FW-HTF-R: Biometrics and AI to Support Expert Nurse Decision-Making

FW-HTF-R：生物识别技术和人工智能支持专家护士决策

• 批准号: 2129097
• 负责人: Denny Yu
• 金额: $ 50万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129097&HistoricalAwards=false
• 关键词: FW; HTF; Biometrics; AI; Support

Healthcare workers routinely make fast life-saving decisions. Failures in decision-making can threaten the welfare of the patient and the worker. High-performing, expert nurses utilize decision-making strategies such as prioritizing important information, maintaining a big-picture view of the situation, and executing effective clinical skills to provide safe patient care. New or inexperienced nurses often lack the experiences to support expert decision-making. This project is developing sensing technology and algorithms that can be used in real-time decision-support for training nursing students. The technology could improve the skills and real-time decision making of these students, especially in distracting and risky situations. The project is also creating teaching modules for high school students and inter-disciplinary university courses that integrate fields such as computer science, human factors engineering, and nursing. The project vision is to augment the nursing workforce with technology that will support nurse decision-making in real time while nurses actively care for patients, work with other caregivers, and carry out their diverse responsibilities. To achieve this goal, the project is developing real-time biometrics that can assess the cognitive state of workers during their complex, dynamic healthcare work. The project will also study expert nurse behaviors to learn efficient decision-making patterns that could be used to guide novice nurses. Imitation (computer) learning of expert behavior will be used to train the guides. In the future, the developed system could also support rapid recognition of patient risk by integrating user sensing and decision-support behaviors with just-in-time wearables designed for healthcare workplaces.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.

医护人员通常会快速做出挽救生命的决定。决策失败可能会威胁患者和工作人员的福利。高绩效的专家护士会利用决策策略，例如对重要信息进行优先排序、保持对情况的全局看法以及执行有效的临床技能来提供安全的患者护理。新护士或缺乏经验的护士往往缺乏支持专家决策的经验。该项目正在开发可用于培训护理学生的实时决策支持的传感技术和算法。该技术可以提高这些学生的技能和实时决策能力，特别是在分散注意力和危险的情况下。该项目还为高中生创建教学模块，以及整合计算机科学、人因工程和护理等领域的跨学科大学课程。该项目的愿景是通过技术来增强护理人员队伍，这些技术将支持护士实时决策，同时护士积极护理患者、与其他护理人员合作并履行其多样化的职责。为了实现这一目标，该项目正在开发实时生物识别技术，可以评估工作人员在复杂、动态的医疗保健工作期间的认知状态。该项目还将研究专家护士的行为，以学习可用于指导新手护士的有效决策模式。对专家行为的模仿（计算机）学习将用于培训导游。未来，所开发的系统还可以通过将用户感知和决策支持行为与专为医疗保健工作场所设计的即时可穿戴设备相结合，支持快速识别患者风险。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：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。

项目成果

Detailing experienced nurse decision making during acute patient care simulations

详细介绍经验丰富的护士在急性患者护理模拟过程中的决策

• DOI: 10.1016/j.apergo.2023.103988
• 发表时间: 2023-05
• 期刊: Applied Ergonomics
• 影响因子: 3.2
• 作者: Anton, Nicholas E.;Zhou, Guoyang;Hornbeck, Tera;Nagle, Amy M.;Norman, Susan;Shroff, Anand D.;Yu, Denny
• 通讯作者: Yu, Denny