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Stochastic programming for outpatient scheduling with flexible inpatient exam accommodation

Author

Listed:
  • Yifei Sun

    (Dartmouth College)

  • Usha Nandini Raghavan

    (Philips Healthcare)

  • Vikrant Vaze

    (Dartmouth College)

  • Christopher S Hall

    (Philips Healthcare)

  • Patricia Doyle

    (Lahey Hospital and Medical Center)

  • Stacey Sullivan Richard

    (Lahey Hospital and Medical Center)

  • Christoph Wald

    (Lahey Hospital and Medical Center)

Abstract

This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams cannot be cancelled. Stochastic programming models were formulated and solved to tackle the stochasticity in the procedure durations and patient arrival patterns. The first model, a two-stage stochastic programming model, is formulated to optimize the slot size. The second model further optimizes the inpatient block (IPB) placement and slot size simultaneously. A computational method is developed to solve the second optimization problem. A case study is conducted using the data from Magnetic Resonance Imaging (MRI) centers of Lahey Hospital and Medical Center (LHMC). The current schedule and the schedules obtained from the optimization models are evaluated and compared using simulation based on FlexSim Healthcare. Results indicate that the overall weighted cost can be reduced by 11.6% by optimizing the slot size and can be further reduced by an additional 12.6% by optimizing slot size and IPB placement simultaneously. Three commonly used sequencing rules (IPBEG, OPBEG, and a variant of ALTER rule) were also evaluated. The results showed that when optimization tools are not available, ALTER variant which evenly distributes the IPBs across the day has the best performance. Sensitivity analysis of weights for patient waiting time, machine idle time and exam cancellations further supports the superiority of ALTER variant sequencing rules compared to the other sequencing methods. A Pareto frontier was also developed and presented between patient waiting time and machine idle time to enable medical centers with different priorities to obtain solutions that accurately reflect their respective optimal tradeoffs. An extended optimization model was also developed to incorporate the emergency patient arrivals. The optimal schedules from the extended model show only minor differences compared to those from the original model, thus proving the robustness of the scheduling solutions obtained from our optimal models against the impacts of emergency patient arrivals.

Suggested Citation

  • Yifei Sun & Usha Nandini Raghavan & Vikrant Vaze & Christopher S Hall & Patricia Doyle & Stacey Sullivan Richard & Christoph Wald, 2021. "Stochastic programming for outpatient scheduling with flexible inpatient exam accommodation," Health Care Management Science, Springer, vol. 24(3), pages 460-481, September.
    • Handle: RePEc:kap:hcarem:v:24:y:2021:i:3:d:10.1007_s10729-020-09527-z
    DOI: 10.1007/s10729-020-09527-z
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    References listed on IDEAS

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    1. Chrwan-Jyh Ho & Hon-Shiang Lau, 1992. "Minimizing Total Cost in Scheduling Outpatient Appointments," Management Science, INFORMS, vol. 38(12), pages 1750-1764, December.
    2. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    3. Mehmet A. Begen & Maurice Queyranne, 2011. "Appointment Scheduling with Discrete Random Durations," Mathematics of Operations Research, INFORMS, vol. 36(2), pages 240-257, May.
    4. Federico Sabria & Carlos F. Daganzo, 1989. "Approximate Expressions for Queueing Systems with Scheduled Arrivals and Established Service Order," Transportation Science, INFORMS, vol. 23(3), pages 159-165, August.
    5. Sujin Kim & Raghu Pasupathy & Shane G. Henderson, 2015. "A Guide to Sample Average Approximation," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 207-243, Springer.
    6. Kenneth J. Klassen & Reena Yoogalingam, 2019. "Appointment scheduling in multi-stage outpatient clinics," Health Care Management Science, Springer, vol. 22(2), pages 229-244, June.
    7. 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.
    8. Mehmet A. Begen & Retsef Levi & Maurice Queyranne, 2012. "Technical Note---A Sampling-Based Approach to Appointment Scheduling," Operations Research, INFORMS, vol. 60(3), pages 675-681, June.
    9. Camilo Mancilla & Robert Storer, 2012. "A sample average approximation approach to stochastic appointment sequencing and scheduling," IISE Transactions, Taylor & Francis Journals, vol. 44(8), pages 655-670.
    10. J Patrick & M L Puterman, 2007. "Improving resource utilization for diagnostic services through flexible inpatient scheduling: A method for improving resource utilization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 235-245, February.
    11. Ho, Chrwan-Jyh & Lau, Hon-Shiang, 1999. "Evaluating the impact of operating conditions on the performance of appointment scheduling rules in service systems," European Journal of Operational Research, Elsevier, vol. 112(3), pages 542-553, February.
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