Probabilistic Model for Managing the Arrival Times of Pre-Hospital Ambulances Based on their Geographical Location (GIS)

The treatment of health emergencies involves several quantifiable processes. In this context, this article proposes a quantitative methodological tool that seeks to determine the best scenarios in order to obtain an efficient reduction in response times. Within the services of pre-hospital care, tra...

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Bibliographic Details
Main Authors: Valencia-Nunez, Edison R., Lopez, Hamilton V. Montenegro, Cevallos-Torres, Lorenzo J.
Format: Conference Proceeding
Language:English
Subjects:
Data models
Geospatial analysis
Hospitals
location
Mathematical model
Probability
random
ambulances
stochastic binomial
Online Access:Request full text
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Summary:The treatment of health emergencies involves several quantifiable processes. In this context, this article proposes a quantitative methodological tool that seeks to determine the best scenarios in order to obtain an efficient reduction in response times. Within the services of pre-hospital care, transportation plays a critical role in the comprehensive management of medical emergencies, so, determining the ideal location of the place from where the ambulances meet the demand, influenced the time of arrival of the resource. Getting to determine the best location of ambulances for health management can be a complex task, given the number of variables involved in this process, and the financial costs involved in conducting empirical experimentation in the field. The proposed model sought to provide decision makers with quantitative probabilistic tools, through the use of Geospatial Software (GIS) and quantitative analysis, which allowed obtaining experimental results minimizing implementation costs, through the operationalization of demand and availability variables and location of ambulances based on their distribution of probabilities, which, Through stochastic processes, they determined the impact on the time of arrival of the resource, this reduction of the arrival time of the ambulance, was contrasted with the initial data, arriving to determine that these variations had statistical significance through parametric tests
ISSN:2573-1998
DOI:10.1109/ICEDEG.2018.8372348