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Authors: Fraser L. Greenroyd 1 ; Rebecca Hayward 2 ; Andrew Price 3 ; Peter Demian 3 and Shrikant Sharma 2

Affiliations: 1 School of Civil and Building Engineering, Loughborough University and BuroHappold Engineering, United Kingdom ; 2 BuroHappold Engineering, United Kingdom ; 3 School of Civil and Building Engineering and Loughborough University, United Kingdom

ISBN: 978-989-758-265-3

Keyword(s): Hospital Operations, Patient Scheduling, Discrete-event Simulation, Healthcare Delivery, Outpatient Operations.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Process Management ; Domain-Specific Tools ; e-Business ; Enterprise Engineering ; Enterprise Information Systems ; Knowledge Management and Information Sharing ; Knowledge-Based Systems ; Logistics ; Simulation and Modeling ; Simulation Tools and Platforms ; Symbolic Systems

Abstract: As the National Health Service (NHS) of England continues to face tighter cost saving and utilisation government set targets, finding the optimum between costs, patient waiting times, utilisation of resources, and user satisfaction is increasingly challenging. Patient scheduling is a subject which has been extensively covered in the literature, with many previous studies offering solutions to optimise the patient schedule for a given metric. However, few analyse a large range of metrics pertinent to the NHS. The tool presented in this paper provides a discrete-event simulation tool for analysing a range of patient schedules across nine metrics, including: patient waiting, clinic room utilisation, waiting room utilisation, staff hub utilisation, clinician utilisation, patient facing time, clinic over-run, post-clinic waiting, and post-clinic patients still being examined. This allows clinic managers to analyse a number of scheduling solutions to find the optimum schedule for their depa rtment by comparing the metrics and selecting their preferred schedule. Also provided is an analysis of the impact of variations in appointment durations and their impact on how a simulation tool provides results. This analysis highlights the need for multiple simulation runs to reduce the impact of non-representative results from the final schedule analysis. (More)

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Paper citation in several formats:
Greenroyd F., Hayward R., Price A., Demian P. and Sharma S. (2017). Maximising Patient Throughput using Discrete-event Simulation.In Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-758-265-3, pages 204-214. DOI: 10.5220/0006400002040214

@conference{simultech17,
author={Fraser L. Greenroyd and Rebecca Hayward and Andrew Price and Peter Demian and Shrikant Sharma},
title={Maximising Patient Throughput using Discrete-event Simulation},
booktitle={Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2017},
pages={204-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006400002040214},
isbn={978-989-758-265-3},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Maximising Patient Throughput using Discrete-event Simulation
SN - 978-989-758-265-3
AU - Greenroyd F.
AU - Hayward R.
AU - Price A.
AU - Demian P.
AU - Sharma S.
PY - 2017
SP - 204
EP - 214
DO - 10.5220/0006400002040214

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