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FW-HTF-RM: Enhancing Future Work of Nursing Professionals through Collaborative Human-Robot Interfaces

FW-HTF-RM：通过协作式人机界面增强护理专业人员的未来工作

基本信息

批准号：
2026584
负责人：
Dan Popa
金额：
$ 149.97万
依托单位：
University of Louisville Research Foundation Inc
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：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026584&HistoricalAwards=false
关键词：
FW HTF RM Enhancing Future

项目摘要

This Future of Work at the Human-Technology Frontier (FW-HTF) research project advances a vision for the profession of nursing where collaborative human-robot interfaces (CHRIs) can enhance nurse productivity and reduce on-the-job stress. Interfaces in this project are broadly defined as any link between humans and intelligent machines such as robots, using advanced sensors, mobile computing, and display devices. In this project, interfaces will intelligently “adapt” to provide both physical and cognitive assistance to nurses and patients in future healthcare environments. The project will pursue four objectives. The team will develop a taxonomy of nursing tasks to determine those that can be justifiably delegated to intelligent robots. The team will then compare the ability of two novel CHRIs to facilitate stable and effective shared human-robot control of a robot-assisted walking task (i.e., fall prevention). They will also investigate a CHRI recommender system that coordinates patient sitting tasks such as vital signs monitor and item fetching among several nurses and mobile manipulators. The goal here is to determine the optimal number of robotic assistants per group of patients and nurses. Finally, the team will evaluate the social and economic impact of the technology on nurses, patients, and healthcare facilities. The project will promote the progress of science and advance the national health by providing a blueprint for engineering future nursing assistant robots, for informing healthcare facility design to accommodate the robots, and for advancing instruction on the use of intelligent robotic assistants into formal nursing education, nurse training, and credentialing. Other potential benefits of the project include the development of instructional programs in robotics and machine learning at the University of Louisville, involvement of undergraduate nursing students in this research, and outreach to rural primary care clinics and hospital settings through the Center for Health Systems Innovation at the Oklahoma State University.This project includes four objectives: Behavioral observation, documentation reviews, task inventories and critical incidents will be analyzed to develop a task, skill, and context taxonomy to identify nursing tasks that can be assigned to intelligent robotic nurse assistants; The team will develop two CHRIs and then compare their performance in an assisted walking task with the abilities of human nursing staff. These interfaces utilize neural networks and generic algorithms and adjust to psycho-physiological and tactile signals from users and are designed to allow novice nurses and patients to operate robots with wearable sensors for prevention of falls. The team will then enhance the informational capabilities of the physical CHRIs using collaborative filtering, hybrid recommendation and machine learning techniques. The goal here is to develop an intelligent recommender system that will promote efficiency in the deployment of robotic assistants capable of performing patient sitting tasks such as vital signs monitoring and item fetching. The interfaces in this project will be evaluated by approximately 150 expert and novice users, nursing students and simulated patients to advance understanding of which types of tasks are better assigned to people and which may be delegated to robots in nursing scenarios. Finally, the team will perform economic analyses of the impact of the technology on nursing costs and the skilling needs for future healthcare industry through O*NET databases.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.

“人类技术前沿工作的未来”（FW-HTF）研究项目为护理专业提出了一个愿景，即协作人机界面（CHRIs）可以提高护士的工作效率并减少工作压力。这个项目中的接口被广泛定义为人类和智能机器（如机器人）之间的任何连接，使用先进的传感器、移动计算和显示设备。在这个项目中，接口将智能地“适应”，在未来的医疗环境中为护士和患者提供身体和认知方面的帮助。该项目将追求四个目标。该团队将制定护理任务的分类，以确定哪些任务可以合理地委托给智能机器人。然后，该团队将比较两个新型CHRIs的能力，以促进稳定和有效的人机共享控制机器人辅助行走任务（即防止跌倒）。他们还将研究一个CHRI推荐系统，该系统可以协调患者坐着的任务，如几个护士和移动操作器之间的生命体征监测和物品提取。这里的目标是确定每组病人和护士的机器人助手的最佳数量。最后，该团队将评估该技术对护士、患者和医疗机构的社会和经济影响。该项目将为未来护理助理机器人的工程设计提供蓝图，为适应机器人的医疗设施设计提供信息，并将智能机器人助理的使用指导推进到正规护理教育、护士培训和资格认证中，从而促进科学进步，促进国民健康。该项目的其他潜在好处包括路易斯维尔大学机器人和机器学习教学计划的发展，本科护理专业学生参与这项研究，并通过俄克拉荷马州立大学卫生系统创新中心向农村初级保健诊所和医院机构推广。该项目包括四个目标：行为观察、文档审查、任务清单和关键事件分析，以开发任务、技能和上下文分类，以确定可以分配给智能机器人护士助理的护理任务；该团队将开发两个CHRIs，然后将它们在辅助行走任务中的表现与人类护理人员的能力进行比较。这些接口利用神经网络和通用算法，并根据用户的心理生理和触觉信号进行调整，旨在让新手护士和患者操作带有可穿戴传感器的机器人，以防止跌倒。然后，该团队将使用协同过滤、混合推荐和机器学习技术来增强物理CHRIs的信息能力。这里的目标是开发一个智能推荐系统，该系统将提高部署机器人助手的效率，这些机器人助手能够执行患者坐诊任务，如生命体征监测和物品提取。该项目的界面将由大约150名专家和新手用户、护理学生和模拟患者进行评估，以进一步了解在护理场景中哪些任务最好分配给人类，哪些任务可以委托给机器人。最后，该团队将通过O*NET数据库对该技术对护理成本和未来医疗保健行业技能需求的影响进行经济分析。该奖项反映了美国国家科学基金会的法定使命，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（15）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Lean Health Care Internships: A Novel Systems-Based Practice Education Program for Undergraduate Medical Students

精益医疗保健实习：针对本科医学生的基于系统的新型实践教育计划

DOI：
10.1097/acm.0000000000005312
发表时间：
2023
期刊：
Academic Medicine
影响因子：
7.4
作者：
Erdmann, Marjorie A.;Paramel, Ipe S.;Marshall, Carolyn M.
通讯作者：
Marshall, Carolyn M.

Technology Use During COVID-19 Pandemic: Future Implications for Nursing and Health Care

COVID-19 大流行期间的技术使用：对护理和医疗保健的未来影响

DOI：
10.1097/cin.0000000000000906
发表时间：
2022
期刊：
Nursing
影响因子：
0
作者：
Logsdon, M. Cynthia
通讯作者：
Logsdon, M. Cynthia

Clustering temporal disease networks to assist clinical decision support systems in visual analytics of comorbidity progression

DOI：
10.1016/j.dss.2021.113583
发表时间：
2021-07-07
期刊：
DECISION SUPPORT SYSTEMS
影响因子：
7.5
作者：
Lu, Yajun;Chen, Suhao;Gin, Andrew
通讯作者：
Gin, Andrew

Evolution and impact of bias in human and machine learning algorithm interaction

DOI：
10.1371/journal.pone.0235502
发表时间：
2020-08-13
期刊：
PLOS ONE
影响因子：
3.7
作者：
Sun, Wenlong;Nasraoui, Olfa;Shafto, Patrick
通讯作者：
Shafto, Patrick

Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers

DOI：
10.1145/3534678.3539430
发表时间：
2022-08
期刊：
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
影响因子：
0
作者：
Khalil Damak;Sami Khenissi;O. Nasraoui
通讯作者：
Khalil Damak;Sami Khenissi;O. Nasraoui