Un-Meetings as tools for translational idea generation: A semantic content analysis of an Opioid Crisis Un-Meeting

Abstract

Abstract Background: Team development and idea generation are key intertwined steps in translational science that need a framework to accommodate unstructured, participatory interactions. To this end, we introduced Un-Meetings to the Clinical and Translational Science Awards (CTSA) Program, innovative events that facilitate cross-disciplinary idea generation and informal discussions between translational scientists, policy makers, community members, advocates, and public health professionals. Here we describe a mixed methods study to characterize the conceptual diversity and clusterization of ideas generated through an Opioid Crisis Un-Meeting. Methods: An Un-Meeting targeting translation science approaches to the opioid crisis were hosted at the University of Rochester Center for Leading Innovation and Collaboration (CLIC). We used semantic analysis and conceptual mapping of keywords to analyze how attendee-led idea generation sessions identified topics for breakout discussions. Results: One hundred and two individuals from 40 institutions proposed 150 unique ideas that were grouped into 23 breakout sessions. Network analysis showed that diverse pools of experts were bridged by topics addressing the complexities of the opioid crisis. Two clusters emerged: (1) systems, contexts, and community engagement, and (2) technologies, innovations, and treatment advancements. Conclusions: The cross-disciplinary nature of topic areas that bridge across thematic communities provide opportunities for CTSA programs to engage and support development of diverse translational teams. Potential opportunities for team building include technological advancements of opioid prevention, treatment, surveillance, systems approaches, and studies focusing on special populations and health disparities. The analysis method here may be useful in identifying naturally emerging teams of experts and community gaps when addressing large problems.