Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA.
Department of Psychiatry, Zucker Hillside Hospital at Northwell Health, Glen Oaks, New York, USA.
Med Educ. 2021 Feb;55(2):222-232. doi: 10.1111/medu.14292. Epub 2020 Sep 7.
Patient handovers remain a significant patient safety challenge. Cognitive load theory (CLT) can be used to identify the cognitive mechanisms for handover errors. The ability to measure cognitive load types during handovers could drive the development of more effective curricula and protocols. No such measure currently exists.
The authors developed the Cognitive Load Inventory for Handoffs (CLIH) using a multi-step process, including expert interviews to enhance content validity and talk-alouds to optimise response process validity. The final version contained 28 items. From January to March 2019, we administered a cross-sectional survey to 1807 residents and fellows from a large health care system in the USA. Participants completed the CLIH following a handover. Exploratory factor analysis of data from one-third of respondents identified high-performing items; confirmatory factor analysis of data from the remaining sample assessed model fit. Model fit was evaluated using the comparative fit index (CFI) (>0.90), Tucker-Lewis index (TFI) (>0.80), standardised root mean square residual (SRMR) (<0.08) and root mean square of error of approximation (RMSEA) (<0.08).
Participants included 693 trainees (38.4%) (231 in the exploratory study and 462 in the confirmatory study). Eleven items were removed during exploratory factor analysis. Confirmatory factor analysis of the 16 remaining items (five for intrinsic load, seven for extraneous load and four for germane load) supported a three-factor model and met criteria for good model fit: the CFI was 0.95, TFI was 0.93, RMSEA was 0.074 and SRMR was 0.07. The factor structure was comparable for gender and role. Intrinsic, extraneous and germane load scales had high internal consistency. With one exception, scale scores were associated, as hypothesised, with postgraduate level and clinical setting.
The CLIH measures three types of cognitive load during patient handovers. Evidencefor validity is provided for the CLIH's content, response process, internal structure and association with other variables. This instrument can be used to determine the relative drivers of cognitive load during handovers in order to optimize handover instruction and protocols.
患者交接仍然是一个重大的患者安全挑战。认知负荷理论(CLT)可用于确定交接错误的认知机制。在交接过程中测量认知负荷类型的能力可以推动更有效的课程和协议的发展。目前没有这样的措施。
作者使用多步骤过程开发了交接认知负荷清单(CLIH),包括专家访谈以提高内容效度和出声思维以优化反应过程效度。最终版本包含 28 个项目。2019 年 1 月至 3 月,我们对美国一个大型医疗系统的 1807 名住院医师和研究员进行了横断面调查。参与者在交接后完成了 CLIH。三分之一受访者数据的探索性因子分析确定了表现良好的项目;其余样本数据的验证性因子分析评估了模型拟合度。通过比较拟合指数(CFI)(>0.90)、塔克-刘易斯指数(TFI)(>0.80)、标准均方根残差(SRMR)(<0.08)和近似误差均方根(RMSEA)(<0.08)来评估模型拟合度。
参与者包括 693 名学员(38.4%)(探索性研究 231 名,验证性研究 462 名)。在探索性因子分析中删除了 11 个项目。16 个剩余项目(5 个用于内在负荷,7 个用于外在负荷,4 个用于固有负荷)的验证性因子分析支持三因素模型,并符合良好模型拟合标准:CFI 为 0.95,TFI 为 0.93,RMSEA 为 0.074,SRMR 为 0.07。性别和角色的因子结构相似。内在、外在和固有负荷量表具有较高的内部一致性。除一项外,量表得分与研究生水平和临床环境呈假设相关。
CLIH 在患者交接期间测量三种类型的认知负荷。CLIH 的内容、反应过程、内部结构和与其他变量的关联提供了有效性证据。该工具可用于确定交接过程中认知负荷的相对驱动因素,以优化交接指导和协议。