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 世纪的劳动力面临着日益增长的工作需求，而雇主则优化工作资源以满足监管、财政和生产力标准，这在当今的医疗保健系统中可能最为明显，但劳动力不断面临着应对快速变化的医疗服务方式的压力。采用电子健康记录，并增加对公开报告的质量指标的依赖 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，职业倦怠并没有被那些遭受职业倦怠困扰的人认识到，并且通常在员工出现之前才被发现。因此，该项目的首要目标是开发一种数据驱动的技术，在其对健康和工作绩效的影响显现之前预测即将发生的倦怠。在医院环境中，35%-45% 的注册护士表示职业倦怠主要是由于工作要求增加（患者敏锐度更高）、工作效率低下、人际冲突、道德困扰和情绪低落所致。在此背景下，建议对影响其工作的决策的控制程度降低与患者预后不良（医疗差错、医院获得性感染的风险增加）、护理质量下降、缺勤增加和患者满意度差有关。提出了该项目的愿景和目标。该项目的愿景是通过结合工作场所、心理和生理因素来开发一种预测注册护士职业倦怠的技术，并探索采用这种技术的障碍。下列目标：创建一个独特的、开放访问的、去识别化的数据集，改变国际上的倦怠科学，并告知连续生理测量（通过智能手表测量）和重复（每季度）心理测量（使用经过验证的评级量表测量）的相互作用。 ) ) 和工作相关因素（管理数据库），用于预测 Mayo Clinic 佛罗里达州（队列 A 和 B）和罗切斯特（队列 C）站点的注册护士的职业倦怠（目标 2）。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，解决个体倦怠表现固有随机性的挑战，并且 MTL 将识别预测倦怠的常见因素。倦怠风险（高、中、低）。使用队列 A 建立的可预测性将在队列 B 和 C 中得到验证。探索障碍（生物伦理学）。和行政）通过评估注册护士、护士主管和医院管理人员的观点来采用职业倦怠预测技术。