SIMULATION MODELLING OF DELAYED DISCHARGE FROM HOSPITAL

Delayed discharge happens when patients remain in hospital despite being medically fit for discharge. This situation negatively impacts patients for example through loss of mobility and independence, and creates operational difficulties for hospitals, such as reduced bed availability. This study presents a discrete event simulation model of the hospital discharge process in NHS England, where patients are discharged to one of four pathways. The model captures the proportion of delayed patients who are considered to be difficult to place in onwards care. The simulation model is parameterised using published NHS England data. Two of the parameters were not known from the data and an optimisation approach is presented to estimate their value. We describe the model and computational experiments applied to five large NHS Foundation Trusts in the South East region of England.

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Publication Details

Published
2025-03-20
DOI
https://doi.org/10.36819/sw25.040
Primary Topic
Quality and Safety in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

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article

SIMULATION MODELLING OF DELAYED DISCHARGE FROM HOSPITAL

Carlos Lamas‐Fernandez, Christine Currie, Laura Boyle, Ranga Abeysooriya et al.
Quality and Safety in Healthcare
article

SIMULATION MODELLING OF DELAYED DISCHARGE FROM HOSPITAL

Carlos Lamas‐Fernandez, Christine Currie, Laura Boyle, Ranga Abeysooriya, Nirmani Amarasinghe
article en

Abstract

Delayed discharge happens when patients remain in hospital despite being medically fit for discharge. This situation negatively impacts patients for example through loss of mobility and independence, and creates operational difficulties for hospitals, such as reduced bed availability. This study presents a discrete event simulation model of the hospital discharge process in NHS England, where patients are discharged to one of four pathways. The model captures the proportion of delayed patients who are considered to be difficult to place in onwards care. The simulation model is parameterised using published NHS England data. Two of the parameters were not known from the data and an optimisation approach is presented to estimate their value. We describe the model and computational experiments applied to five large NHS Foundation Trusts in the South East region of England.

National Institute for Health and Care Research, Department of Health and Social Care
Openalex Percentile: Top 90%
Quality and Safety in Healthcare
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