shows: Theory and Application in Proton Therapy," Working Paper Series rwp19-019, Harvard University, John F. Kennedy School of Government.
  • Handle: RePEc:ecl:harjfk:rwp19-019
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    References listed on IDEAS

    as
    1. Guido Kaandorp & Ger Koole, 2007. "Optimal outpatient appointment scheduling," Health Care Management Science, Springer, vol. 10(3), pages 217-229, September.
    2. Thomas Bortfeld & Timothy C. Y. Chan & Alexei Trofimov & John N. Tsitsiklis, 2008. "Robust Management of Motion Uncertainty in Intensity-Modulated Radiation Therapy," Operations Research, INFORMS, vol. 56(6), pages 1461-1473, December.
    3. Carri W. Chan & Linda V. Green & Yina Lu & Nicole Leahy & Roger Yurt, 2013. "Prioritizing Burn-Injured Patients During a Disaster," Manufacturing & Service Operations Management, INFORMS, vol. 15(2), pages 170-190, May.
    4. Ariel Kulik & Hadas Shachnai & Tami Tamir, 2013. "Approximations for Monotone and Nonmonotone Submodular Maximization with Knapsack Constraints," Mathematics of Operations Research, INFORMS, vol. 38(4), pages 729-739, November.
    5. Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
    6. Chan, Timothy C.Y. & Mišić, Velibor V., 2013. "Adaptive and robust radiation therapy optimization for lung cancer," European Journal of Operational Research, Elsevier, vol. 231(3), pages 745-756.
    7. Jianzhe Luo & Vidyadhar G. Kulkarni & Serhan Ziya, 2012. "Appointment Scheduling Under Patient No-Shows and Service Interruptions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 670-684, October.
    8. Refael Hassin & Sharon Mendel, 2008. "Scheduling Arrivals to Queues: A Single-Server Model with No-Shows," Management Science, INFORMS, vol. 54(3), pages 565-572, March.
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