4.2 Article

Towards Team-Centered Informatics: Accelerating Innovation in Multidisciplinary Scientific Teams Through Visual Analytics

Journal

JOURNAL OF APPLIED BEHAVIORAL SCIENCE
Volume 55, Issue 1, Pages 50-72

Publisher

SAGE PUBLICATIONS INC
DOI: 10.1177/0021886318794606

Keywords

visual analytics; computational evolving boundary objects; biomedical insights; multidisciplinary scientific teams; team-centered informatics

Funding

  1. Institute for Translational Sciences at the University of Texas Medical Branch
  2. Clinical and Translational Science Award from the National Center for Advancing Translational Sciences, National Institutes of Health [UL1 TR001439]
  3. Patient-Centered Outcomes Research Institute [ME-1511-33194]
  4. National Library of Medicine of the National Institutes of Health award [R01 LM012095]

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A critical goal of multidisciplinary scientific teams is to integrate knowledge from diverse disciplines for the purpose of developing novel insights and innovations. For example, multidisciplinary translational teams (MTTs) which typically include physicians, biologists, statisticians, and informaticians, aim to integrate biological and clinical knowledge leading to innovations for improving health outcomes. However, such teams face numerous barriers in integrating multidisciplinary knowledge, which is further exacerbated by the explosion of molecular and clinical data generated from millions of patients. Here, we explore the use of a visual analytical representation to help MTTs integrate molecular and clinical data with the goal of accelerating translational insights. The results suggest that the visual analytical representation functioned as a computational evolving boundary object which (a) evolved through several emergent states that progressively helped integrate diverse disciplinary knowledge, (b) enabled team members to play primary and supportive roles in evolving the data representation resulting in a more egalitarian team structure, and (c) enabled the team to arrive at novel translational insights leading to domain and methodology publications. However, the interventions also revealed limitations in the approach motivating new visual analytical approaches. These results suggest (a) implications for theory related to modeling computational evolving boundary objects (CEBOs) as an instance of team-centered informatics, and (b) implications for practice related to the design and use of interactive features that enable teams to fluidly evolve CEBOs through emergent states, with the goal of deriving novel insights from large multiomics datasets.

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