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Locating Helicopter Emergency Medical Service Bases to Optimise Population Coverage Versus Average Response Time Alan A

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Locating Helicopter Emergency Medical Service Bases to Optimise Population Coverage Versus Average Response Time Alan A Garner and van den Berg BMC Emergency Medicine (2017) 17:31 DOI 10.1186/s12873-017-0142-5 RESEARCH ARTICLE Open Access Locating helicopter emergency medical service bases to optimise population coverage versus average response time Alan A. Garner1* and Pieter L. van den Berg2 Abstract Background: New South Wales (NSW), Australia has a network of multirole retrieval physician staffed helicopter emergency medical services (HEMS) with seven bases servicing a jurisdiction with population concentrated along the eastern seaboard. The aim of this study was to estimate optimal HEMS base locations within NSW using advanced mathematical modelling techniques. Methods: We used high resolution census population data for NSW from 2011 which divides the state into areas containing 200–800 people. Optimal HEMS base locations were estimated using the maximal covering location problem facility location optimization model and the average response time model, exploring the number of bases needed to cover various fractions of the population for a 45 min response time threshold or minimizing the overall average response time to all persons, both in green field scenarios and conditioning on the current base structure. We also developed a hybrid mathematical model where average response time was optimised based on minimum population coverage thresholds. Results: Seven bases could cover 98% of the population within 45mins when optimised for coverage or reach the entire population of the state within an average of 21mins if optimised for response time. Given the existing bases, adding two bases could either increase the 45 min coverage from 91% to 97% or decrease the average response time from 21mins to 19mins. Adding a single specialist prehospital rapid response HEMS to the area of greatest population concentration decreased the average state wide response time by 4mins. The optimum seven base hybrid model that was able to cover 97.75% of the population within 45mins, and all of the population in an average response time of 18 mins included the rapid response HEMS model. Conclusions: HEMS base locations can be optimised based on either percentage of the population covered, or average response time to the entire population. We have also demonstrated a hybrid technique that optimizes response time for a given number of bases and minimum defined threshold of population coverage. Addition of specialized rapid response HEMS services to a system of multirole retrieval HEMS may reduce overall average response times by improving access in large urban areas. Keywords: Helicopter, Bases, Modelling, Optimization, Coverage, Response, Population * Correspondence: [email protected] 1CareFlight, Northmead, NSW, Australia Full list of author information is available at the end of the article © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Garner and van den Berg BMC Emergency Medicine (2017) 17:31 Page 2 of 11 Background HEMS type service in the large urban area of Sydney, Helicopter Emergency Medical Services (HEMS) form Australia when superimposed upon a system otherwise an important component of the prehospital care system exclusively operating MR-HEMS. in many developed jurisdictions. HEMS can deliver We therefore explore the mathematically optimal loca- advanced medical care to the scene of an incident, rap- tions of MR-HEMS bases in NSW comparing the MCLP idly transport patients to specialist centres or provide a and ARTM and a proposed hybrid model. Using detailed rescue capability that may not be possible or timely by population density data for the whole of NSW, we fit surface vehicle. HEMS generally provide a higher level MCLP and ARTM to explore optimal base structures. of clinical care than is available by road EMS systems as We performed both “green field” analyses, assuming a helicopters enable a single, more highly trained team to hypothetical situation with no current MR-HEMS bases, cover a large geographical area. HEMS are also expen- and optimisation conditioned on the current bases, in sive resources that require optimal base locations to order to explore whether improvements to the existing maximise both clinical effectiveness and operational effi- base structure could be achieved by moving or adding a ciency. A mathematical model such as the Maximal few select bases. We also explore the effects of a Covering Location Problem (MCLP) [1] can be utilised specialist E-HEMS type service when overlayed on the to determine both the optimum location of a defined MR-HEMS network in NSW. number of bases to produce the highest percentage of population