2014
Abelha, A.; Pereira, E.; Brandão, A.; Portela, F.; Santos, M.; Machado, J.
Simulating a multi-level priority triage system for maternity emergency Proceedings Article
Em: de Oliveira C.B. Brito A.C., Tavares J. M. R. S. (Ed.): pp. 278-282, EUROSIS, 2014, ISBN: 9789077381861, (cited By 3; Conference of 28th European Simulation and Modelling Conference, ESM 2014 ; Conference Date: 22 October 2014 Through 24 October 2014; Conference Code:110004).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Decision support systems; Gynecology; Modal analysis; Obstetrics; Real time systems, Department of healths; Health professionals; Intelligent decision support systems; Maternity care; Real time; Simulation algorithms; Specific priority triage system; Uniform distribution, Interoperability
@inproceedings{Abelha2014278,
title = {Simulating a multi-level priority triage system for maternity emergency},
author = {A. Abelha and E. Pereira and A. Brandão and F. Portela and M. Santos and J. Machado},
editor = {Tavares J. M. R. S. de Oliveira C.B. Brito A.C.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84922311084&partnerID=40&md5=ab182dda99e8716d8e62cdb08d6dd3b9},
isbn = {9789077381861},
year = {2014},
date = {2014-01-01},
journal = {Modelling and Simulation 2014 - European Simulation and Modelling Conference, ESM 2014},
pages = {278-282},
publisher = {EUROSIS},
abstract = {Nowadays Decision Support Systems are increasingly used in order to help health professionals. An example of this application is the implementation of a triage system in hospital emergency. In Centro Materno Infantil do Norte it was implemented an intelligent pre-triage system aiming to prioritize the patients on two levels: Urgent and or outpatient service. However, although specific for obstetrics and gynaecology cases, the system does not meet all clinical requirements. Thus using a simulation algorithm developed within this framework, it was intended to simulate a specific priority triage system for gynaecology and obstetrics but with five acuity levels as suggested by the Portuguese general department of Health (Direção Geral de Saúde). For this study the repository of specific pre-tri age system was used to test the algorithm. After application it was found that the implementation of this system will reduce waiting time, allowing a uniform distribution according to the waiting time and the clinical features. The percentage of deviation between the waiting time and the actual time obtained by simulation algorithm it is approximately 121.6%.},
note = {cited By 3; Conference of 28th European Simulation and Modelling Conference, ESM 2014 ; Conference Date: 22 October 2014 Through 24 October 2014; Conference Code:110004},
keywords = {Artificial intelligence; Decision support systems; Gynecology; Modal analysis; Obstetrics; Real time systems, Department of healths; Health professionals; Intelligent decision support systems; Maternity care; Real time; Simulation algorithms; Specific priority triage system; Uniform distribution, Interoperability},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays Decision Support Systems are increasingly used in order to help health professionals. An example of this application is the implementation of a triage system in hospital emergency. In Centro Materno Infantil do Norte it was implemented an intelligent pre-triage system aiming to prioritize the patients on two levels: Urgent and or outpatient service. However, although specific for obstetrics and gynaecology cases, the system does not meet all clinical requirements. Thus using a simulation algorithm developed within this framework, it was intended to simulate a specific priority triage system for gynaecology and obstetrics but with five acuity levels as suggested by the Portuguese general department of Health (Direção Geral de Saúde). For this study the repository of specific pre-tri age system was used to test the algorithm. After application it was found that the implementation of this system will reduce waiting time, allowing a uniform distribution according to the waiting time and the clinical features. The percentage of deviation between the waiting time and the actual time obtained by simulation algorithm it is approximately 121.6%.