Johns Hopkins engineers awarded NSF grant to investigate human-machine teaming in health care
The list of U.S. health care challenges is long and costly. The country spends more than $3 trillion dollars on healthcare each year – and the number is climbing. Another pressing issue is that medical error is the third leading cause of death in the U.S., according to a 2016 Johns Hopkins study.
With a $1.5 million award from the National Science Foundation, Johns Hopkins computer scientists will explore how human-machine teaming can help mitigate some of the biggest problems facing the U.S health care system.
Suchi Saria, John C. Malone Assistant Professor of Computer Science is leading the grant, titled “Human-Machine Teaming for Medical Decision Making.” The research team includes fellow John C. Malone Assistant Professor Chien-Ming Huang.
According to the researchers, machine intelligence presents opportunities to increase human work productivity and quality. The team will investigate effective teaming between humans and intelligent machines, similar to effective human-human teamwork, with a special focus on how human-machine teaming can be applied to medical decision making.
Awarded by NSF’s “Future of Work at the Human Technology Frontier” program, the project aims 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 proposed approach for human-machine teaming.
Medical errors often occur because health care providers must deal with overwhelming workloads, time pressures and constraints, and uncertainties in medical conditions. Ultimately, the research team hopes the NSF-funded project will lead to a new model of patient care in which care providers team with intelligent cognitive assistants to enhance quality of care and reduce medical errors.