2016
Pereira, S.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.
Predicting pre-triage waiting time in a maternity emergency room through data mining Proceedings Article
Em: H., Zheng X. Zeng D. D. Chen (Ed.): pp. 105-117, Springer Verlag, 2016, ISSN: 03029743, (cited By 5; Conference of International Conference for Smart Health, ICSH 2015 ; Conference Date: 17 November 2015 Through 18 November 2015; Conference Code:163109).
Resumo | Links | BibTeX | Etiquetas: Adverse events; Business Intelligence platform; Classification algorithm; Emergency care; IDSS; Information systems and technologies; Maternity care; Triage system, Artificial intelligence; Decision support systems; Emergency rooms; Forecasting; Gynecology; Health; Interoperability; Obstetrics, Data mining
@inproceedings{Pereira2016105,
title = {Predicting pre-triage waiting time in a maternity emergency room through data mining},
author = {S. Pereira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
editor = {Zheng X. Zeng D.D. Chen H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84958528894&doi=10.1007%2f978-3-319-29175-8_10&partnerID=40&md5=2950ba905955e8200d582703152d613b},
doi = {10.1007/978-3-319-29175-8_10},
issn = {03029743},
year = {2016},
date = {2016-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9545},
pages = {105-117},
publisher = {Springer Verlag},
abstract = {An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The implementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services. © Springer International Publishing Switzerland 2016.},
note = {cited By 5; Conference of International Conference for Smart Health, ICSH 2015 ; Conference Date: 17 November 2015 Through 18 November 2015; Conference Code:163109},
keywords = {Adverse events; Business Intelligence platform; Classification algorithm; Emergency care; IDSS; Information systems and technologies; Maternity care; Triage system, Artificial intelligence; Decision support systems; Emergency rooms; Forecasting; Gynecology; Health; Interoperability; Obstetrics, Data mining},
pubstate = {published},
tppubtype = {inproceedings}
}
An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The implementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services. © Springer International Publishing Switzerland 2016.