Collaborative Research: FW-HTF-R: The Future of Robot-Assisted Nursing: Interactive AI Frameworks for Upskilling Nurses and Customizing Robot Assistance
合作研究:FW-HTF-R:机器人辅助护理的未来:用于提高护士技能和定制机器人辅助的交互式人工智能框架
基本信息
- 批准号:2222876
- 负责人:
- 金额:$ 12.17万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-01 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The specific objectives of the Future of Work at the Human-Technology Frontier program are (1) to facilitate convergent research that employs the joint perspectives, methods, and knowledge of computer science, engineering, learning sciences, research on education and workforce training, and social, behavioral, and economic sciences; (2) to encourage the development of a research community dedicated to designing intelligent technologies and work organization and modes inspired by their positive impact on individual workers, the work at hand, the way people learn and adapt to technological change, creative and supportive workplaces (including remote locations, homes, classrooms, or virtual spaces), and benefits for social, economic, and environmental systems at different scales; (3) to promote deeper basic understanding of the interdependent human-technology partnership to advance societal needs by advancing design of intelligent work technologies that operate in harmony with human workers, including consideration of how adults learn the new skills needed to interact with these technologies in the workplace, and by enabling broad workforce participation, including improving accessibility for those challenged by physical or cognitive impairment; and (4) to understand, anticipate, and explore ways of mitigating potential risks arising from future work at the human-technology frontier.Nursing is a discipline of knowledge and practice focused on delivering patient-centered care. As the healthcare providers who are with hospitalized patients 24 hours a day, 7 days a week, registered nurses are crucial for ensuring patient safety and delivering patient-centered care. Alarmingly, however, the U.S. is experiencing a dire nursing shortage, which is projected to significantly worsen in the next decade. As a result, nurses have limited time for patient-centered care. To continue providing high-quality patient care, healthcare leaders are scrambling for solutions, often turning to technological aids such as artificial intelligence (AI) and robots. On one hand, AI-enabled robotic assistants hold the potential to support nurses in some routine tasks, allowing them to spend more time on patient care and improving patient outcomes. On the other hand, the introduction of robots also brings forth several areas of concerns such as increase in nursing workload due to required training and maintenance to use these complex systems. This planning project will develop a multi-disciplinary research agenda to systematically introduce nurses to AI-enabled robotics technology, with the goal of ensuring that the integration of robots in the future of nursing brings long-term positive impact.Seamless integration of robotic assistants in the nursing workflow requires (a) careful scientific study of the impact of robots on nursing workload, (b) design of nurse-centered frameworks for customizing robotic assistance, and (c) continued development of nurse training content and practices. To develop a research agenda that expands on these thrusts, the project will involve three main planning activities. First, the project will bring together technologists, healthcare professionals, social scientists, and educators through workshops and stakeholder meetings. These meetings will facilitate cross-disciplinary collaborations and formation of a convergent research team. Second, through participatory design, the project team will create a testbed for robot-assisted nursing, where multiple disciplines can brainstorm, prototype, and evaluate solutions for robot-assisted nursing. Third, the project team will use the testbed for generating preliminary data to assess the impact of robotic assistants on nursing workload. The data collection will be conducted with the help of nurse volunteers of varied experience, and in a combination of simulated and physical environments.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.
