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. 2021 Dec;24(4):666-685.
doi: 10.1007/s10729-021-09560-6. Epub 2021 Jun 10.

Managing admission and discharge processes in intensive care units

Jie Bai  1 , Andreas Fügener  2 , Jochen Gönsch  3 , Jens O Brunner  4 , Manfred Blobner  5
Affiliations

Managing admission and discharge processes in intensive care units

Jie Bai et al. Health Care Manag Sci. 2021 Dec.

Abstract

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously. This paper discusses the trade-off between medical and monetary goals when managing intensive care units by modeling the problem as a Markov decision process. Intuitive, myopic rule mimicking decision-making in practice is applied as a benchmark. In a numerical study based on real-world data, we demonstrate that the medical results deteriorate dramatically when focusing on monetary goals only, and vice versa. Using our model, we illustrate the trade-off along an efficiency frontier that accounts for all combinations of medical and monetary goals. Coming from a solution that optimizes monetary costs, a significant reduction of expected mortality can be achieved at little additional monetary cost.

Keywords: Admission and discharge decisions; Dynamic programming; Intensive care unit; Markov decision process; Operations research.

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Figures

Fig. 1
Fig. 1
The patient flow in the ICU
Fig. 2
Fig. 2
Sequence of events
Fig. 3
Fig. 3
Comparison of historical and theoretical arrival process
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Fig. 4
Comparison of historical and theoretical LOS distribution
Fig. 5
Fig. 5
Comparison of the policies of Myopicmed (upper row) and MDPmed (lower row)
Fig. 6
Fig. 6
Comparison of the policies of Myopicmon (upper row) and MDPmon (lower row)
Fig. 7
Fig. 7
Frequency of ICU occupancy for MDPmed ([%]; empty/white: never observed after warm-up)
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Fig. 8
Frequency of ICU occupancy for MDPmon ([%]; empty/white: never observed after warm-up)
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Fig. 9
Medical and monetary costs for all five events
Fig. 10
Fig. 10
The capacity saved by MDP
Fig. 11
Fig. 11
The trade off between medical and monetary cost
See this image and copyright information in PMC

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