Abstract:ResumoUma das maiores preocupações de sistemas urbanos de atendimento médico-emergencial é a rapidez no atendimento às vítimas. Os tempos de resposta dependem de diversos fatores como condições de tráfego local, dia da semana e período do dia, tipo e número de veículos disponíveis, localização destes veículos, políticas de despacho, etc. Neste trabalho apresenta-se uma análise dos efeitos da descentralização de ambulâncias que operam no sistema de atendimento médico-emergencial (SAMU-192) de Campinas, SP. O pr… Show more
“…A realocação das bases nos períodos da noite e madrugada parece ser suficiente para atingir os parâmetros sugeridos pela Organização Mundial da Saúde (OMS) no tempo de resposta, porém, durante o dia, são necessárias outras medidas para diminuir esse tempo 21 . A disponibilidade de recursos e tecnologia de localização espacial das unidades de atendimento possibilita identificar a ambulância mais próxima ao chamado e auxilia o motorista no deslocamento 22,23 .…”
Resumo
Estudo de caso para a avaliação do Serviço de Atendimento Móvel de Urgência (SAMU) em Santa Catarina, Brasil, período de 2013/2014 Este é um artigo publicado em acesso aberto (Open Access) sob a licença Creative Commons Attribution, que permite uso, distribuição e reprodução em qualquer meio, sem restrições, desde que o trabalho original seja corretamente citado.
“…A realocação das bases nos períodos da noite e madrugada parece ser suficiente para atingir os parâmetros sugeridos pela Organização Mundial da Saúde (OMS) no tempo de resposta, porém, durante o dia, são necessárias outras medidas para diminuir esse tempo 21 . A disponibilidade de recursos e tecnologia de localização espacial das unidades de atendimento possibilita identificar a ambulância mais próxima ao chamado e auxilia o motorista no deslocamento 22,23 .…”
Resumo
Estudo de caso para a avaliação do Serviço de Atendimento Móvel de Urgência (SAMU) em Santa Catarina, Brasil, período de 2013/2014 Este é um artigo publicado em acesso aberto (Open Access) sob a licença Creative Commons Attribution, que permite uso, distribuição e reprodução em qualquer meio, sem restrições, desde que o trabalho original seja corretamente citado.
“…Firstly, we can model and solve many models with a randomly created dispatch preference lists for each solution, as seen in Takeda et al (2004). By the end of the solutions, a mean value is calculated to find the final probabilities for each state.…”
Section: Dispatch Randomnessmentioning
confidence: 99%
“…Only in some cases, less than 4%, there was the dispatch of more than one vehicle to the scene. Moreover, ASV answer only red requests, which does not comply with the hypercube model hypothesis, but the workload over the system is low, allowing a relaxation of this hypothesis as seen in Takeda et al (2004Takeda et al ( , 2007. The system allows the formation of waiting lines and there is no limit for its size, wherein requests are organized following their priority.…”
Section: Simple Server Dispatchmentioning
confidence: 99%
“…The system is based on the French model, operating for more than 30 years (Takeda et al, 2004). It works 24/7; the crews have physicians, nurses and nursing assistants.…”
Section: Introductionmentioning
confidence: 99%
“…(Larson & Odoni, 2007). The hypercube queueing model was used on several papers in Brazil, as Chiyoshi et al (2000), Iannoni et al (2009Iannoni et al ( , 2015, Iannoni & Morabito (2006, 2008, Souza et al (2013Souza et al ( , 2014Souza et al ( , 2015, Takeda et al (2004Takeda et al ( , 2007, Rodrigues (2014). In other parts of the globe, many other papers emerge as Chelst & Barlach (1981), Brandeau & Larson (1986), Burwell et al (1993), Sacks & Grief (1994), Swersey (1994) and Larson & Odoni (2007).…”
Resumo: O estudo de Sistemas de Atendimento Emergencial -SAE visa encontrar meios de fornecer serviços de
Abstract:The study of EMS aims to find ways to provide effective health services and improve the quality of life of the population while respecting the limitations of available resources. In this context, this paper aims to show the potential of application of the hypercube queueing model using queue priorities with more than one preferential server without using partial backup on SAMU, where the workload is relatively low. To do so, were done some experiments with the hypercube queueing model and future scenario prospection by a case study on the SAMU system from Bauru, Brazil. It was evaluated the impacts of demand increase over the system and the acquisition of a new ambulance was evaluated considering the best options to locate it. Main results show that a 50% demand increase can double mean response times. In contrast, minor increases have a smaller impact over the system, as observed on 5.71% and 13.57% demand increases, where the mean response times raised 5% and 16% respectively. The acquisition of a new ambulance was evaluated in terms of mean response times also. The best location had a 3% lower mean response time, on average. Financial support: CAPES and FAPESP.
Rail operators worldwide are seeing an increase in demand for services, driven by expanding global trade and the use of export commodities. Punctual service in a train stopped on the railroad is extremely important since the delay in service significantly impacts performance, asset utilization, and railroad productivity. The present work addresses the development and the solution of an optimization model for locating railway service teams that serve locomotives that need emergency maintenance on a Brazilian railway. Additionally, a hypercube model was solved to estimate several performance measures that assist in decision‐making. From the result analysis of different scenarios, it was found that the optimized maintenance teams' location provides superior results to the current ones considering the same resources. Also, it was possible to increase the coverage with double attendance in the studied system, monitoring different indicators such as attendance teams' average system travel time and workload.
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