000			02343nam a22001697a 4500
999			_c510329 _d510329
008			190811b ||||| |||| 00| 0 eng d
100			_aBhavnani, Suresh K. et al _98186
245			_aTowards team-centered informatics: accelerating innovation in multidisciplinary scientific teams through visual analytics
260			_c2019
300			_ap.50-72.
520			_aA 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. - Reproduced.
650			_aknowledge _98121
650			_aInnovation _98122
773			_aJournal of Applied Behavioral Science
906			_aInterdisciplinary approach
942			_cAR