Essa Changaiz Dil, Victor Gideon, Khan Sadia Farhan, Ally Hafisa, Khan Abdus Salam
Al-Shifa Institute of Health Sciences Narowal, Pakistan.
Shifa Tameer-e-Millat University, Shifa College of Nursing Islamabad, Pakistan.
Am J Emerg Med. 2023 Nov;73:63-68. doi: 10.1016/j.ajem.2023.08.021. Epub 2023 Aug 12.
The study aimed to measure emergency nurses' prevalence of cognitive biases when utilizing Emergency Severity Index (ESI). Moreover, the study aimed to measure the differences between cognitive biases and demographic variables.
Nurses use Emergency Severity Index (ESI) to prioritize the patients. Cognitive biases could compromise the clinical decisions of nurses in triage. Consequently, this hinders the delivery of safe and quality patient care.
A cross-sectional analytical approach invited 208 emergency nurses from four tertiary care hospitals. Institutional review board approval and permission from institutional heads were obtained. Informed consent was attained before data collection. Data was collected through a structured scenario-based questionnaire to measure cognitive biases at five levels of ESI. Descriptive and inferential statistics were obtained through v25.0 of SPSS.
Among the 86.6% response rate, 56.2% of nurses were male. 62.90% had nursing diplomas. Cognitive biases were present at all ESI levels one to five, in order 51%, 45%, 90%, 89%, and 91% among nurses. Premature closure 22%, tolerance to risk 12%, satisfying bias 25%, framing effect 22%, and blind obedience 34% from level one to five consecutively. Demographic variables, including males, experience between 2 and 5 years, general nursing as qualification, and without emergency severity index certification, were identified to encounter more cognitive biases when making triage decisions.
Numerous cognitive biases are considerably existing among emergency nurses when prioritizing patients. Cognitive de-biasing measures can improve triage decisions among nurses that could enhance quality care and patient safety.
本研究旨在衡量急诊护士在使用急诊严重程度指数（ESI）时认知偏差的发生率。此外，该研究旨在衡量认知偏差与人口统计学变量之间的差异。
护士使用急诊严重程度指数（ESI）对患者进行优先排序。认知偏差可能会损害护士在分诊中的临床决策。因此，这阻碍了安全和高质量患者护理的提供。
采用横断面分析方法，邀请了来自四家三级护理医院的208名急诊护士。获得了机构审查委员会的批准和机构负责人的许可。在数据收集前获得了知情同意。通过基于结构化情景的问卷收集数据，以衡量ESI五个级别的认知偏差。通过SPSS 25.0获得描述性和推断性统计数据。
在86.6%的回复率中，56.2%的护士为男性。62.90%拥有护理文凭。在ESI的所有一至五级中均存在认知偏差，护士中的比例依次为51%、45%、90%、89%和91%。从一级到五级，过早关闭为22%，风险容忍为12%，满意偏差为25%，框架效应为22%，盲目服从为34%。已确定人口统计学变量，包括男性、2至5年工作经验、普通护理资质且无急诊严重程度指数认证的护士，在进行分诊决策时会遇到更多认知偏差。
急诊护士在对患者进行优先排序时存在大量认知偏差。认知去偏差措施可以改善护士的分诊决策，从而提高护理质量和患者安全。
