Motivation

Emergency Departments’ (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage.

Objectives

The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation.

Methods

We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers’ title, abstract and key words for the topics “triage”, “emergency department”/“emergency room” and concepts within the field of intelligent systems.

Results

From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients’ age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient's prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage.

Conclusions

In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals’ decision-making thereby leading to better clinical management and patients’ outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.

中文翻译：

---

使用智能系统的急诊分诊临床决策支持系统：回顾。

动机

在全球范围内实施的急诊科（ED）现代分诊系统完全基于医学知识和经验。这是这些系统的局限性，因为可能存在可在大量临床历史数据中探索的隐藏模式。可以将智能技术应用于这些数据以开发临床决策支持系统（CDSS），从而为卫生专业人员提供客观标准。因此，最重要的是要确定什么因素阻碍了此类系统在ED分类中的应用。

目标

本文的目的是评估用于分诊的智能CDSS如何帮助提高急诊室的护理质量，并确定其在实施过程中面临的挑战。

方法

我们对6个数字图书馆进行了手动搜索，从而应用了标准的范围审查方法，即：ScienceDirect，IEEE Xplore，Google Scholar，Springer，MedlinePlus和Web of Knowledge。为每个数字图书馆创建并自定义搜索查询，以获取信息。核心搜索包括在论文标题，摘要和关键词中搜索主题“分类”，“应急部门” /“应急室”和智能系统领域中的概念。

结果

从评论搜索中，我们发现逻辑回归是模型设计最常用的技术，而接收器工作曲线（AUC）下的面积是最常用的性能指标。除了分类优先级外，最常用的建模变量是患者的年龄，性别，生命体征和主要诉求。所选论文的主要贡献包括改善患者的优先顺序，预测重症监护的需求，住院或重症监护病房（ICU）的住院时间，ED住院时间（LOS）以及根据分诊中的可用信息得出的死亡率。

结论

在ED中验证CDSS的论文中，作者发现卫生专业人员的决策有了改善，从而改善了临床管理和患者的治疗效果。但是，我们发现超过一半的研究缺少此实施阶段。我们得出的结论是，对于这些研究，有必要验证CDSS并定义关键绩效指标，以证明在分流中纳入CDSS可以真正改善护理水平。