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.

抽象的： 21世纪的劳动力正在经历不断提高的工作需求，而雇主优化工作资源以满足监管，财政和生产力标准。在当今的医疗保健系统中，这可能是最明显的，在当今的医疗保健系统中，劳动力承受着持续的压力，以应对快速变化的护理交付方法，宽广采用电子健康记录以及对公开报告的质量指标的责任增加。 2019年5月，世界卫生组织将倦怠定义为发生的现象。不幸的是，遭受苦难的人认为倦怠未被识别，并且在员工的绩效检测或灾难发生在工作场所中。因此，该项目的总体目标是开发一种以数据为基础的技术来预测即将来临的倦怠，然后在其对健康和工作绩效的影响变得明显之前。作为一个案例研究，该项目将在注册护士（RNS）中建立倦怠的可预测性。在医院环境中，RN的35％-45％通过增加的工作需求（患者敏锐度更高），工作效率低下，人际关系冲突，道德困扰以及对影响其工作的决策的低水平控制，报告了倦怠的主要驱动力。 RN中的倦怠与患者不良结局有关（医疗错误的风险增加，医院获得感染的风险增加），较低的护理质量，在这种情况下增加，拟议的项目的愿景和目的是提出的。该项目的愿景是开发一项技术，通过结合工作场所，心理和身体因素来预测RNS（作为案例研究）的倦怠，并探索采用这种技术的障碍。这项工作重点是以下目的：AIM1。 To create a unique, open-access, de-identified dataset that transforms the science of burnout internationally and informs the interaction of continuous physical measurements (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 （队列-C）站点。目标2。开发一个结合概率图形模型（PGM）和多任务学习（MTL）的分析框架，以得出可解释的倦怠预测。 PGMS解决了个体倦怠表现固有的随机性的挑战，MTL将确定可预测倦怠风险（高，中和低）的常见倦怠因素。使用队列A建立的可预测性将在Cohorts-B＆C中进行验证。 AIM 3。通过评估RN，护士主管和医院管理员的观点来探索障碍（生物伦理学和行政）来采用倦怠预测技术。