背景:护士倦怠导致营业额增加,这是医疗保健系统中的一个严重问题。虽然有充分的证据表明护士工作倦怠,以前的研究中制定的干预措施是一般性的,没有考虑特定的倦怠维度和个体特征.
目的:本研究的目的是开发和优化针对护士职业倦怠的首个量身定制的移动干预措施,它推荐基于人工智能(AI)算法的程序,并测试其可用性,有效性,和满意度。
方法:在本研究中,基于人工智能的移动干预,护士治疗空间,旨在为护士职业倦怠提供量身定制的计划。为期4周的计划包括正念冥想,笑声疗法,讲故事,反思写作,接受和承诺疗法。人工智能算法通过由参与者人口统计学组成的预测试计算相似性,向参与者推荐了其中一个程序,研究变量,以及用哥本哈根倦怠量表测量的倦怠维度得分。完成为期4周的课程后,倦怠,工作压力,使用应激反应清单修改表格的应激反应,应用程序的可用性,应对策略指标的应对策略,和程序满意度(1:非常不满意;5:非常满意)进行了测量。如果用户的倦怠分数在2周计划后降低,AI将推荐的计划识别为有效,并相应地更新算法。经过试点测试(n=10),进行AI优化(n=300)。配对双尾t检验,方差分析,用Spearman相关性检验干预效果和算法优化。
结果:NurseHealingSpace被实现为一个移动应用程序,该应用程序配备了一个系统,该系统根据用户之间的相似性通过AI推荐4个程序中的1个程序。AI算法可以很好地匹配推荐给使用有效数据最相似的参与者的程序。用户对便利性和视觉质量感到满意,但对没有通知和无法自定义程序不满意。该应用程序的总体可用性评分为3.4分,满分5分。护士的职业倦怠分数在第一个2周项目完成后显著下降(t=7.012;P<.001),在第二个2周项目后进一步下降(t=2.811;P=.01)。完成护士治疗空间计划后,工作压力(t=6.765;P<.001)和应激反应(t=5.864;P<.001)显著降低。在第二个为期两周的节目中,倦怠水平按参与顺序降低(r=-0.138;P=.04)。第一个程序(F=3.493;P=.03)和第二个程序(F=3.911;P=.02)的用户满意度均有所提高。
结论:该计划有效地减少了倦怠,工作压力,和应激反应。护士管理人员能够使用这种基于AI的计划来防止护士辞职并保持医疗服务质量,从而为护士职业倦怠提供量身定制的干预措施。因此,这个应用程序可以改善定性医疗保健,提高员工满意度,降低成本,并最终提高医疗保健系统的效率。
BACKGROUND: Nurse burnout leads to an increase in turnover, which is a serious problem in the health care system. Although there is ample evidence of nurse burnout, interventions developed in previous studies were general and did not consider specific burnout dimensions and individual characteristics.
OBJECTIVE: The objectives of this
study were to develop and optimize the first tailored mobile intervention for nurse burnout, which recommends programs based on artificial intelligence (AI) algorithms, and to test its usability, effectiveness, and satisfaction.
METHODS: In this
study, an AI-based mobile intervention, Nurse Healing Space, was developed to provide tailored programs for nurse burnout. The 4-week program included mindfulness meditation, laughter therapy, storytelling, reflective writing, and acceptance and commitment therapy. The AI algorithm recommended one of these programs to participants by calculating similarity through a pretest consisting of participants\' demographics, research variables, and burnout dimension scores measured with the Copenhagen Burnout Inventory. After completing a 4-week program, burnout, job stress, stress response using the Stress Response Inventory Modified Form, the usability of the app, coping strategy by the coping strategy indicator, and program satisfaction (1: very dissatisfied; 5: very satisfied) were measured. The AI recognized the recommended program as effective if the user\'s burnout score reduced after the 2-week program and updated the algorithm accordingly. After a pilot test (n=10), AI optimization was performed (n=300). A paired 2-tailed t test, ANOVA, and the Spearman correlation were used to test the effect of the intervention and algorithm optimization.
RESULTS: Nurse Healing Space was implemented as a mobile app equipped with a system that recommended 1 program out of 4 based on similarity between users through AI. The AI algorithm worked well in matching the program recommended to participants who were most similar using valid data. Users were satisfied with the convenience and visual quality but were dissatisfied with the absence of notifications and inability to customize the program. The overall usability score of the app was 3.4 out of 5 points. Nurses\' burnout scores decreased significantly after the completion of the first 2-week program (t=7.012; P<.001) and reduced further after the second 2-week program (t=2.811; P=.01). After completing the Nurse Healing Space program, job stress (t=6.765; P<.001) and stress responses (t=5.864; P<.001) decreased significantly. During the second 2-week program, the burnout level reduced in the order of participation (r=-0.138; P=.04). User satisfaction increased for both the first (F=3.493; P=.03) and second programs (F=3.911; P=.02).
CONCLUSIONS: This program effectively reduced burnout, job stress, and stress responses. Nurse managers were able to prevent nurses from resigning and maintain the quality of medical services using this AI-based program to provide tailored interventions for nurse burnout. Thus, this app could improve qualitative health care, increase employee satisfaction, reduce costs, and ultimately improve the efficiency of the health care system.