Intensive care unit discharge policies prior to treatment completion

Hosseinifard, S. Zahra, Abbasi, Babak and Minas, James P. 2014, Intensive care unit discharge policies prior to treatment completion, Operations research for health care, vol. 3, no. 3, pp. 168-175, doi: 10.1016/j.orhc.2014.06.001.

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Title Intensive care unit discharge policies prior to treatment completion
Author(s) Hosseinifard, S. ZahraORCID iD for Hosseinifard, S. Zahra
Abbasi, Babak
Minas, James P.
Journal name Operations research for health care
Volume number 3
Issue number 3
Start page 168
End page 175
Total pages 8
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-09
ISSN 2211-6923
Keyword(s) intensive care unit
hospital readmission
dynamic programming
Summary In this study we explore a model to optimize the Intensive Care Unit (ICU) discharging decisions prior to service completion as a result of capacity-constrained situation under uncertainty. Discharging prior to service completion, which is called demand-driven discharge or premature discharging, increases the chance that a patient to be readmitted to the ICU in the near future. Since readmission imposes an additional load on ICUs, the cost of demand-driven discharge is pertained to the surge of readmission chance and the length of stay (LOS) in the ICU after readmission. Hence, the problem is how to select a current patient in the ICU for demand-driven discharge to accommodate a new critically ill patient. In essence, the problem is formulated as a stochastic dynamic programming model. However, even in the deterministic form i.e. knowing the arrival and treatment times in advance, solving the dynamic programming model is almost unaffordable for a sizable problem. This is illustrated by formulating the problem by an integer programming model. The uncertainties and difficulties in the problem are convincing reasons to use the optimization-simulation approach. Thus, using simulations, we evaluate various scenarios by considering Weibull distribution for the LOS. While it is known that selecting a patient with the lowest readmission risk is optimum under certain conditions and supposing a memory-less distribution for LOS; we remark that when LOS is non-memory-less, considering readmission risk and remaining LOS rather than just readmission risk leads to better results.
Language eng
DOI 10.1016/j.orhc.2014.06.001
Field of Research 089999 Information and Computing Sciences not elsewhere classified
080605 Decision Support and Group Support Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
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