EMS call volume predictions: A comparative study

Setzler, Hubert H.; Saydam, Cem; Park, Sungjune

doi:10.1016/j.cor.2008.05.010

Cited by 115 publications

(86 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This d j should be a measure for the number of calls within demand point j. See, for example, Channouf et al [23], and Setzler et al [24] for EMS call volume forecasting methods. Each base location has a capacity b i , which is the maximum number of ambulances that may be located at that station.…”

Section: Model Formulationmentioning

confidence: 99%

Linear formulation for the Maximum Expected Coverage Location Model with fractional coverage

Berg

Kommer

Zuzáková

2016

Operations Research for Health Care

View full text Add to dashboard Cite

a b s t r a c tSince ambulance providers are responsible for life-saving medical care at the scene in emergency situations and since response times are important in these situations, it is crucial that ambulances are located in such a way that good coverage is provided throughout the region. Most models that are developed to determine good base locations assume strict 0-1 coverage given a fixed base location and demand point. However, multiple applications require fractional coverage. Examples include stochastic, instead of fixed, response times and survival probabilities. Straightforward adaption of the well-studied MEXCLP to allow for coverage probabilities results in a non-linear formulation in integer variables, limiting the size of instances that can be solved by the model. In this paper, we present a linear integer programming formulation for the problem. We show that the computation time of the linear formulation is significantly shorter than that for the non-linear formulation. As a consequence, we are able to solve larger instances. Finally, we will apply the model, in the setting of stochastic response times, to the region of Amsterdam, the Netherlands.

show abstract

Section: Model Formulationmentioning

confidence: 99%

Linear formulation for the Maximum Expected Coverage Location Model with fractional coverage

Berg

Kommer

Zuzáková

2016

Operations Research for Health Care

View full text Add to dashboard Cite

show abstract

“…To obtain the required outcomes geographical information system methodology has been proposed in their work. Multiple methodologies like moving average, artificial neural network [30] and support vector methods are used for the analysis of collected data in specified locality with integration of GIS technology. They emphasize on backward propagation [31] with the integration of ANN to provide proper data analysis with time and space.…”

Section: Literature Surveymentioning

confidence: 99%

“…The ARIMA predicts the demand on daily basis considering the weather factor. In [21] and [22] adopted back propagation neural network for prediction of EMS demand and compared neural network with moving average. The outcome shows that the neural network outperforms Moving average in every aspect.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Patient Inflow Prediction Model for Hospital Resource Management

Srikanth

Arivazhagan

2017

IJSC

View full text Add to dashboard Cite

There has been increasing demand in improving service provisioning in hospital resources management. Hospital industries work with strict budget constraint at the same time assures quality care. To achieve quality care with budget constraint an efficient prediction model is required. Recently there has been various time series based prediction model has been proposed to manage hospital resources such ambulance monitoring, emergency care and so on. These models are not efficient as they do not consider the nature of scenario such climate condition etc. To address this artificial intelligence is adopted. The issues with existing prediction are that the training suffers from local optima error. This induces overhead and affects the accuracy in prediction. To overcome the local minima error, this work presents a patient inflow prediction model by adopting resilient backpropagation neural network. Experiment are conducted to evaluate the performance of proposed model inter of RMSE and MAPE. The outcome shows the proposed model reduces RMSE and MAPE over existing back propagation based artificial neural network. The overall outcomes show the proposed prediction model improves the accuracy of prediction which aid in improving the quality of health care management.

show abstract

“…Generally this kind of population flow reverses direction during afternoon rush hours. Data analyses from various metropolitan areas show significant variability in both volume and location of demand for EMS services [2,3]. Therefore, given such demand fluctuations, EMS administrators must first determine the optimal quantity and location of ambulances to meet, or exceed, a given time standard.…”

Section: Introductionmentioning

confidence: 99%