FW-HTF-P: Technology as partner for improving the effectiveness of teams of clinicians

This project’s goal is to build a deeper understanding of human-human and human-machine teaming in dynamic healthcare environments, in particular Intensive Care Units (ICUs). Teamwork is an important part of clinical practice and as data-powered technologies expand, human-machine teamwork will become increasingly important as well. Further, there are common needs in human-human and human-machine partnerships around clear and timely communication of context-sensitive data and on developing a shared understanding of the data and its reliability. Clinicians are taught to engage in patient-centered care, but with increases in technology, are being asked to be data-workers as well. The research team will interview groups of frontline clinicians and expert healthcare administrators and technologists to identify issues that arise for clinicians both around managing data and the technological tools they are given. The team will use these results to identify promising directions for future technologies and identify the additional expertise needed for a team that can pursue them. In particular, the team expects that the work will lead to new ideas for to support better remote collaboration and to reduce clinician burnout.

The project starts from known challenges that data-intensive healthcare technologies bring to clinical work: lack of expertise in data management, dealing with noisy and incomplete data, bridging gaps between incompatible systems, adjusting clinical workflows to account for these technologies, and developing shared awareness. The goal is to deepen understanding of these problems from the expertise of multiple fields, taking an iterative, human-centered design approach with a range of healthcare stakeholders, including nurses, doctors, and hospital administrators. Through focus groups, expert panels, prototyping exercises, and workshops, the project team will explore three main categories of potential technical advances: 1) technologies that sense people’s presence, location, and activities in order to support shared access to teams’ real-time status; 2) the potential of machine learning to support data and decision analysis while still being intelligible to human partners; and 3) better designs for presenting sensed and analyzed information that align with work contexts and workflows, in order to support mixed in-person and remote teams. These activities will lead to the development of a refined set of research questions and promising socio-technical interventions, clinical testbeds and partnerships, and an expanded team with the appropriate expertise and management structure to pursue future research in this area.

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.