Acuity-Based Allocation of ICU-Downstream Beds with Flexible Staffing

Silviya Valeva (), Guodong Pang (), Andrew J. Schaefer () and Gilles Clermont ()
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Silviya Valeva: Department of Decision & System Sciences, Erivan K. Haub School of Business, Saint Joseph’s University, Philadelphia, Pennsylvania 19131
Guodong Pang: Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, Texas 77251
Andrew J. Schaefer: Department of Computational Applied Mathematics and Operations Research, Rice University, Houston, Texas 77251
Gilles Clermont: Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15260

INFORMS Journal on Computing, 2023, vol. 35, issue 2, 403-422

Abstract: Intensive care units (ICUs) are crucial resources within hospitals, caring for the most critically ill patients. We propose a novel modeling framework that improves the outflow of ICU patients by anticipating unit interactions and resource sharing within the system. Across an arbitrary bipartite network of units, we consider two types of downstream staffing (baseline and flexible) and a two-stage decision process. In the first stage, we determine the level of flexible bed staffing using existing physical beds at downstream units in anticipation of incoming transfers from the ICUs. In the second stage, we determine the allocation of ICU patients to downstream beds. The goal of the model is to reduce inefficiencies and transfer delays causing ICU bed block due to lack of space in downstream units. We formulate a dynamic multiperiod model and analyze the dual of its (relaxed) stationary counterpart. Decomposing the relaxed stationary model into an ICU and downstream subproblems, we calculate the relative values of downstream beds and derive a practical acuity-based policy for the daily operational decisions. Using a large-scale simulation calibrated with historic hospital data, we demonstrate that our acuity-based policy reduces the number of long-run diverted ICU arrivals, particularly in high-demand scenarios, thus improving ICU throughput, when compared with a deterministic, a generalized randomized-most-idle, and static policies.

Keywords: ICU management; flexible staffing; acuity-based policy (search for similar items in EconPapers)
Date: 2023
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