Improving ambulance coverage in a mixed urban-rural region in Norway using mathematical modeling
| Autor: | Peter Fiskerstrand, Jørgen Einerkjær, Pieter L. van den Berg, Karen Aardal, Trond Thoresen, Jo Røislien |
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| Přispěvatelé: | Department of Technology and Operations Management, Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands |
| Rok vydání: | 2018 |
| Předmět: |
Rural Population
Emergency Medical Services Time Factors Urban Population Science Ambulances ambulanse Rural areas sykebil 03 medical and health sciences 0302 clinical medicine Backup Medisinske Fag: 700 [VDP] Statistics Range (statistics) Emergency medical services Urban Health Services Humans 030212 general & internal medicine Urban areas Mathematical models Travel Multidisciplinary Location model business.industry Norway Critical care and emergency medicine 030208 emergency & critical care medicine Workload Ranging Mathematical Concepts Models Theoretical Roads Environmental science Medicine Mathematical modeling pre-hospitale tjenester Rural Health Services Rural area business Rural population |
| Zdroj: | PLoS ONE, 14(4):e0215385. Public Library of Science PLoS ONE, 14(4) PLOS ONE e0215385 PLoS ONE, Vol 14, Iss 4, p e0215385 (2019) |
| ISSN: | 1932-6203 |
| Popis: | BackgroundAmbulance services play a crucial role in providing pre-hospital emergency care. In order to ensure quick responses, the location of the bases, and the distribution of available ambulances among these bases, should be optimized. In mixed urban-rural areas, this optimization typically involves a trade-off between backup coverage in high-demand urban areas and single coverage in rural low-demand areas. The aim of this study was to find the optimal distribution of bases and ambulances in the Vestfold region of Norway in order to optimize ambulance coverage.MethodThe optimal location of bases and distribution of ambulances was estimated using the Maximum Expected Covering Location Model. A wide range of parameter settings were fitted, with the number of ambulances ranging from 1 to 15, and an average ambulance utilization of 0, 15, 35 and 50%, corresponding to the empirical numbers for night, afternoon and day, respectively. We performed the analysis both conditioned on the current base structure, and in a fully greenfield scenario.ResultsFour of the five current bases are located close to the mathematical optimum, with the exception of the northernmost base, in the rural part of the region. Moving this base, along with minor changes to the location of the four other bases, coverage can be increased from 93.46% to 97.51%. While the location of the bases is insensitive to the workload of the system, the distribution of the ambulances is not. The northernmost base should only be used if enough ambulances are available, and this required minimum number increases significantly with increasing system workload.ConclusionAs the load of the system increases, focus of the model shifts from providing single coverage in low-demand areas to backup coverage in high-demand areas. The classification rule for urban and rural areas significantly affects results and must be evaluated accordingly. |
| Databáze: | OpenAIRE |
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