FW-HTF-P: Physiological Sensing to Enable Expert Decision-Making in Healthcare

FW-HTF-P：生理传感支持医疗保健领域的专家决策

• 批准号: 1928661
• 负责人: Denny Yu
• 金额: $ 15万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928661&HistoricalAwards=false
• 关键词: FW; HTF; Physiological; Sensing; Enable

Decision-making in healthcare requires rapid synthesis of information from multiple sources, and failure can lead to patient harm. Expert, high-performing nurses can quickly grasp complex situations and perform the skills required for effective patient care. New nurses, however, often lack experience and the expertise that comes with it, hindering their ability to take immediate proper action. Currently, no technological aids exist to support decision nursing decision-making. This award will develop a model of expert nurse responses in ambulatory (outpatient) care and use this model to develop real-time support. With an aging population and care moving to home environments, decision-support technology can have an impact on the health across populations. The technology can also elevate the quality of care in underserved regions and improve training. In addition, this work will advance undergraduate curriculum with new models linking technology with decision-making and improve K-12 education through new project modules that introduce the impact of technology on health. The goal of this project is to augment performance of ambulatory care nurses by integrating physiological sensing of behavior and cognitive function with models of expert behaviors. First, the project will develop models for expert nurse decision-making and actions. The fundamental processes of nursing expertise at the individual and system levels will be modeled with a mixed method approach that includes ethnography and eye-tracking measurements. Second, the project will use physiological sensors (eye tracking and EEG) to monitor nurse capability and behavior. The sensor data and models that use them will be used to identify appropriate times for coaching interventions that will provide decision support to nurses in real-time through augmented reality. The entire system will be evaluated in a controlled setting at the Center for Nursing Education and Simulation with nursing trainees. The goal of this planning project is to develop the necessary infrastructure, and foundational work to expand the opportunities for studying future technology, workers, and work at the level of a full Future of Work at the Human-Technology Frontier research proposal.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

医疗保健决策需要快速综合多个来源的信息，失败可能会导致患者受伤。专业、高绩效的护士可以快速掌握复杂的情况并执行有效的患者护理所需的技能。然而，新护士往往缺乏经验和随之而来的专业知识，这阻碍了他们立即采取适当行动的能力。目前，尚不存在支持决策护理决策的技术辅助工具。该奖项将开发门诊（门诊）护理中的专家护士响应模型，并使用该模型来开发实时支持。随着人口老龄化和护理转移到家庭环境，决策支持技术可以对整个人群的健康产生影响。该技术还可以提高服务欠缺地区的护理质量并改善培训。 此外，这项工作还将通过将技术与决策联系起来的新模式推进本科课程，并通过介绍技术对健康影响的新项目模块改进 K-12 教育。该项目的目标是通过将行为和认知功能的生理感知与专家行为模型相结合来提高门诊护理护士的绩效。首先，该项目将开发专家护士决策和行动的模型。个人和系统层面的护理专业知识的基本过程将采用混合方法进行建模，其中包括人种学和眼动追踪测量。其次，该项目将使用生理传感器（眼动追踪和脑电图）来监测护士的能力和行为。传感器数据和使用它们的模型将用于确定指导干预的适当时间，从而通过增强现实为护士实时提供决策支持。整个系统将在护理教育和模拟中心的受控环境中与护理实习生一起进行评估。 该规划项目的目标是开发必要的基础设施和基础工作，以扩大在人类技术前沿研究提案的完整未来工作水平上研究未来技术、工人和工作的机会。该奖项反映了通过使用基金会的智力价值和更广泛的影响审查标准进行评估，NSF 的法定使命被认为值得支持。

项目成果

Nurse Perceptions of the Usability of Augmented Reality to Support Clinical Decision Making: Results of a Pilot Study

护士对增强现实支持临床决策的可用性的看法：试点研究的结果

• DOI: 10.1109/vrw55335.2022.00115
• 发表时间: 2022-04
• 期刊: 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW
• 作者: Anton, Nicholas E.;Zhou, Guoyang;Hornbeck, Tera M.;Yu, Denny
• 通讯作者: Yu, Denny

Identifying factors that nurses consider in the decision-making process related to patient care during the COVID-19 pandemic

确定护士在 COVID-19 大流行期间与患者护理相关的决策过程中考虑的因素

• DOI: 10.1371/journal.pone.0254077
• 发表时间: 2021
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Anton N;Hornbeck T;Modlin S;Haque MM;Crites M;Yu D
• 通讯作者: Yu D