Challenges
To flourish, translational research requires a knowledge-driven ecosystem, in which constituents generate, contribute, manage and analyze data available from all parts of the landscape. The goal is a continuous feedback loop to accelerate the translation of data into knowledge. Collaboration, data sharing, data integration and standards are very important. Only by seamlessly structuring and integrating these data types will the complex and underlying causes and outcomes of illness be revealed, and effective prevention, early detection and personalized treatments be realized.
Translational research requires that information and data flow from hospitals, clinics and study participants in an organized and structured format, to repositories and laboratories. Also, the scale, scope and multi-disciplinary approach that translational research requires means a new level of operations management capabilities within and across studies, repositories and laboratories. Meeting the increased operational requirements of larger studies, with ever increasing specimen counts, larger and more complex systems biology data sets, and government regulations, requires informatics that enables the integration of both operational capabilities and clinical and basic data. Most informatics systems today are inadequate to handle the tasks of complicated operations and contextually in data management and analysis.
Read more about this topic: Translational Research
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