Agent based simulation to optimise emergency departments

Nowadays, many of the health care systems are large and complex environments and quite dynamic, specifically Emergency Departments, EDs. It is opened and working 24 hours per day throughout the year with limited resources, whereas it is overcrowded. Thus, is mandatory to simulate EDs to improve qualitatively and quantitatively their performance. This improvement can be achieved modelling and simulating EDs using Agent-Based Model, ABM and optimising many different staff scenarios. This work optimises the staff configuration of an ED. In order to do optimisation, objective functions to minimise or maximise have to be set. One of those objective functions is to find the best or optimum staff configuration that minimise patient waiting time. The staff configuration comprises: doctors, triage nurses, and admissions, the amount and sort of them. Staff configuration is a combinatorial problem, that can take a lot of time to be solved. HPC is used to run the experiments, and encouraging results were obtained. However, even with the basic ED used in this work the search space is very large, thus, when the problem size increases, it is going to need more resources of processing in order to obtain results in an acceptable time.

Locating emergency services with priority rules: The priority queuing covering location problem

One of the assumptions of the Capacitated Facility Location Problem (CFLP) is thatdemand is known and fixed. Most often, this is not the case when managers take somestrategic decisions such as locating facilities and assigning demand points to thosefacilities. In this paper we consider demand as stochastic and we model each of thefacilities as an independent queue. Stochastic models of manufacturing systems anddeterministic location models are put together in order to obtain a formula for thebacklogging probability at a potential facility location.Several solution techniques have been proposed to solve the CFLP. One of the mostrecently proposed heuristics, a Reactive Greedy Adaptive Search Procedure, isimplemented in order to solve the model formulated. We present some computationalexperiments in order to evaluate the heuristics performance and to illustrate the use ofthis new formulation for the CFLP. The paper finishes with a simple simulationexercise.

Locating emergency services with different priorities: The priority queuing covering location problem

Previous covering models for emergency service consider all the calls to be of the sameimportance and impose the same waiting time constraints independently of the service's priority.This type of constraint is clearly inappropriate in many contexts. For example, in urban medicalemergency services, calls that involve danger to human life deserve higher priority over calls formore routine incidents. A realistic model in such a context should allow prioritizing the calls forservice.In this paper a covering model which considers different priority levels is formulated andsolved. The model heritages its formulation from previous research on Maximum CoverageModels and incorporates results from Queuing Theory, in particular Priority Queuing. Theadditional complexity incorporated in the model justifies the use of a heuristic procedure.

Emergency hospital services utilization in Lleida (Spain): a cross-sectional study of immigrant and Spanish-born populations

Background: The use of emergency hospital services (EHS) has increased steadily in Spain in the last decade while the number of immigrants has increased dramatically. Studies show that immigrants use EHS differently than native-born individuals, and this work investigates demographics, diagnoses and utilization rates of EHS in Lleida (Spain). Methods: Cross-sectional study of all the 96,916 EHS visits by patients 15 to 64 years old, attended during the years 2004 and 2005 in a public teaching hospital. Demographic data, diagnoses of the EHS visits, frequency of hospital admissions, mortality and diagnoses at hospital discharge were obtained. Utilization rates were estimated by group of origin. Poisson regression was used to estimate the rate ratios of being visited in the EHS with respect to the Spanish-born population. Results: Immigrants from low-income countries use EHS services more than the Spanish-born population. Differences in utilization patterns are particularly marked for Maghrebi men and women and sub-Saharan women. Immigrant males are at lower risk of being admitted to the hospital, as compared with Spanish-born males. On the other hand, immigrant women are at higher risk of being admitted. After excluding the visits with gynecologic and obstetric diagnoses, women from sub-Saharan Africa and the Maghreb are still at a higher risk of being admitted than their Spanish-born counterparts. Conclusion: In Lleida (Spain), immigrants use more EHS than the Spanish born population. Future research should indicate whether the same pattern is found in other areas of Spain and whether EHS use is attributable to health needs, barriers to access to the primary care services or similarities in the way immigrants access health care in their countries of origin.

A Multiagent System for Coordinating Ambulances for Emergency Medical Services

To coordinate ambulances for emergency medical services, a multiagent system uses an auction mechanism based on trust. Results of tests using real data show that this system can efficiently assign ambulances to patients, thereby reducing transportation time. Emergency transportation on specialized vehicles is needed when a person's health is in risk of irreparable damage. A patient can't benefit from sophisticated medical treatments and technologies if she or he isn't placed in a proper healthcare center with the appropriate medical team. For example, strokes are neurological emergencies involving a limited amount of time in which treatment measures are effective

Emergency Management and Wildland-Urban Interface Fires in the United States of America

FEMA's mission is to support our citizens and first responders to ensure that as a Nation we work together to build, sustain, and improve our capability to prepare for, protect against, respond to, recover from, and mitigate all hazards.

Emergency Management in the United States of America

Core capabilities are under the above mission areas as outlined in the National Preparedness Goal. Planning, Public Information and Warning, and Operational Coordination cut across all five mission areas. Without these three cross-cutting capabilities, the other capabilities might not be achieved or could be weakened. Other core capabilities are aligned under a specific mission area, based on where it had the most relevance. Core capabilities alignment: Prevention capabilities focus on things related to preventing an imminent terrorist attack; by imminent, we mean an attack that is about to happen ; Protection capabilities focus on security— making sure things, systems, and people are protected ; Mitigation capabilities focus on risk, resilience and building a culture of preparedness; Response capabilities focus on meeting a community’s immediate needs when disaster strikes and finally, recovery capabilities focus on getting communities back on their feet.