Thompson Samuel C, Holmgren A Jay, Ford Eric W
Health Care Manage Rev. 2022;47(1):78-85. doi: 10.1097/HMR.0000000000000308.
Voluntary turnover (VTO) of nursing employees is expensive for hospital systems and is often associated with lower levels of patient satisfaction, as well as adverse patient outcomes such as falls and medication errors.
The aim of this study was to establish nurses' electronic medical record (EMR) use patterns and test if they can be used to predict VTO.
METHODOLOGY/APPROACH: The study followed 1,836 hospital nurses via the collection of EMR metadata through two 1-month time periods that were 1 year apart. Machine learning algorithms were then used to derive patterns of EMR utilization using VTO as a key variable for classification. Post hoc analysis of the most predictive variables was conducted.
The predictive model was effective in identifying which nurses would turnover 73.4% of the time and which nurses would not turnover 84.1% of the time.
The ability to accurately predict nurses' intentions to leave is critical to reducing turnover. Early identification can lead to specific interventions to mitigate factors that are adversely impacting the nursing experience. Post hoc analysis and the key informant interviews indicated that many nurses do not appear to have good EMR navigation skills and spend significant effort in search of patient information.
护理人员的自愿离职（VTO）对医院系统来说成本高昂，并且常常与患者满意度较低以及诸如跌倒和用药错误等不良患者结局相关联。
本研究的目的是确定护士的电子病历（EMR）使用模式，并测试这些模式是否可用于预测自愿离职。
方法/途径：该研究通过在相隔1年的两个1个月时间段内收集EMR元数据，对1836名医院护士进行跟踪。然后使用机器学习算法，以自愿离职作为分类的关键变量来推导EMR使用模式。对最具预测性的变量进行了事后分析。
该预测模型在识别哪些护士会离职方面的准确率为73.4%，在识别哪些护士不会离职方面的准确率为84.1%。
准确预测护士离职意图的能力对于减少人员流动至关重要。早期识别可以导致采取具体干预措施，以减轻对护理体验产生不利影响的因素。事后分析和关键信息提供者访谈表明，许多护士似乎没有良好的EMR导航技能，并且在查找患者信息上花费了大量精力。
