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Predicting Pre-triage Waiting Time in a Maternity Emergency Room Through Data Mining

机译:通过数据挖掘预测产妇急诊室的分类前等待时间

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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 conse-quences 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.
机译:一个不适合的患者流程以及妇产病的急诊室的延长等待名单,关于妇科和妇产科,可能会影响母亲和孩子的健康,导致对他们的安全和满意度的不利事件和灭绝。预测急诊室内的患者的等待时间是避免这个问题的手段。本研究旨在预测Centro Materno Infantil的妇产科和妇产科的妇产科妇科和妇产科的预诊断时间,位于葡萄牙北部的欧葛托中心住院地区的母亲和围产科院妇幼保健院。使用CMIN中可用的信息系统和技术收集的信息引发数据挖掘技术。该模型显影呈现出良好的结果,达到约74%和94%的精度和特异性值。此外,在急诊室工作的患者和分类专业人员以及一些时间变量的人数被确定为直接增强剂到分类前等待时间。在CMIN部署的决策支持系统和商业智能平台中实现了达到的知识,导致通过急诊室进行患者流动,提高服务质量。

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