SCH: Wearable Augmented Prediction of Burnout in Nurses: A Synergy of Engineering, Bioethics, Nursing

SCH：护士倦怠的可穿戴增强预测：工程、生物伦理学、护理的协同作用

• 批准号: 10437161
• 负责人: Arjun Prasanna Athreya
• 金额: $ 30万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-11 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10437161
• 关键词: Absenteeism; Address; Adopted; Adoption; Adverse effects; Affect; Bioethics; Case Study; Client satisfaction; Clinic; Clinical; Data; Data Set; Discipline of Nursing; Distress; Electronic Health Record; Employee; Engineering; Ethics; Florida; Goals; Health; Health Personnel; Healthcare Systems; High Prevalence; Hospital Administrators; Hospitals; Individual; International; Measures; Medical Errors; Modeling; Morals; Nosocomial Infections; Nurses; Occupational; Occupations; Patient-Focused Outcomes; Patients; Performance; Performance at work; Physiological; Predictive Factor; Productivity; Quality of Care; Registered nurse; Reporting; Resources; Rest; Risk; Science; Site; Stress; Technology; Translating; Vision; Work; Workplace; World Health Organization; administrative database; base; burnout; care delivery; cohort; experience; global health; insight; interpersonal conflict; multi-task learning; patient safety; psychologic; public health relevance; smart watch; synergism

ABSTRACT: The 21st century workforce is experiencing increasing job demands while employers optimize job resources to meet regulatory, fiscal and productivity standards. This is perhaps most apparent in today’s healthcare system, wherein the workforce is under constant stress to cope with rapidly changing care delivery approaches, widespread adoption of electronic health records, and increased reliance on publicly reported quality metrics. In May 2019, the World Health Organization defined burnout as an occupational phenomenon. Unfortunately, burnout is underrecognized by those who suffer from it, and it typically goes undetected until employees’ performance deteriorates or catastrophes occur in workplace. Therefore, this project’s overarching goal is to develop a data-driven technology for predicting impending burnout before its effects on health and work performance become manifest. As a case study, this project will establish predictability of burnout in registered nurses (RNs). In hospital settings, 35%-45% of RNs report burnout primarily driven by increased work demands (higher patient acuity), work inefficiencies, interpersonal conflict, moral distress, and low level of control over decisions that affect their work. Burnout in RNs is associated with poor patient outcomes (increased risk of medical errors, hospital-acquired infections), lower quality of care, increased absenteeism and poor patient satisfaction. Within this context, the proposed project’s vision and aims are presented. This project’s vision is to develop a technology to predict burnout in RNs (as a case study) by combining workplace, psychological, and physiological factors, and exploring the barriers to adopting such a technology. This effort focuses on the following aims: Aim1. To create a unique, open- access, de-identified dataset that transforms the science of burnout internationally and informs the interaction of continuous physiological measures (measured from smart watches) and repeated (quarterly) psychological (measured using validated rating scales) and work-related factors (administrative databases) for predicting burnout (Aim 2) in RNs at Mayo Clinic’s Florida (Cohorts-A&B) and Rochester (Cohort-C) sites. Aim 2. To develop an analytical framework combining probabilistic graphical models (PGMs) and multitask learning (MTL) to derive interpretable predictions of burnout. PGMs addresses the challenge of inherent stochasticity of burnout manifestation across individuals, and MTL will identify common burnout factors predictive of burnout risks (high, medium and low). Predictability established using Cohort-A will be validated in Cohorts-B&C. Aim 3. Explore barriers (bioethics and administrative) to adopting burnout prediction technologies by assessing perspectives of RNs, nurse supervisors and hospital administrators.

摘要： 21世纪的劳动力正在经历不断增长的工作需求，而雇主则优化工作资源，以满足监管、财政和生产率标准。这可能在当今的医疗体系中最为明显，在这个体系中，劳动力面临着持续不断的压力，以应对快速变化的医疗服务提供方法，电子健康记录的广泛采用，以及对公开报告的质量指标的日益依赖。2019年5月，世界卫生组织将职业倦怠定义为一种职业现象。不幸的是，职业倦怠并没有被患者充分认识到，在员工表现恶化或工作场所发生灾难之前，这种情况通常都不会被察觉。因此，该项目的首要目标是开发一种数据驱动的技术，用于在对健康和工作表现的影响变得明显之前预测即将到来的职业倦怠。作为一个案例研究，这个项目将建立注册护士(RN)职业倦怠的可预测性。在医院环境中，35%-45%的RN报告称，主要是由于工作需求增加(患者敏锐度更高)、工作效率低下、人际冲突、精神困扰以及对影响他们工作的决策的控制水平较低而导致工作倦怠。RNS中的倦怠与患者预后差(医疗差错风险增加、医院获得性感染)、较低的护理质量、更多的缺勤率和患者满意度差有关。在此背景下，提出了拟议项目的愿景和目标。这个项目的愿景是开发一种通过结合工作场所、心理和生理因素来预测RNS中的职业倦怠的技术(作为案例研究)，并探索采用这种技术的障碍。这项努力侧重于以下目标：目标1.为了创建一个独特的、开放访问的、不确定的数据集，它在国际上改变了工作倦怠的科学，并在Mayo Clinic的佛罗里达(Cohorts-A&B)和Rochester(Cohort-C)站点的RN中提供了连续的生理测量(从智能手表测量)和重复的(季度)心理测量(使用经过验证的评价表测量)和工作相关因素(管理数据库)的交互作用，以预测职业倦怠(目标2)。目的2.开发一个结合概率图形模型(PGMS)和多任务学习(MTL)的分析框架，以获得对工作倦怠的可解释预测。职业倦怠量表解决了个体职业倦怠表现的内在随机性的挑战，MTL将确定常见的职业倦怠因素，预测职业倦怠风险(高、中、低)。使用Cohort-A建立的预测性将在Cohorts-B&C中得到验证。目的3.通过评估RN、护士主管和医院管理人员的观点，探索采用职业倦怠预测技术的障碍(生物伦理学和管理)。