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Symbiotic simulation for the operational management of inpatient beds: Model development and validation using Δ-method

Symbiotic simulation for the operational management of inpatient beds: Model development and validation using Δ-method
Symbiotic simulation for the operational management of inpatient beds: Model development and validation using Δ-method
In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This paper shows how symbiotic simulation can help hospitals with important short-term operational decision making. We demonstrate how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. Further, we propose a new Δ-Method that is suitable for validating a stochastic symbiotic simulation model. We demonstrate the benefit of our symbiotic simulation by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census.
1386-9620
Oakley, David
0c52bf69-4ba1-4daf-8f7a-0ad3db489fb2
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Worthington, David
ab73db70-8d63-4991-b743-c0c2f8ca6c47
Oakley, David
0c52bf69-4ba1-4daf-8f7a-0ad3db489fb2
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Worthington, David
ab73db70-8d63-4991-b743-c0c2f8ca6c47

Oakley, David, Onggo, Bhakti Stephan and Worthington, David (2019) Symbiotic simulation for the operational management of inpatient beds: Model development and validation using Δ-method. Health Care Management Science. (doi:10.1007/s10729-019-09485-1).

Record type: Article

Abstract

In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This paper shows how symbiotic simulation can help hospitals with important short-term operational decision making. We demonstrate how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. Further, we propose a new Δ-Method that is suitable for validating a stochastic symbiotic simulation model. We demonstrate the benefit of our symbiotic simulation by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census.

Text
Symbiotic Simulation Inpatient Beds HCMS_Full_revised - Accepted Manuscript
Restricted to Repository staff only until 29 April 2020.
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More information

Accepted/In Press date: 29 April 2019
e-pub ahead of print date: 3 June 2019

Identifiers

Local EPrints ID: 430631
URI: https://eprints.soton.ac.uk/id/eprint/430631
ISSN: 1386-9620
PURE UUID: e0c36e62-92fd-43cc-b017-69d933ebd3fb
ORCID for Bhakti Stephan Onggo: ORCID iD orcid.org/0000-0001-5899-304X

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Date deposited: 07 May 2019 16:30
Last modified: 10 Sep 2019 00:21

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