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： 世纪的劳动力正在经历不断增长的工作需求，而雇主优化工作资源，以满足监管，财政和生产力标准。这可能在当今的医疗保健系统中最为明显，其中劳动力处于不断的压力下，以科普快速变化的护理提供方法，电子健康记录的广泛采用以及对公开报告的质量指标的依赖增加。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，职业倦怠被那些遭受职业倦怠的人低估了，并且通常在员工的表现恶化或工作场所发生灾难之前不会被发现。因此，该项目的首要目标是开发一种数据驱动的技术，用于在其对健康和工作表现的影响变得明显之前预测即将到来的倦怠。作为一个案例研究，本研究将建立可预测的职业倦怠在注册护士（RNs）。在医院环境中，35%-45%的RN报告倦怠主要是由工作需求增加（更高的患者敏锐度），工作效率低下，人际冲突，道德困扰以及对影响其工作的决策控制水平低所驱动。RN的倦怠与患者预后不良（医疗错误风险增加，医院获得性感染），护理质量降低，缺勤率增加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。该项目的愿景是通过结合工作场所，心理和生理因素，并探索采用这种技术的障碍，开发一种技术来预测RN的倦怠（作为案例研究）。这项工作的重点是实现以下目标：目标1。创造一个独特的，开放获取的，去识别数据集，在国际上改变了倦怠科学，并告知连续生理测量的相互作用（从智能手表测量）和重复心理学季刊（使用经验证的评级量表测量）和工作相关因素（管理数据库）用于预测马约诊所的佛罗里达（Cohorts-A&B）和罗切斯特（Cohort-C）地点的RN的倦怠（Aim 2）。目标二。建立一个结合概率图形模型（PGMs）和多任务学习（MTL）的分析框架，以获得可解释的倦怠预测。PGM解决了个体倦怠表现固有随机性的挑战，MTL将识别预测倦怠风险（高、中、低）的常见倦怠因素。使用队列A建立的可预测性将在队列B &C中得到验证。目标3。探讨障碍（生物伦理学和行政）采用倦怠预测技术通过评估RNs，护士主管和医院管理人员的观点。