Time-dependent stochastic methods for managing and scheduling Emergency Medical Services

Vile, Julie Leanne, Gillard, Jonathan William ORCID: https://orcid.org/0000-0001-9166-298X, Harper, Paul Robert ORCID: https://orcid.org/0000-0001-7894-4907 and Knight, Vincent Anthony ORCID: https://orcid.org/0000-0002-4245-0638 2016. Time-dependent stochastic methods for managing and scheduling Emergency Medical Services. Operations Research for Health Care 8 , pp. 42-52. 10.1016/j.orhc.2015.07.002

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Abstract

Emergency Medical Services (EMS) are facing increasing pressures in many nations given that demands on the service are rising. This article focuses in particular on the operations of the Welsh Ambulance Service Trust (WAST), which is the only organisation that provides urgent paramedical care services on a day-to-day basis across the whole of Wales. In response to WAST’s aspiration to improve the quality of care it provides, this research investigates several interrelated advanced statistical and operational research (OR) methods, culminating in a suite of decision support tools to aid WAST with capacity planning issues. The developed techniques are integrated in a master workforce capacity planning tool that may be independently operated by WAST planners. By means of incorporating methods that seek to simultaneously better predict future demands, recommend minimum staffing requirements and generate low-cost rosters, the tool ultimately provides planners with an analytical base to effectively deploy resources. Whilst the tool is primarily developed for WAST, the generic nature of the methods considered means they could equally be applied to any service subject to demand that is of an urgent nature, cannot be backlogged, is heavily time-dependent and highly variable

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Mathematics
Subjects:	Q Science > QA Mathematics
Publisher:	Elsevier
ISSN:	2211-6923
Funders:	EPSRC
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	18 July 2015
Last Modified:	20 Nov 2024 07:15
URI:	https://orca.cardiff.ac.uk/id/eprint/76147

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