Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Ambulance allocation for maximal survival with heterogeneous outcome measures

Knight, Vincent Anthony ORCID: https://orcid.org/0000-0002-4245-0638, Harper, Paul Robert ORCID: https://orcid.org/0000-0001-7894-4907 and Smith, Leanne 2012. Ambulance allocation for maximal survival with heterogeneous outcome measures. OMEGA -The International Journal of Management Science. 40 (6) , pp. 918-926. 10.1016/j.omega.2012.02.003

Full text not available from this repository.

Abstract

This paper proposes new models for locating emergency medical services (EMS) by incorporating survival functions for capturing multiple-classes of heterogeneous patients. The Maximal Expected Survival Location Model for Heterogeneous Patients (MESLMHP) aims to maximize the overall expected survival probability of multiple-classes of patients, whereby different classes could be defined according to agreed patient categories based on response time targets, or by capturing differing medical conditions each with a corresponding survival function. Furthermore, we propose and demonstrate an approximation approach to solving the extended stochastic version of MESLMHP, which utilizes queuing theory to permit the modeling of congestion and utilization at each ambulance station, and does not require assumptions to be made on the utilization of ambulances. Both models are demonstrated using data from the ambulance service in Wales. We show that our multiple outcome measures and survival-maximizing approach, rather than one based on average response time targets alone or a single patient class provides more effective EMS ambulanceallocations.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Uncontrolled Keywords: Ambulance allocation; Health care modeling; Survival function; Patient outcome; Covering model
Publisher: Elsevier
ISSN: 0305-0483
Last Modified: 19 Oct 2022 10:43
URI: https://orca.cardiff.ac.uk/id/eprint/25344

Citation Data

Cited 103 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item