Please use this identifier to cite or link to this item:

TitlePredicting pre-triage waiting time in a maternity emergency room through data mining
Author(s)Pereira, Sónia Patrícia Pinto
Portela, Filipe
Santos, Manuel
Machado, José Manuel
Abelha, António
KeywordsData mining
Classification algorithms
Gynecology and obstetrics care
Maternity care
Emergency room
Triage system
Issue date2016
JournalLecture Notes in Computer Science
Abstract(s)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 imp lementation 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.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

Files in This Item:
File Description SizeFormat 
2015 - ICSH - Predicting Pre-triage Time vffr1.pdf430,25 kBAdobe PDFView/Open

Partilhe no FacebookPartilhe no TwitterPartilhe no DeliciousPartilhe no LinkedInPartilhe no DiggAdicionar ao Google BookmarksPartilhe no MySpacePartilhe no Orkut
Exporte no formato BibTex mendeley Exporte no formato Endnote Adicione ao seu ORCID