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A Two-Time-Scale Approach to Time-Varying Queues in Hospital Inpatient Flow Management

Author

Listed:
  • J. G. Dai

    (School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853)

  • Pengyi Shi

    (Krannert School of Management, Purdue University, West Lafayette, Indiana 47907)

Abstract

We analyze a time-varying M peri /Geo 2timeScale / N queueing system. The arrival process is periodic Poisson. The service time of a customer has components in different time scales: length of stay (LOS) in days and departure time ( h dis ) in hours. This queueing system has been used to study patient flows from the emergency department (ED) to hospital inpatient wards. In that setting, the LOS of a patient is simply the number of days she spends in a ward, and her departure time h dis is the discharge hour on the day of her discharge. We develop a new analytical framework that can perform exact analysis on this novel queueing system. This framework has two steps: first analyze the midnight customer count process and obtain its stationary distribution, then analyze the time-dependent customer count process to compute various performance measures. We also develop approximation tools that can significantly reduce the computational time. In particular, via Stein’s method, we derive explicit expressions to approximate the stationary distribution of the midnight count. We provide error bounds for these approximations and numerically demonstrate that they are remarkably accurate for systems with various sizes and load conditions. Our theoretical and numerical analysis have produced a number of insights that can be used to improve hospital inpatient flow management. We find that the LOS term affects the overnight wait caused by the mismatch between daily arrivals and discharges, whereas the h dis term affects the intraday wait caused by the nonsynchronization between the arrival and discharge time patterns. Thus, reducing LOS or increasing capacity can impact the daily average performance significantly; shifting the discharge timing to earlier times of a day can alleviate the peak congestion in the morning and mainly affects the time-dependent performance.

Suggested Citation

  • J. G. Dai & Pengyi Shi, 2017. "A Two-Time-Scale Approach to Time-Varying Queues in Hospital Inpatient Flow Management," Operations Research, INFORMS, vol. 65(2), pages 514-536, April.
    • Handle: RePEc:inm:oropre:v:65:y:2017:i:2:p:514-536
    DOI: 10.1287/opre.2016.1566
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    References listed on IDEAS

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    1. Gagan L. Choudhury & David M. Lucantoni & Ward Whitt, 1997. "Numerical Solution of Piecewise-Stationary M t / G t /1 Queues," Operations Research, INFORMS, vol. 45(3), pages 451-463, June.
    2. Linda Green & Peter Kolesar, 1991. "The Pointwise Stationary Approximation for Queues with Nonstationary Arrivals," Management Science, INFORMS, vol. 37(1), pages 84-97, January.
    3. Linda V. Green & Peter J. Kolesar, 1997. "The Lagged PSA for Estimating Peak Congestion in Multiserver Markovian Queues with Periodic Arrival Rates," Management Science, INFORMS, vol. 43(1), pages 80-87, January.
    4. Michael H. Rothkopf & Shmuel S. Oren, 1979. "A Closure Approximation for the Nonstationary M/M/s Queue," Management Science, INFORMS, vol. 25(6), pages 522-534, June.
    5. Noah Gans & Ger Koole & Avishai Mandelbaum, 2003. "Telephone Call Centers: Tutorial, Review, and Research Prospects," Manufacturing & Service Operations Management, INFORMS, vol. 5(2), pages 79-141, September.
    6. Armann Ingolfsson & Elvira Akhmetshina & Susan Budge & Yongyue Li & Xudong Wu, 2007. "A Survey and Experimental Comparison of Service-Level-Approximation Methods for Nonstationary M(t)/M/s(t) Queueing Systems with Exhaustive Discipline," INFORMS Journal on Computing, INFORMS, vol. 19(2), pages 201-214, May.
    7. Ward Whitt, 1991. "The Pointwise Stationary Approximation for Mt/Mt/s Queues Is Asymptotically Correct As the Rates Increase," Management Science, INFORMS, vol. 37(3), pages 307-314, March.
    8. Galit B. Yom-Tov & Avishai Mandelbaum, 2014. "Erlang-R: A Time-Varying Queue with Reentrant Customers, in Support of Healthcare Staffing," Manufacturing & Service Operations Management, INFORMS, vol. 16(2), pages 283-299, May.
    9. Shlomo Halfin & Ward Whitt, 1981. "Heavy-Traffic Limits for Queues with Many Exponential Servers," Operations Research, INFORMS, vol. 29(3), pages 567-588, June.
    10. Zohar Feldman & Avishai Mandelbaum & William A. Massey & Ward Whitt, 2008. "Staffing of Time-Varying Queues to Achieve Time-Stable Performance," Management Science, INFORMS, vol. 54(2), pages 324-338, February.
    11. Avishai Mandelbaum & Petar Momčilović & Yulia Tseytlin, 2012. "On Fair Routing from Emergency Departments to Hospital Wards: QED Queues with Heterogeneous Servers," Management Science, INFORMS, vol. 58(7), pages 1273-1291, July.
    12. Pengyi Shi & Mabel C. Chou & J. G. Dai & Ding Ding & Joe Sim, 2016. "Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time," Management Science, INFORMS, vol. 62(1), pages 1-28, January.
    13. Jacqueline Griffin & Shuangjun Xia & Siyang Peng & Pinar Keskinocak, 2012. "Improving patient flow in an obstetric unit," Health Care Management Science, Springer, vol. 15(1), pages 1-14, March.
    14. Otis B. Jennings & Avishai Mandelbaum & William A. Massey & Ward Whitt, 1996. "Server Staffing to Meet Time-Varying Demand," Management Science, INFORMS, vol. 42(10), pages 1383-1394, October.
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