Surgery Sequencing Coordination with Recovery Resource Constraints

Miao Bai (), Robert H. Storer () and Gregory L. Tonkay ()
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Miao Bai: Department of Operations and Information Management, University of Connecticut, Storrs, Connecticut 06269
Robert H. Storer: Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015
Gregory L. Tonkay: Department of Industrial and Systems Engineering, Lehigh University, Bethlehem, Pennsylvania 18015

INFORMS Journal on Computing, 2022, vol. 34, issue 2, 1207-1223

Abstract: Surgical practice administrators need to determine the sequence of surgeries and reserved operating room (OR) time for each surgery in the surgery scheduling process. Both decisions require coordination among multiple ORs and the recovery resource in the postanesthesia care unit (PACU) in a surgical suite. Although existing studies have addressed OR time reservation, surgery sequencing coordination is an open challenge in the stochastic surgical environment. In this paper, we propose an algorithmic solution to this problem based on stochastic optimization. The proposed methodology involves the development of a surrogate objective function that is highly correlated with the original one. The resulting surrogate model has network-structured subproblems after Lagrangian relaxation and decomposition, which makes it easier to solve than the impractically difficult original problem. We show that our proposed approach finds near-optimal solutions in small instances and outperforms benchmark methods by 13%–51% or equivalently an estimated saving of $760–$7,420 per day in surgical suites with 4–10 ORs. Our results illustrate a mechanism to alleviate congestion in the PACU. We also recommend that practice administrators prioritize sequencing coordination over the optimization of OR time reservation in an effort for performance improvement. Furthermore, we demonstrate how administrators should consider the impact of sequencing decisions when making strategic capacity adjustments for the PACU. Summary of Contribution: Our work provides an algorithmic solution to an open question in the field of healthcare operations management. This solution approach involves formulating a surrogate optimization model and exploiting its decomposability and network-structure. In computational experiments, we quantitatively benchmark its performance and assess its benefits. Our numerical results provide unique managerial insights for healthcare leadership.

Keywords: stochastic programming; healthcare; surgery sequencing; recovery resource (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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