护士的致命弱点：利用大数据确定影响差点发生的工作量因素。
Nurse's Achilles Heel: Using Big Data to Determine Workload Factors That Impact Near Misses.
机构信息
Professor, College of Nursing, Department of Community Mental Health, University of South Alabama, Mobile, AL, USA.
Chair and Professor, College of Nursing, Department of Community Mental Health, University of South Alabama, Mobile, AL, USA.
出版信息
J Nurs Scholarsh. 2021 May;53(3):333-342. doi: 10.1111/jnu.12652. Epub 2021 Mar 30.
PURPOSE
To explore how big data can be used to identify the contribution or influence of six specific workload variables: patient count, medication count, task count call lights, patient sepsis score, and hours worked on the occurrence of a near miss (NM) by individual nurses.
DESIGN
A correlational and cross-section research design was used to collect over 82,000 useable data points of historical workload data from the three unique systems on a medical-surgical unit in a midsized hospital in the southeast United States over a 60-day period. Data were collected prior to the start of the Covid-19 pandemic in the United States.
METHODS
Combined data were analyzed using JMP Pro version 12. Mean responses from two groups were compared using a t-test and those from more than two groups using analysis of variance. Logistic regression was used to determine the significance of impact each workload variable had on individual nurses' ability to administer medications successfully as measured by occurrence of NMs.
FINDINGS
The mean outcome of each of the six workload factors measured differed significantly (p < .0001) among nurses. The mean outcome for all workload factors except the hours worked was found to be significantly higher (p < .0001) for those who committed an NM compared to those who did not. At least one workload variable was observed to be significantly associated (p < .05) with the occurrence or nonoccurrence of NMs in 82.6% of the nurses in the study.
CONCLUSIONS
For the majority of the nurses in our study, the occurrence of an NM was significantly impacted by at least one workload variable. Because the specific variables that impact performance are different for each individual nurse, decreasing only one variable, such as patient load, will not adequately address the risk for NMs. Other variables not studied here, such as education and experience, might be associated with the occurrence of NMs.
CLINICAL RELEVANCE
In the majority of nurses, different workload variables increase their risk for an NM, suggesting that interventions addressing medication errors should be implemented based on the individual's risk profile.
目的
探讨如何利用大数据来确定六个特定工作量变量（患者数量、药物数量、任务数量、呼叫灯、患者脓毒症评分和护士工作时间）对个体护士发生接近失误（NM）的贡献或影响。
设计
采用相关和横断面研究设计，在美国东南部一家中型医院的外科病房的三个独特系统中，在 60 天内收集了超过 82000 个有用的历史工作量数据点。数据采集于美国新冠疫情大流行之前。
方法
使用 JMP Pro 版本 12 对合并数据进行分析。使用 t 检验比较两组的平均反应，使用方差分析比较三组以上的平均反应。使用逻辑回归确定每个工作量变量对个体护士成功给药能力（通过 NM 的发生来衡量）的影响的显著性。
结果
所测量的六个工作量因素中的每个因素的平均结果在护士之间存在显著差异（p <.0001）。与未发生 NM 的护士相比，发生 NM 的护士的所有工作量因素（除工作时间外）的平均结果都显著更高（p <.0001）。在研究中的 82.6%的护士中，观察到至少一个工作量变量与 NM 的发生或不发生显著相关（p <.05）。
结论
在我们的研究中，大多数护士发生 NM 的情况受到至少一个工作量变量的显著影响。由于对每个个体护士的绩效产生影响的具体变量不同，仅减少一个变量（例如患者数量）不足以解决 NM 的风险。这里未研究的其他变量，如教育和经验，可能与 NM 的发生相关。
临床意义
在大多数护士中，不同的工作量变量增加了他们发生 NM 的风险，这表明针对药物错误的干预措施应根据个体的风险状况实施。
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