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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前急诊室的预婚等待时间

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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 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 Centra 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:在妇产科急诊室,由于妇科和妇产科护理不适当的病人流量以及漫长的等候名单,可能会影响母子的健康,导致不良事件和后果,影响其安全性和满意度。预测患者在急诊室的等待时间是避免此问题的一种方法。这项研究的目的是预测位于葡萄牙北部的波尔图中心医院产妇和围产儿护理中心Centra Materno Infantil do Norte(CMIN)的妇科和产科急诊的分流前等待时间。使用从CMIN中可用的信息系统和技术收集的信息来引入数据挖掘技术。建立的模型显示出良好的结果,分别达到了约74%和94%的准确度和特异性值。此外,在急诊室工作的患者和分诊专业人员的数量以及一些时间变量被认为是分诊前等待时间的直接促进因素。在CMIN中部署的决策支持系统和商业智能平台中所获得知识的实施,可以优化通过急诊室的患者流量并提高服务质量。

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