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Design and Optimization Methods for Elective Hospital Admissions

Author

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  • Jonathan E. Helm

    (Department of Operations and Decision Technologies, Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • Mark P. Van Oyen

    (Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109)

Abstract

Hospitals typically lack effective enterprise level strategic planning of bed and care resources, contributing to bed census levels that are statistically “out of control.” This system dysfunction manifests itself in bed block, surgical cancelation, ambulance diversions, and operational chaos. This is the classic hospital admission scheduling and control (HASC) problem, which has been addressed in its entirety only through inexact simulation-based search heuristics. This paper develops new analytical models of controlled hospital census that can, for the first time, be incorporated into a mixed-integer programming model to optimally solve the strategic planning/scheduling portion of the HASC. Our new solution method coordinates elective admissions with other hospital subsystems to reduce system congestion. We formulate a new Poisson-arrival-location model (PALM) based on an innovative stochastic location process that we developed and call the patient temporal resource needs model. We further extend the PALM approach to the class of deterministic controlled-arrival-location models (d-CALM) and develop linearizing approximations to stochastic blocking metrics. This work provides the theoretical foundations for an efficient scheduled admissions planning system as well as a practical decision support methodology to stabilize hospital census.

Suggested Citation

  • Jonathan E. Helm & Mark P. Van Oyen, 2014. "Design and Optimization Methods for Elective Hospital Admissions," Operations Research, INFORMS, vol. 62(6), pages 1265-1282, December.
    • Handle: RePEc:inm:oropre:v:62:y:2014:i:6:p:1265-1282
    DOI: 10.1287/opre.2014.1317
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    Cited by:

    1. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
    2. Karsten Schwarz & Michael Römer & Taïeb Mellouli, 2019. "A data-driven hierarchical MILP approach for scheduling clinical pathways: a real-world case study from a German university hospital," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 597-636, December.
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    4. Jingui Xie & Weifen Zhuang & Marcus Ang & Mabel C. Chou & Li Luo & David D. Yao, 2021. "Analytics for Hospital Resource Planning—Two Case Studies," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1863-1885, June.
    5. J. G. Dai & Pengyi Shi, 2019. "Inpatient Overflow: An Approximate Dynamic Programming Approach," Manufacturing & Service Operations Management, INFORMS, vol. 21(4), pages 894-911, October.
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    7. Soroush Saghafian & Nikolaos Trichakis & Ruihao Zhu & Helen A. Shih, 2023. "Joint patient selection and scheduling under no‐shows: Theory and application in proton therapy," Production and Operations Management, Production and Operations Management Society, vol. 32(2), pages 547-563, February.
    8. 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.
    9. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    10. Amirhossein Meisami & Jivan Deglise-Hawkinson & Mark E. Cowen & Mark P. Oyen, 2019. "Data-driven optimization methodology for admission control in critical care units," Health Care Management Science, Springer, vol. 22(2), pages 318-335, June.
    11. Jim G. Dai & Pengyi Shi, 2021. "Recent Modeling and Analytical Advances in Hospital Inpatient Flow Management," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1838-1862, June.
    12. Jillian A. Berry Jaeker & Anita L. Tucker, 2017. "Past the Point of Speeding Up: The Negative Effects of Workload Saturation on Efficiency and Patient Severity," Management Science, INFORMS, vol. 63(4), pages 1042-1062, April.
    13. Neyshabouri, Saba & Berg, Bjorn P., 2017. "Two-stage robust optimization approach to elective surgery and downstream capacity planning," European Journal of Operational Research, Elsevier, vol. 260(1), pages 21-40.
    14. Alex F. Mills & Jonathan E. Helm & Yu Wang, 2021. "Surge Capacity Deployment in Hospitals: Effectiveness of Response and Mitigation Strategies," Manufacturing & Service Operations Management, INFORMS, vol. 23(2), pages 367-387, March.
    15. Wang, Yu & Zhang, Yu & Tang, Jiafu, 2019. "A distributionally robust optimization approach for surgery block allocation," European Journal of Operational Research, Elsevier, vol. 273(2), pages 740-753.
    16. David Oakley & Bhakti Stephan Onggo & Dave Worthington, 2020. "Symbiotic simulation for the operational management of inpatient beds: model development and validation using Δ-method," Health Care Management Science, Springer, vol. 23(1), pages 153-169, March.
    17. Hessam Bavafa & Charles M. Leys & Lerzan Örmeci & Sergei Savin, 2019. "Managing Portfolio of Elective Surgical Procedures: A Multidimensional Inverse Newsvendor Problem," Operations Research, INFORMS, vol. 67(6), pages 1543-1563, November.
    18. Minglong Zhou & Melvyn Sim & Shao‐Wei Lam, 2022. "Advance admission scheduling via resource satisficing," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4002-4020, November.

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