Modelling capacity along a patient pathway with delays to transfer and discharge

Wood, Richard M.; Murch, Ben J.

doi:10.1080/01605682.2019.1609885

Cited by 17 publications

(14 citation statements)

References 42 publications

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“…4 Simulation output results for no constraint to bed number availability. This shows the number of intensive care beds that would be required to satisfy all demand intensive care and the acute wards, within a pathway model similar to that of [16]).…”

Section: Further Researchmentioning

confidence: 99%

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COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

Wood

McWilliams

Thomas

et al. 2020

Health Care Manag Sci

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Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. In appreciating these 'capacity-dependent' deaths, this paper reports on the clinically-led development of a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients. With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity. Based on information available at the time, results suggest that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day. Accounting for the additional 'capacity-independent' deaths, which occur even when appropriate care is available within the intensive care setting, yields an aggregate reduction in total deaths of 30%. The modelling tool, which is freely available and open source, has since been used to support COVID-19 response planning at a number of healthcare systems within the

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Section: Further Researchmentioning

confidence: 99%

“…Computer simulations of patient flow, demand and capacity have been used extensively to inform decision-making in healthcare [13][14][15][16]. This is especially true for the stochastic, discrete-event approach to simulation, as it is particularly suited to situations where entities (e.g.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

Wood

McWilliams

Thomas

et al. 2020

Health Care Manag Sci

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“…An example for the application considered here would be the inability to discharge a patient from intensive care due to the lack of an available acute bed. While this has not been modelled (this would be possible at the cost of additional complexity, see Wood & Murch, 2019), the effects can be understood by adjusting the length of stay distribution used within the simulation according to estimated or hypothetical delay times. Finally, it is assumed in this study that all intensive care beds are available for newly-arriving COVID-19 patients.…”

Section: Discussionmentioning

confidence: 99%

“…Computer simulations of patient flow, demand and capacity have been used extensively to inform decision-making in healthcare (Fone et al, 2003, Griffiths et al, 2013, Mohiuddin et al, 2017, Wood & Murch, 2019). This is especially true for the stochastic, discrete-event approach to simulation, as it is particularly suited to situations where entities (e.g.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study

Wood

McWilliams

Bourdeaux

et al. 2020

Preprint

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Background. Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome. The aim of this study is to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving nonpharmaceutical interventions and supply-side measures to increase surge capacity or reduce length of stay. Methods.A stochastic discrete event simulation model is developed to represent the key dynamics of the intensive care admissions process for COVID-19 patients. Model inputs are aligned to levers available to planners with key outputs including duration of time at maximum capacity (to inform workforce requirements), peak daily deaths (for mortuary planning), and total deaths (as an ultimate marker of intervention efficacy). The model -freely available -is applied to the COVID-19 response at a large hospital in England for which the effect of a number of possible interventions are simulated.Results. Capacity-dependent deaths are closely associated with both the nature and effectiveness of non-pharmaceutical interventions and availability of intensive care beds. For the hospital considered, results suggest that capacity-dependent deaths can be reduced five-fold through a combination of isolation policies, a doubling of bed capacity, and 25% reduced length of stay. Conclusions.Without treatment or vaccination there is little that can be done to reduce deaths occurring when patients have otherwise been treated in the most appropriate hospital setting. Healthcare planners should therefore focus on minimising the capacity-dependent deaths that are within their influence.

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“…Ding et al [21] proposed a Markov chain based on container circulation model to analyze the relationship between container terminals. Wood et al [22] present a versatile model based on Markov chain to estimate capacity requirements along a patient pathway with delays to transfer and discharge.…”

Section: Introductionmentioning

confidence: 99%

Container Terminal Berth-Quay Crane Capacity Planning Based on Markov Chain

Jiang

Zheng

et al. 2021

PROMET

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This paper constructs a berth-quay crane capacity planning model with the lowest average daily cost in the container terminal, and analyzes the influence of the number of berths and quay cranes on the terminal operation. The object of berth-quay crane capacity planning is to optimize the number of berths and quay cranes to maximize the benefits of the container terminal. A steady state probability transfer model based on Markov chain for container terminal is constructed by the historical time series of the queuing process. The current minimum time operation principle (MTOP) strategy is proposed to correct the state transition probability of the Markov chain due to the characteristics of the quay crane movement to change the service capacity of a single berth. The solution error is reduced from 7.03% to 0.65% compared to the queuing theory without considering the quay crane movement, which provides a basis for the accurate solution of the berth-quay crane capacity planning model. The proposed berth-quay crane capacity planning model is validated by two container terminal examples, and the results show that the model can greatly guide the container terminal berth-quay crane planning.

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Modelling capacity along a patient pathway with delays to transfer and discharge

Cited by 17 publications

References 42 publications

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care

COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study

Container Terminal Berth-Quay Crane Capacity Planning Based on Markov Chain

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