Emergency Department, University Hospital of Liege, , Liège, Belgium.
Emerg Med J. 2014 Feb;31(2):115-20. doi: 10.1136/emermed-2012-201927. Epub 2013 Jan 23.
Overcrowding in emergency departments (ED) leads to reductions in quality of care. Consequently, several different triage tools have been developed to prioritise patient intake. Differences in emergency medical services in different countries have limited the generalisation of pre-existing triage systems; for this reason, specific algorithms corresponding to local characteristics are needed. Accordingly, we developed a specific French-language triage system named Echelle Liégeoise d'Index de Sévérité à l'Admission (ELISA). This study tested its validity and efficiency.
ELISA is a five-category nursing triage algorithm. Intrarater agreement was tested by comparing triage levels attributed to the same clinical scenarios at two different times. Interrater agreement was investigated by comparing triage categories attributed to clinical cases by different triage nurses. Finally, validity was estimated by studying the correlations between the triage ranking assigned by the nurse and actual resource consumption and patient outcome.
The distribution of the difference between nurse classification at the two times was statistically unrelated to which nurse carried out the evaluation. Regarding interrater agreement, assigned classifications were compared to the reference assignment. Cohen's κ coefficient revealed an almost perfect agreement between classification by nurses and the reference. Finally, statistical analysis revealed a strong relation between ELISA and the overall need for supplementary clinical testing. Outcomes were also significantly correlated with ELISA.
The need for a specific, French-language triage tool in our ED led us to develop a new triage scale. This study demonstrates that the scale is a valid triage tool with high interrater and intrarater agreement and considerable efficiency.
急诊部(ED)过度拥挤会导致医疗质量下降。因此,已经开发了几种不同的分诊工具来优先处理患者。不同国家的紧急医疗服务差异限制了现有分诊系统的推广;因此,需要针对当地特点制定特定的算法。因此,我们开发了一种特定的法语分诊系统,名为 Echelle Liégeoise d'Index de Sévérité à l'Admission(ELISA)。本研究测试了其有效性和效率。
ELISA 是一种五级护理分诊算法。通过比较两次不同时间对同一临床场景进行的分诊级别,测试了内部一致性。通过比较不同分诊护士对临床病例进行的分诊类别,研究了内部一致性。最后,通过研究护士分配的分诊等级与实际资源消耗和患者结局之间的相关性来评估有效性。
两次评估之间的差异分布与进行评估的护士无关。关于内部一致性,分配的分类与参考分配进行了比较。科恩氏κ系数显示,护士的分类与参考之间存在几乎完美的一致性。最后,统计分析显示 ELISA 与整体补充临床检测需求之间存在很强的关系。结局也与 ELISA 显著相关。
我们的 ED 需要一种特定的、法语的分诊工具,这促使我们开发了一种新的分诊量表。本研究表明,该量表是一种有效的分诊工具,具有较高的内部一致性和外部一致性,并且效率相当高。