OPTIMIZATION MODEL FOR THE INSTALLATION OF SAMU BASES: APPLICATION IN NATAL-RN

Authors

DOI:

https://doi.org/10.15675/gepros.v15i4.2668

Keywords:

Emergency medical service, health care, model simulation

Abstract

Purpose – The objective of this study was the application of a mathematical model aiming to designate neighborhoods to install new Mobile Emergency Care Service (SAMU) bases to minimize the distance traveled by ambulances in the city of Natal / RN.

Design/methodology/approach –The data were grouped in order to obtain parameters, such as: call district, time, day of the week, number of accidents. After data collection and processing, a matrix of neighborhood-to-neighborhood distances in the city of Natal based on Google Maps was created. A model was created to minimize the distance traveled by ambulances with the aid of the AIMMS program.

Findings – The application of the model allowed for the simulation of scenarios with the installation of 3 to 8 fixed bases. There was a significant reduction in the distance traveled by the ambulances, which reached 48%, after the installation of eight bases. In other words, there was a reduction of 6,560 kilometers traveled per month by ambulances.  

Research, Practical & Social implications – The reduction in the total distance covered by the ambulances has practical and social implications, since it provides an increase in the number of ambulances available to serve the population and directly reflects in the reduction in the average response time of the service.

Originality/value The article contributes to the debate on efficiency in Brazilian medical emergency services by proposing engineering and management solutions for monitoring critical indicators such as response time.

Keywords - Emergency medical service. Health care. Model simulation

Author Biography

Eric Lucas dos Santos Cabral, Universidade Federal do Rio Grande do Norte


References

ARINGHIERI, R.; CARELLO, G.; MORALE, D. Supporting decision making to improve the performance of an Italian Emergency Medical Service. Annals of Operations Research, v. 236, n. 1, p. 131–148, 2016. DOI: https://doi.org/10.1007/s10479-013-1487-0

BAHRAMI, M. A. et al. Pre-Hospital Emergency Medical Services in Developing Countries: A Case Study about EMS Response Time in Yazd, Iran. Iranian Red Crescent Medical Journal, v. 13, n. 10, p. 735–738, 2011.

BROTCORNE, L.; LAPORTE, G.; SEMET, F. Ambulance location and relocation models. European Journal of Operational Research, v. 147, n. 3, p. 451–463, 2003. DOI: https://doi.org/10.1016/S0377-2217(02)00364-8

CHURCH, R. L.; DAVIS, R. R. The fixed charge maximal covering location problem. Papers in Regional Science, v. 71, n. 3, p. 199–215, 1992. DOI: https://doi.org/10.1007/BF01434264

COSTA, L. P. DA; MORAIS, I. R. D. Espaço, iniquidade e transporte público: avaliação da acessibilidade urbana na cidade de Natal/RN por meio de indicadores de sustentabilidade. Sociedade & Natureza, v. 26, n. 2, p. 237–251, 2014. DOI: https://doi.org/10.1590/1982-451320140203

CABRAL E.L.S. et al. Response Time in Emergency Medical. Systematic Review. Acta Cirúrgica Brasileira, v.33, p. 1111-1121,2018. DOI: https://doi.org/10.1590/s0102-865020180120000009

DASKIN, M.; STERN, E. A Hierarchical Objective Set Covering Model for Emergency Medical Service Vehicle Deployment. Transportation Science, v. 15, n. May, 1981. DOI: https://doi.org/10.1287/trsc.15.2.137

FRAZÃO, T.et al. Multicriteria decision analysis (MCDA) in health care: a systematic review of the main characteristics and methodological steps. BMC Medical Informatics and Decision Making, v. 1, p. 1–16, 2018. DOI: https://doi.org/10.1186/s12911-018-0663-1

GENDREAU, M.; LAPORTE, G.; SEMET, F. Solving an ambulance location model by tabu search. Location Science, v. 5, n. 2, p. 75–88, 1997. DOI: https://doi.org/10.1016/S0966-8349(97)00015-6

HILLIER, F.; LIEBERMAN, G. Introdução a Pesquisa Operacional. 8. ed. Porto Alegre: McGraw-Hill, 2010.