coverage, or the minimum number of bases Methods that would be required to meet a defined threshold per- Setting centage. The MCLP model has found a wide range of NSW currently has a system of nine MR-HEMS services applications including in health sciences and emergency operating from seven bases 24 h a day to cover a services [2, 3]. It is also possible to optimise base loca- population distributed over more than 809,000 km2. The tions based on other criteria such as minimising average population is however mostly urban and coastal, being response time to the entire population using an Average concentrated in Sydney and Newcastle. The Australian Response Time Model (ARTM) which may produce dif- Capital Territory (ACT) is not administratively part of ferent optimal base locations to the MCLP model. This NSW, but NSW Government funds an ACT based model was introduced by ReVelle [4] as the p-median MR-HEMS to provide services to NSW residents in problem and was, for example, applied to emergency the south east of the State. Hence, the ACT based services by Dzator [5]. Using the State of New South MR-HEMS functionally forms part of the NSW Wales, Australia as an example this study seeks to math- retrieval system and is part of the seven base net- ematically determine optimum HEMS base locations work. In addition, a specialist rapid response prehos- comparing models optimised for population coverage pital helicopter service (E-HEMS model as typically versus optimisation to minimise average response times, utilised in densely populated European countries such as well as exploring a hybrid optimisation model. as Germany and the Netherlands) operates from a An additional element that we sought to explore was hospital site near to the demographic centre of Sydney the effect of HEMS configurations on optimisation (CareFlight Rapid Response Helicopter - CRRH). This models. In many high density European countries HEMS service is not part of the MR-HEMS network and does (E-HEMS) roles are specialised with separate services not perform interhospital transfers or hoist rescue, provid- conducting scene response, interfacility transfer and ing a dedicated prehospital service only operating exclu- search, rescue and hoisting (SAR) roles. In jurisdictions sively in the area up to 60 nm (111 km) of its base near such as Australia however there are a small number of the demographic centre of Sydney. The area covered by large urban areas but population density is otherwise the CRRH includes 70% of the total NSW population. It very low with large transport distances. The Australian operates 12 h a day year round (rather than daylight hours HEMS model utilises multirole retrieval HEMS (MR- specifically) due to funding limitations. Both MR-HEMS HEMS) services that have a broad scope of operations and the CRRH are tasked by a central dispatch system including offshore and mountain hoist rescue, specia- operated by NSW Ambulance [7] utilising the same lised interfacility transports such as ECMO, IABP and criteria. Road ambulances are routinely dispatched in neonatal transfers as well as scene response. Although parallel to all HEMS responses. previous studies have demonstrated significantly faster The most recent publicly available data from 2011 response times in Sydney for an E-HEMS type service indicates that there were 3339 pre-hospital and inter- [6], the dispatch system currently does not recognise the hospital HEMS missions across NSW including those difference in response capabilities between this and the performed by the ACT and CRRH services [8]. In 2015, MR-HEMS [7]. A secondary objective of this study was there were 3970 significant trauma patients (Injury therefore to model the response time effect of an E- Severity Score greater than twelve) in NSW, with 28.9% Garner and van den Berg BMC Emergency Medicine (2017) 17:31 Page 3 of 11 of injuries sustained in rural areas [9]. Across NSW, 230 persons per square kilometre is not financially 15.4% of all severe trauma cases were transported by achievable in a jurisdiction with the population density helicopter, but in rural areas this was 39.1% of cases. of NSW which has nine persons per square kilometre. The Norwegian target of 45mins was therefore consid- Data sources ered an appropriate international benchmark for The most recent Australian census from which detailed comparison with NSW as it is both a high income juris- population data is available was conducted in 2011 when diction and has very similar population density. the population of NSW was 6.9 million people. The finest resolution of data available from the census is Modelling methods Statistical Area 1 (SA1) level data [10]. SA1s generally Optimal base locations were determined by approaching have a population of 200 to 800 persons, and an average the question as an MCLP or by the ART model. The population of about 400 persons.
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