183 Translational Health Informatics Support Service Practices, Challenges, and Facilitators

OBJECTIVES/GOALS: METHODS/STUDY POPULATION: Utilized novel TS evaluation methods and tools: - Translational Science Case Study protocol adapted to examine translational support service practices, barriers and facilitators influencing translational movement. - Translational Science Benefits Model (TSBM) Checklist elements for translational/research impact analysis. Triangulated diverse data sources: - Primary data: semi-structured interviews with translational service stakeholders. - Secondary data: service’s applications, reports, and publications; public stories/news related to their research support; scientific publications; organizational/policy documents; and interviews with research stakeholders featured in published sources. RESULTS/ANTICIPATED RESULTS: Translational challenges include: complexity and constant change of health data; lack of data/informatics literacy amongst researchers; limited appreciation and funding for research data services; silos of functionality and data related to biomedical informatics. Translational facilitators are: the UMN CTSA support; available infrastructure and knowledge base; researchers as the best promoters for services; multidisciplinary collaborations with research/community/healthcare teams; best practice approaches; and learning by doing. The translational/research support service contributes to community and public health, clinical/medical benefits, data literacy, catalyzing data-rich research, and health equity. DISCUSSION/SIGNIFICANCE: The evaluation case study provides evidence and lessons learned related to translational benefits, challenges, and facilitators of a successful translational research support service integrating best informatics practices in clinical research and contributing to health equity improvement.