IGNÁCIO, A. A. V.; FERREIRA FILHO, V. J. M. O uso de software de modelagem AIMMS na solução de problemas de programação matemática. Pesquisa Operacional, v. 24, n. 1987, p. 197–210, 2004. DOI: https://doi.org/10.1590/S0101-74382004000100011

KIM, S. H.; LEE, Y. H. Iterative optimization algorithm with parameter estimation for the ambulance location problem. Health Care Management Science, p. 1–21, 2015.

MALTA, D. C. et al. Lesões no trânsito e uso de equipamento de proteção na população brasileira, segundo estudo de base populacional. Ciência & Saúde Coletiva, v. 21, n. 2, p. 399–410, 2016. DOI: https://doi.org/10.1590/1413-81232015212.23742015

MESSIAS, K. L. M. et al. Qualidade da informação dos óbitos por causas externas em Fortaleza, Ceará, Brasil. Ciência & Saúde Coletiva, v. 21, n. 4, p. 1255–1267, 2016. DOI: https://doi.org/10.1590/1413-81232015214.07922015

MINISTÉRIO DA SAÚDE. Diretrizes para a implantação do Serviço de Atendimento Móvel de Urgência (SAMU 192). [s.l: s.n.]. Disponível em: <http://bvsms.saude.gov.br/bvs/saudelegis/gm/2012/prt1010_21_05_2012.html>.

NOGUEIRA JUNIOR, L. C. Um estudo para redução do tempo de resposta do SAMU de Belo Horizonte através da realocação das bases de operação. p. 75, 2011.

PAHO, O. P. A. H. Situación de salud en las américas: Indicadores Básicos 2009. p. 12, 2009.

REICHENHEIM, M. E. et al. Violence and injuries in Brazil: The effect, progress made, and challenges ahead. The Lancet, v. 377, n. 9781, p. 1962–1975, 2011. DOI: https://doi.org/10.1016/S0140-6736(11)60053-6

SHARIAT-MOHAYMANY, A. et al. Linear upper-bound unavailability set covering models for locating ambulances: Application to Tehran rural roads. European Journal of Operational Research, v. 221, n. 1, p. 263–272, 2012. DOI: https://doi.org/10.1016/j.ejor.2012.03.015

SOUZA, R. M. DE et al. Análise da configuração de SAMU utilizando múltiplas alternativas de localização de ambulâncias. Gest. Prod., São Carlos, v. 20, p. 287–302, 2013. DOI: https://doi.org/10.1590/S0104-530X2013000200004

TAKEDA, R. A.; WIDMER, J. A.; MORABITO, R. Analysis of ambulance decentralization in an urban emergency medical service using the hypercube queueing model. Computers and Operations Research, v. 34, p. 727–741, 2007. DOI: https://doi.org/10.1016/j.cor.2005.03.022

WANKHADE, P. Performance measurement and the UK emergency ambulance service: Unintended consequences of the ambulance response time targets. International Journal of Public Sector Management, v. 24, n. 5, p. 384–402, 2011. DOI: https://doi.org/10.1108/09513551111147132

Downloads

Additional Files

Published

2020-11-24

How to Cite

Cabral, E. L. dos S., Castro, W. R. S., Francisco, C. A. C., & Souza, R. P. de. (2020). OPTIMIZATION MODEL FOR THE INSTALLATION OF SAMU BASES: APPLICATION IN NATAL-RN. Revista Gestão Da Produção Operações E Sistemas, 15(4), 205. https://doi.org/10.15675/gepros.v15i4.2668

Issue

Section

Articles

Similar Articles

You may also start an advanced similarity search for this article.