在人类技术前沿计划的工作的未来的具体目标是(1)促进融合研究,采用联合的观点,方法和计算机科学,工程,学习科学,教育和劳动力培训研究,社会,行为和经济科学的知识;(2)鼓励发展一个研究团体,致力于设计智能技术和工作组织和模式,这些技术和工作组织和模式受到其对个人工人的积极影响的启发,手头的工作,人们学习和适应技术变革的方式,创造性和支持性的工作场所(包括远程位置、家庭、教室或虚拟空间),以及不同规模的社会、经济和环境系统的效益;(3)促进对相互依存的人类的更深入的基本理解-技术伙伴关系,通过推进与人类工人和谐运作的智能工作技术的设计,包括考虑成年人如何学习在工作场所与这些技术互动所需的新技能,以及促进广泛的劳动力参与,包括改善那些受到身体或认知障碍挑战的人的无障碍环境;及(4)了解、预测及探索减轻未来人类科技前沿工作所产生的潜在风险的方法。护理是一门以病人为中心的护理知识与实践的学科。作为每周7天,每天24小时与住院患者在一起的医疗保健提供者,注册护士对于确保患者安全和提供以患者为中心的护理至关重要。然而,令人担忧的是,美国正在经历严重的护理短缺,预计未来十年将大幅恶化。因此,护士只有有限的时间来进行以病人为中心的护理。为了继续提供高质量的患者护理,医疗保健领导者正在争先恐后地寻找解决方案,通常转向人工智能(AI)和机器人等技术辅助手段。一方面,支持人工智能的机器人助手有可能在一些常规任务中为护士提供支持,使他们能够将更多的时间用于患者护理并改善患者的治疗效果。另一方面,机器人的引入也带来了几个令人担忧的领域,例如由于使用这些复杂系统所需的培训和维护而增加了护理工作量。该规划项目将制定一个多学科的研究议程,系统地向护士介绍人工智能机器人技术,目标是确保机器人在未来护理中的整合带来长期的积极影响。机器人助手在护理工作流程中的无缝整合需要(a)仔细科学研究机器人对护理工作量的影响,(B)设计以护士为中心的框架,用于定制机器人辅助,以及(c)继续开发护士培训内容和实践。为了制定一个研究议程,扩大这些重点,该项目将涉及三个主要的规划活动。首先,该项目将通过研讨会和利益相关者会议将技术专家、医疗保健专业人员、社会科学家和教育工作者聚集在一起。这些会议将促进跨学科的合作,并形成一个融合的研究团队。其次,通过参与式设计,项目团队将创建一个机器人辅助护理的试验平台,多个学科可以集思广益,原型设计和评估机器人辅助护理的解决方案。第三,项目团队将使用测试平台生成初步数据,以评估机器人助理对护理工作量的影响。数据收集将在不同经验的护士志愿者的帮助下进行,并在模拟和物理环境的组合中进行。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Robotic Tutors for Nurse Training: Opportunities for HRI Researchers
- DOI:10.1109/ro-man57019.2023.10309573
- 发表时间:2023-08
- 期刊:
- 影响因子:0
- 作者:Carlos Quintero-Pe˜na;Peizhu Qian;N. Fontenot;Hsin-Mei Chen;Shannan K Hamlin;L. Kavraki;Vaibhav Unhelkar
- 通讯作者:Carlos Quintero-Pe˜na;Peizhu Qian;N. Fontenot;Hsin-Mei Chen;Shannan K Hamlin;L. Kavraki;Vaibhav Unhelkar
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Lydia Kavraki其他文献
Lydia Kavraki的其他文献
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{{ truncateString('Lydia Kavraki', 18)}}的其他基金
A Framework for Manipulation Planning and Execution under Uncertainty in Partially-Known Environments
部分已知环境中不确定性下的操纵规划和执行框架
- 批准号:
2336612 - 财政年份:2024
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
Collaborative Research [FW-HTF-RM]: The Future of Nurse Training: Robotic Teaching Assistant Systems for Nursing Instructors
协作研究 [FW-HTF-RM]:护士培训的未来:护理讲师的机器人助教系统
- 批准号:
2326390 - 财政年份:2023
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
IIBR:Informatics:RAPID: Structure-based identification of SARS-derived peptides with potential to induce broad protective immunity
IIBR:信息学:RAPID:基于结构的 SARS 衍生肽的鉴定,具有诱导广泛保护性免疫的潜力
- 批准号:
2033262 - 财政年份:2020
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
RI: Small: A Novel Framework for Informed Manipulation Planning
RI:小型:知情操纵规划的新颖框架
- 批准号:
2008720 - 财政年份:2020
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
NRI: FND: Robotic Collaboration through Scalable Reactive Synthesis
NRI:FND:通过可扩展反应合成进行机器人协作
- 批准号:
1830549 - 财政年份:2018
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
RI: Small: Robot Motion Planning with an Experience Database
RI:小型:使用经验数据库进行机器人运动规划
- 批准号:
1718478 - 财政年份:2017
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
SHF: Medium: Automating robot programming through constraint solving and motion planning
SHF:中:通过约束求解和运动规划实现机器人编程自动化
- 批准号:
1514372 - 财政年份:2015
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
AF: Small: An Integrated Approach to Characterizing Conformational Changes of Large Proteins
AF:小:表征大蛋白质构象变化的综合方法
- 批准号:
1423304 - 财政年份:2014
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
NRI: Small: Collaborative Research: Rethinking Motion Generation for Robots Operating in Human Workspaces
NRI:小型:协作研究:重新思考在人类工作空间中操作的机器人的运动生成
- 批准号:
1317849 - 财政年份:2013
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
ABI Innovation: Mining Metabolic and Enzyme Databases for the Composition of Non-Canonical Pathways
ABI 创新:挖掘代谢和酶数据库以组成非规范途径
- 批准号:
1262491 - 财政年份:2013
- 资助金额:
$ 12.17万 - 项目类别:
Standard Grant
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