Using Hospital Admission Predictions at Triage for Improving Patient Length of Stay in Emergency Departments

Wanyi Chen (), Nilay Tanik Argon (), Tommy Bohrmann (), Benjamin Linthicum (), Kenneth Lopiano (), Abhishek Mehrotra (), Debbie Travers () and Serhan Ziya ()
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Wanyi Chen: Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02140
Nilay Tanik Argon: Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina 27599
Tommy Bohrmann: Analytical Partners Consulting LLC, Research Triangle Park, North Carolina 27709
Benjamin Linthicum: School of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599
Kenneth Lopiano: Roundtable Analytics, Inc., Research Triangle Park, North Carolina 27709
Abhishek Mehrotra: School of Medicine, University of North Carolina, Chapel Hill, North Carolina 27599
Debbie Travers: School of Nursing, Duke University, Durham, North Carolina 27710
Serhan Ziya: Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina 27599

Operations Research, 2023, vol. 71, issue 5, 1733-1755

Abstract: Long boarding times have long been recognized as one of the main reasons behind emergency department (ED) crowding. One of the suggestions made in the literature to reduce boarding times was to predict, at the time of triage, whether a patient will eventually be admitted to the hospital and if the prediction turns out to be “admit,” start preparations for the patient’s transfer to the main hospital early in the ED visit. However, there has been no systematic effort in developing a method to help determine whether an estimate for the probability of admit would be considered high enough to request a bed early, whether this determination should depend on ED census, and what the potential benefits of adopting such a policy would be. This paper aims to help fill this gap. The methodology we propose estimates hospital admission probabilities using standard logistic regression techniques. To determine whether a given probability of admission is high enough to qualify a bed request early, we develop and analyze two mathematical decision models. Both models are simplified representations and thus, do not lead to directly implementable policies. However, building on the solutions to these simple models, we propose two policies that can be used in practice. Then, using data from an academic hospital ED in the southeastern United States, we develop a simulation model, investigate the potential benefits of adopting the two policies, and compare their performances with that under a simple benchmark policy. We find that both policies can bring modest to substantial benefits, with the state-dependent policy outperforming the state-independent one particularly under conditions when the ED experiences more than usual levels of patient demand.

Keywords: Policy Modeling and Public Sector OR; patient flow; healthcare operations; queueing; Markov decision processes (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:71:y:2023:i:5:p:1733-1755

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