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FW-HTF-RL: Collaborative Research: Future expert work in the age of "black box", data-intensive, and algorithmically augmented healthcare

FW-HTF-RL：协作研究：“黑匣子”、数据密集型和算法增强医疗保健时代的未来专家工作

基本信息

批准号：
1928614
负责人：
Oded Nov
金额：
$ 150万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1928614&HistoricalAwards=false
关键词：
FW HTF RL Collaborative Research

项目摘要

The nature of expert work is changing. Technological advances such as artificial intelligence and data science increasingly enable new computerized tools and products that make predictions and recommendations which were previously made by human experts. However, many of these new tools are "black boxes" whose inner workings are often not understood by their users, place demands that create cognitive load, and de-emphasize abstract problem solving. As these technologies are being deployed, there is little understanding of how they affect experts' work practices, perceptions of the value of work, and the expert-client relationship. Foundational research is needed in order to understand and improve work in an age of data-intensive enhanced cognition, especially in healthcare where such new technologies are rapidly changing expert work. This project is expected to transform the future of expert work through a combined redesign of technology, workflow, and interactions. It will lead to: a healthier and better-informed population; efficient deployment of human capabilities in restructured healthcare occupations; healthcare providers reducing the proportion of time spent on repetitive tasks while increasing time devoted to value-adding, meaningful activities; guidelines on design and delivery of cognition-augmenting expert advice; and students who are well versed in cross-disciplinary research on cognition-augmenting technologies in the workplace.The project's goals are: i) to study the relationships between experts, patients, and technologies in a multidisciplinary way; ii) to develop new ways for these technologies to serve experts and clients; and iii) to make expert work more responsive, value-adding, and meaningful. The project includes two strands. In the "Understand" strand, the interactions between experts, clients and cognition-augmenting technologies are examined. In the "Shape" strand, the project lays the foundations for technological and organizational interventions that will make the interactions between experts, clients, and technology more effective and empowering. With a multidisciplinary team including researchers in computer science, human-computer interaction, dynamical systems, and organization alongside with medical clinicians, the project will contribute: i) scalable approaches toward quantifying the benefits and drawbacks of cognition-augmented interactions, as well as measuring information flow in relationships between experts, clients, and cognition augmenting technologies; ii) insights into when, why, and how cognition-augmenting technologies are experienced as expertise enhancing, rather than degrading; iii) data-driven methodologies to predict the effects of technical and organizational interventions on experts' work and experts' interaction with patients; iv) novel tools and workflows for experts and clients to interact with black-box cognition-augmenting technologies; v) modeling how representation of problems can be embedded in expert; and vi) systematic exploration of explanation and dialogue interventions with regard to how they affect experts' work and expert-client relationship.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.

专家工作的性质正在发生变化。人工智能和数据科学等技术进步越来越多地使新的计算机化工具和产品能够做出以前由人类专家做出的预测和建议。然而，这些新工具中的许多都是“黑匣子”，其内部工作原理通常不被用户理解，提出的要求会产生认知负荷，并不强调抽象的问题解决。随着这些技术的部署，人们对它们如何影响专家的工作实践、对工作价值的看法以及专家与客户的关系知之甚少。在数据密集型增强认知的时代，需要进行基础研究，以了解和改进工作，特别是在医疗保健领域，这些新技术正在迅速改变专家的工作。该项目预计将通过技术，工作流程和交互的重新设计来改变专家工作的未来。这将导致：更健康和更知情的人口;在重组的医疗保健职业中有效部署人的能力;医疗保健提供者减少花在重复性任务上的时间比例，同时增加用于增值、有意义活动的时间;关于设计和提供认知增强专家咨询的准则;以及精通工作场所认知增强技术跨学科研究的学生。该项目的目标是：i）以多学科的方式研究专家、患者和技术之间的关系; ii）开发这些技术为专家和客户服务的新方法; iii）使专家的工作更具响应性、增值性和意义。该项目包括两个方面。在“理解”链中，研究了专家、客户和认知增强技术之间的交互。在“形状”链中，该项目为技术和组织干预奠定了基础，这些干预将使专家、客户和技术之间的互动更加有效和赋权。该项目由一个多学科团队组成，包括计算机科学、人机交互、动力系统和组织方面的研究人员以及临床医生，该项目将有助于：i）量化认知增强交互的好处和缺点的可扩展方法，以及测量专家、客户和认知增强技术之间关系的信息流; ii）洞察认知增强技术何时、为何以及如何被体验为专业知识的增强，而不是退化; iii）数据驱动的方法，以预测技术和组织干预对专家工作和专家与患者互动的影响; iv）为专家和客户提供新的工具和工作流程，以与黑盒认知增强技术进行交互; v）建模如何将问题的表示嵌入专家中;系统地探讨解释和对话干预如何影响专家的工作和专家-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。

项目成果

期刊论文数量（31）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Good for the Many or Best for the Few?: A Dilemma in the Design of Algorithmic Advice

对多数人有利还是对少数人有利？：算法建议设计中的困境

DOI：
10.1145/3415239
发表时间：
2020
期刊：
Proceedings of the ACM on Human-Computer Interaction
影响因子：
0
作者：
Dove, Graham;Balestra, Martina;Mann, Devin;Nov, Oded
通讯作者：
Nov, Oded

Evaluating the Effect of a COVID-19 Predictive Model to Facilitate Discharge: A Randomized Controlled Trial.

DOI：
10.1055/s-0042-1750416
发表时间：
2022-05
期刊：
APPLIED CLINICAL INFORMATICS
影响因子：
2.9
作者：
Major, Vincent J.;Jones, Simon A.;Razavian, Narges;Bagheri, Ashley;Mendoza, Felicia;Stadelman, Jay;Horwitz, Leora, I;Austrian, Jonathan;Aphinyanaphongs, Yindalon
通讯作者：
Aphinyanaphongs, Yindalon

Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19