2016
Pereira, S.; Torres, L.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.
Predicting triage waiting time in maternity emergency care by means of data mining Proceedings Article
Em: A., Reis L. P. Adeli H. Rocha (Ed.): pp. 579-588, Springer Verlag, 2016, ISSN: 21945357, (cited By 5; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089).
Resumo | Links | BibTeX | Etiquetas: Adverse events; Emergency care; Healthcare organizations; Prediction model; Waiting-time, Artificial intelligence; Decision support systems; Embedded systems; Forecasting; Information systems, Data mining
@inproceedings{Pereira2016579,
title = {Predicting triage waiting time in maternity emergency care by means of data mining},
author = {S. Pereira and L. Torres and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Reis L. P. Adeli H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84961639111&doi=10.1007%2f978-3-319-31307-8_60&partnerID=40&md5=f3949e7b9e5bc5b1211b23e76d69bde2},
doi = {10.1007/978-3-319-31307-8_60},
issn = {21945357},
year = {2016},
date = {2016-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {445},
pages = {579-588},
publisher = {Springer Verlag},
abstract = {Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times. © Springer International Publishing Switzerland 2016.},
note = {cited By 5; Conference of World Conference on Information Systems and Technologies, WorldCIST 2016 ; Conference Date: 22 March 2016 Through 24 March 2016; Conference Code:172089},
keywords = {Adverse events; Emergency care; Healthcare organizations; Prediction model; Waiting-time, Artificial intelligence; Decision support systems; Embedded systems; Forecasting; Information systems, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times. © Springer International Publishing Switzerland 2016.