Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers

Enayati, Shakiba; Mayorga, Marı́a E.; Rajagopalan, Hari; Saydam, Cem

doi:10.1016/j.omega.2017.08.001

Cited by 51 publications

(19 citation statements)

References 35 publications

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“…In contrast to visits for nonurgent conditions, which mostly occur at hospital emergency departments 19 , the number of ambulance emergency calls may be attributed to complicated conditions including delayed arrival times, restricted service times, and hard-to-reach locations. Moreover, a call is considered to be covered if it is responded to within a predefined standard time of 10 minutes in an urban area 20 . Most citizens use public ambulance systems as their initial entry points for receiving urgent care 21 .…”

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confidence: 99%

Ambulance Services Associated with Extreme Temperatures and Fine Particles in a Subtropical Island

Lin

Chen

Hung

et al. 2020

Sci Rep

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this study evaluated the association between the risk of events requiring ambulance services and the ambient temperature and particulate matter of 2.5 μm (pM 2.5 ) and 10 μm (pM 10 ) for populations living in subtropical Taiwan. We used a distributed lag nonlinear model with a quasi-Poisson function to assess the roles of ambient temperature, PM 10 and pM 2.5 in the use of ambulance services for respiratory distress, coma and unconsciousness, chest pain, lying down in public, headaches/dizziness/vertigo/ fainting/syncope and out-of-hospital cardiac arrest (OHCA). The relative risk (RR) and 95% confidence interval (CI) of each specific event were calculated in association with the ambient conditions. In general, the events that required ambulance services had a V-shaped or J-shaped association with the temperature, where the risks were higher at extreme temperatures. The RR of each event was significant when the patients were exposed to temperatures in the 5 th percentile (<15 °C); patients with OHCA had the highest adjusted RR of 1.61 (95% CI = 1.47-1.77). The risks were also significant for coma/ unconsciousness, headaches/dizziness/vertigo/fainting/syncope, and OHCA but not for respiratory distress, chest pain and lying down in public, after exposure to the 99 th percentile temperatures of >30 °C. The risks for use of ambulance services increased with PM exposure and were significant for events of respiratory distress, chest pain and OHCA after exposure to the 99 th percentile pM 2.5 after controlling for temperatures. Events requiring ambulance services were more likely to occur when the ambient temperature was low than when it was high for the population on the subtropical island of Taiwan. The association of the risk of events requiring ambulance services with PM were not as strong as the association with low temperatures.The increased frequency and intensity of extreme climate events are important public health concerns 1 . Studies have reported that ambient temperature and air pollution are important factors with significant impacts associated with various morbidities and mortalities [2][3][4] . The associations between mortality and the temperature have been characterized by U-shaped, V-shaped and J-shaped curves 5,6 , with mortality increasing at extremely cold and/or extremely hot temperatures 7,8 . Studies have also found that the risk of emergency room visits for out-of-hospital cardiac arrest (OHCA) was greater in cold seasons than in hot seasons 9-11 . In Taiwan, the cumulative 6-day relative risk of emergency room visits for OHCA reaches 1.73 when the mean temperature is 14 °C in comparison to when it is >27 °C after controlling for air pollution 12 .Climate conditions have important impacts on the transport and dispersion of air pollutants. The role of pollutants in health impacts may thus vary with climate conditions 13 . However, studies may emphasize the impacts of air pollution rather than temperature on health, especially for respiratory diseases. One study that used the Danish Diet, Cancer, ...

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confidence: 99%

Ambulance Services Associated with Extreme Temperatures and Fine Particles in a Subtropical Island

Lin

Chen

Hung

et al. 2020

Sci Rep

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“…From these four phases, only phase 3 (going to hospital) makes an ambulance not eligible for the assignation process. Priority rules that can include a preparedness measure of the system and the type of emergency associated with the incoming call can be found in [83,84]. Likewise, Jagtenberg et al [85] and Park and Lee [86] use dispatching rules based on a Markov decision process, in which at each states, an action from the set of allowed actions must be followed.…”

Section: Dispatching Policiesmentioning

confidence: 99%

“…However, current computational technology, information technology, and geographic information systems motivate a posteriori type tools development. Several authors (Yue et al [134], Majzoubi et al [135], Mason [83], Billhardt et al [101], Jagtenberg et al [65], Lam et al [105], Van Barneveld, [106,109]; Enayati et al [110], Sudtachat et al [67], Yoon and Albert [136], Roa et al [75], Sun et al [137], Yuangyai et al [138], Nilsang et al [139], van Buuren et al [140], Tsai et al [89]) worked on real-time response tools, which, in the current context, are selfevident as a promising research development line in the future of these tools.…”

Section: Modeling Issuesmentioning

confidence: 99%

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

Neira-Rodado

Escobar

McClean³

2022

IJGI

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In this paper, an analytic review of the recent methodologies tackling the problem of dynamic allocation of ambulances was carried out. Considering that state-of-the-art is moving to deal with more extensive and dynamic problems to address in a better way real-life instances, this research looks to identify the evolution and recent applications of this kind of problem once the basic models are explored. This extensive review allowed us to identify the most recent developments in this problem and the most critical gaps to be addressed. In this sense, it is essential to point out that the dynamic location of emergency medical services (EMS) is nowadays a relevant topic considering its impact on the healthcare system outcomes. Issues related to forecasting, simulation, heterogeneous fleets, robustness, and solution speed for real-life problems, stand out in the identified gaps. Applications of machine learning the deployment challenges during epidemic outbreaks such as SARS and COVID-19 were also explored. At the same time, a proposed notation tries to tackle the fact that the word problem in this kind of work refers to a model on many occasions. The proposed notation eases the comparison between the different model proposals found in the literature.

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“…The results of this study suggests that dynamic approaches, although more expensive, dominate static ones. A real-time approach for maxi-mizing coverage with minimum possible total travel time is proposed by Enayati et al [32]. They consider accumulated workload restrictions for personnel in a shift.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimization Model for Emergency Vehicle Location and Relocation with Consideration of Unavailability Time

2017

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The main purpose of emergency medical services is providing fast medical care, as well as transporting patients to the hospital, in the shortest possible time. Healthcare managers try to improve healthcare systems through reducing the response to demand time. In this paper, we seek to propose an optimization model in order to cover as much demand as possible in the shortest possible time using the available ambulance fleet. To do so, considering the response and service time, amount of demand during the time periods, limitation in the number of available emergency vehicles and the capacity of ambulance stations, we have proposed a mixed integer linear programming optimization model, aiming to minimize the total response time. In this paper we take into account fleet relocation and unavailability time, the time interval in which the vehicle is on its way or doing a service at a demand point. Then, a sensitivity analysis is conducted on the model by manipulating the parameters, so as to observe the effects on the outputs. In order to evaluate the model, several artificial test problems were generated and solved. The results depict the capability of the proposed model in dealing with emergency cases.

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Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers

Cited by 51 publications

References 35 publications

Ambulance Services Associated with Extreme Temperatures and Fine Particles in a Subtropical Island

Ambulance Services Associated with Extreme Temperatures and Fine Particles in a Subtropical Island

Ambulances Deployment Problems: Categorization, Evolution and Dynamic Problems Review

An Optimization Model for Emergency Vehicle Location and Relocation with Consideration of Unavailability Time

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