FW-HTF: Human-Machine Teaming for Medical Decision Making

人类技术前沿的未来工作（FW-HTF）是NSF宣布的10个未来投资新想法之一。FW-HTF跨董事会计划旨在通过支持融合研究来应对不断变化的就业和工作环境的挑战和机遇。这个奖项实现了这个目标的一部分。人工智能的巨大进步正在改变包括交通、金融、国家安全和医学在内的各个领域的人类工作。机器智能提供了通过增强人类能力来提高人类工作效率和工作质量的机会。人类和智能机器之间的有效合作，类似于有效的人与人的合作，有可能产生显着的近期收益。该项目探讨了人机合作在医疗决策中的挑战。医疗保健是美国面临的最大挑战之一。美国每年在医疗保健上花费3万亿美元，而医疗差错是第三大死亡原因。人机认知团队创建了一种新的患者护理模式，其中提供者与智能认知助理合作，以在时间压力，繁重的工作量和医疗条件的不确定性下提高护理质量。本项目旨在探索有效的人机合作以缓解医疗保健领域的挑战性问题的潜力。具体而言，本项目旨在了解（1）人机合作是否有助于医疗决策和其他相关高风险领域的决策;（2）设计有效人机团队的指导原则;（3）目前存在的构建此类团队的障碍;（4）人机合作是否有助于医疗决策和其他相关高风险领域的决策。（4）为了发展高绩效团队，需要解决障碍的新解决方案;（5）计划中的人机合作方法的经济和社会影响。了解有效的人机合作，包括在工作空间和人类工作流程中的更广泛影响，将有助于人类工作的积极转变。特别是，预计这一项目的成果将提高医院的利用率，减少医疗差错。该项目整合了多个学科的观点，包括计算机科学，医学专业知识，卫生政策和决策。该研究的影响将扩展到巴尔的摩地区的多家医院。此外，该项目将吸引当地高中生参与暑期研究体验，研究成果将纳入本科课程。该奖项反映了NSF的法定使命，并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，被认为值得支持。

The Future of Work at the Human-Technology Frontier (FW-HTF) is one of 10 new Big Ideas for Future Investment announced by NSF. The FW-HTF cross-directorate program aims to respond to the challenges and opportunities of the changing landscape of jobs and work by supporting convergent research. This award fulfills part of that aim. Algorithmic advances in artificial intelligence are transforming human work in diverse areas including transportation, finance, national security, and medicine. Machine intelligence presents opportunities to increase human work productivity and the quality of jobs through augmenting human capabilities. Effective teaming between humans and intelligent machines similar to effective human-human teamwork has the potential to yield significant near-term gains. This project explores the challenges of human-machine teaming in medical decision making. Health care is one of the most difficult challenges that the United States is facing. The US spends $3 trillion dollars in health care each year, while medical error is the third leading cause of death. Human-machine cognitive teaming creates a new model of patient care in which providers team with intelligent cognitive assistants to enhance quality of care under time pressure, taxing workloads, and uncertainties in medical conditions. This project explores the potential for effective human-machine teaming to mitigate such challenging problems in health care.Specifically, this project seeks to understand (1) whether human-machine teaming can benefit medical decision making and decision making in other related high stakes domains; (2) the guiding principles for designing effective human-machine teams; (3) barriers that currently exist for building such teams; (4) novel solutions needed to address barriers in order to develop highly performant teams; and (5) the economic and societal impacts of the planned approach for human-machine teaming. Understanding effective human-machine teaming, including the broader implications in the workspace and in human workflows, will contribute to positive transformation of human work. In particular, it is anticipated that the outcomes of this project will result in improvements in hospital utilization and reduction of medical errors. The project integrates multiple disciplinary perspectives, including computer science, medical expertise, health policy, and decision making. The impacts of the research will extend to multiple hospitals in the Baltimore region. Furthermore, the project will engage local high school students in summer research experiences, and the outcomes of the research will be integrated into undergraduate curricula.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.