The 85% bed occupancy fallacyCitation formats

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The 85% bed occupancy fallacy : The use, misuse and insights of queuing theory. / Proudlove, Nathan.

In: Health Services Management Research, Vol. 33, No. 3, 29.08.2020, p. 110-121.

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Proudlove, Nathan. / The 85% bed occupancy fallacy : The use, misuse and insights of queuing theory. In: Health Services Management Research. 2020 ; Vol. 33, No. 3. pp. 110-121.

Bibtex

@article{918fee88bd844e4ba088d6d662a0f4d5,
title = "The 85% bed occupancy fallacy: The use, misuse and insights of queuing theory",
abstract = "Queuing theory can, and has, been used to inform bed pool capacity decision making, though rarely by managers themselves. The insights it brings are also not widely and properly understood by healthcare managers. These two shortcomings lead to the persistent fallacy of there being a globally-applicable optimum average occupancy target, for example 85%, which can in turn lead to over- or under-provision of resources. Through this paper we aim both to make queuing models more accessible and to provide visual demonstrations of the general insights managers should absorb from queuing theory. Occupancy is a consequence of the patient arrival rate and {\textquoteleft}treatment{\textquoteright} rate (the number of beds and length of stay). There is a trade-off between the average occupancy and access to beds (measured by, for example, the risk of access block due to all beds being full or the average waiting time for a bed). Managerially, the decision making input should be the level of access to beds required, and so bed occupancy should be an output. Queuing models are useful to quickly draw the shape of these access-occupancy trade-off curves. Moreover, they can explicitly show the effect that variation (lack of regularity) in the times between arrivals and in the lengths of stay of individual patients has on the shape of the trade-off curves. In particular, with the same level of access, bed pools subject to lower variation can operate at higher average occupancy. Further, to improve access to a bed pool reducing variation should be considered. ",
keywords = "bed management, capacity modelling, healthcare management, queuing theory",
author = "Nathan Proudlove",
year = "2020",
month = aug,
day = "29",
doi = "10.1177/0951484819870936",
language = "English",
volume = "33",
pages = "110--121",
journal = "Health Services Management Research",
issn = "0951-4848",
publisher = "Sage Publications Ltd",
number = "3",

}

RIS

TY - JOUR

T1 - The 85% bed occupancy fallacy

T2 - The use, misuse and insights of queuing theory

AU - Proudlove, Nathan

PY - 2020/8/29

Y1 - 2020/8/29

N2 - Queuing theory can, and has, been used to inform bed pool capacity decision making, though rarely by managers themselves. The insights it brings are also not widely and properly understood by healthcare managers. These two shortcomings lead to the persistent fallacy of there being a globally-applicable optimum average occupancy target, for example 85%, which can in turn lead to over- or under-provision of resources. Through this paper we aim both to make queuing models more accessible and to provide visual demonstrations of the general insights managers should absorb from queuing theory. Occupancy is a consequence of the patient arrival rate and ‘treatment’ rate (the number of beds and length of stay). There is a trade-off between the average occupancy and access to beds (measured by, for example, the risk of access block due to all beds being full or the average waiting time for a bed). Managerially, the decision making input should be the level of access to beds required, and so bed occupancy should be an output. Queuing models are useful to quickly draw the shape of these access-occupancy trade-off curves. Moreover, they can explicitly show the effect that variation (lack of regularity) in the times between arrivals and in the lengths of stay of individual patients has on the shape of the trade-off curves. In particular, with the same level of access, bed pools subject to lower variation can operate at higher average occupancy. Further, to improve access to a bed pool reducing variation should be considered.

AB - Queuing theory can, and has, been used to inform bed pool capacity decision making, though rarely by managers themselves. The insights it brings are also not widely and properly understood by healthcare managers. These two shortcomings lead to the persistent fallacy of there being a globally-applicable optimum average occupancy target, for example 85%, which can in turn lead to over- or under-provision of resources. Through this paper we aim both to make queuing models more accessible and to provide visual demonstrations of the general insights managers should absorb from queuing theory. Occupancy is a consequence of the patient arrival rate and ‘treatment’ rate (the number of beds and length of stay). There is a trade-off between the average occupancy and access to beds (measured by, for example, the risk of access block due to all beds being full or the average waiting time for a bed). Managerially, the decision making input should be the level of access to beds required, and so bed occupancy should be an output. Queuing models are useful to quickly draw the shape of these access-occupancy trade-off curves. Moreover, they can explicitly show the effect that variation (lack of regularity) in the times between arrivals and in the lengths of stay of individual patients has on the shape of the trade-off curves. In particular, with the same level of access, bed pools subject to lower variation can operate at higher average occupancy. Further, to improve access to a bed pool reducing variation should be considered.

KW - bed management

KW - capacity modelling

KW - healthcare management

KW - queuing theory

U2 - 10.1177/0951484819870936

DO - 10.1177/0951484819870936

M3 - Article

VL - 33

SP - 110

EP - 121

JO - Health Services Management Research

JF - Health Services Management Research

SN - 0951-4848

IS - 3

ER -