Data acquisition process for an intelligent decision support in gynecology and obstetrics emergency triage

Manchester Triage System is a reliable system of triage in the emergency department of a hospital. This system when applied to a specific patients’ condition such the pregnancy has several limitations. To overcome those limitations an alternative triage IDSS was developed in the MJD. In this approach the knowledge was obtained directly from the doctors’ empirical and scientific experience to make the first version of decision models. Due to the particular gynecological and/or obstetrics requests other characteristics had been developed, namely a system that can increase patient safety for women in need of immediate care and help low-risk women avoid high-risk care, maximizing the use of resources. This paper presents the arrival flowchart, the associated decisions and the knowledge acquisition cycle. Results showed that this new approach enhances the efficiency and the safety through the appropriate use of resources and by assisting the right patient in the right place.The work of Filipe Portela was supported by the grant SFRH/BD/70156/2010 from FC

Predicting pre-triage waiting time in a maternity emergency room through data mining

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

Improving quality of services in maternity care triage system

The main objectives in hospital triages are to improve the quality of care and reduce the risks associated to the waiting time in emergency care. Thus, an efficient triage is a good way to avoid some future problems and how much quicker it is, more the patient can benefit. The most common triage system is the Manchester Triage System (MTS). MTS is a reliable system focused in the hospital emergency department. However, its use is more suitable for more widespread medical emergencies and not for specialized cases as is Maternity Care and Gynecological and Obstetrics emergencies. To overcome these limitations an alternative pre-triage system, integrated into an intelligent decision support system, was developed in order to better characterize the patient and correctly defined the patient as urgent or not. This system allows increasing patient’s safety, especially women who need immediate care. This paper presents the main results and the workflow describing the decision process in real time in the emergency department, when the women are submitted to the triage process and they are identified possible evolution points of this system.This work is funded by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within projects PEstOE/EEI/UI0752/2014 and PEst-OE/EEI/UI0319/201

Simulating a multi-level priority triage system for Maternity Emergency

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. These systems allow more effective and rapid decisions taking into account the clinical needs of patients. In Centro Materno Infantil do Norte it was implemented an intelligent system of pre-triage which aims to prioritize the patients on two levels: Urgent (URG) and (ARGO). However, although specific for obstetrics and gynecology 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 gynecology and obstetrics but with five levels of acuity as suggested by the Portuguese general department of Health (Direção Geral de Saúde). For this study the repository of specific pre-triage system was used to test the algorithm. After application, it was found that the implementation of this system in Centro Materno Infantil do Norte 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 is approximately 121.6%(undefined

Predicting triage waiting time in maternity emergency care by means of data mining

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.Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/201