Built in collaboration with sponsors, Blur was designed to meet internal and external sharing requirements of large pharmaceutical sponsors, balancing sharing aspirations against constraints. This cooperative approach has led to the design of Blur and its recommendation engine that has propelled Blur to be the most used and trusted tool to accelerate clinical trial sharing.
The risk engine of Blur allows clients to set their own criteria, which dictates how Blur ranks and provides recommendations for transformation. With multiple options presented, users can make informed decisions based on quantifiable risk measurements.
Blur allows sponsors to use the most practical approaches for anonymization; including, anonymization based on study specific population and/or entire therapeutic populations. To accelerate sharing Blur 2.4 now encompasses NLP capabilities, automating many sharing tasks. With quarterly releases and Blur User groups, d-wise and our clients continues to invest and influence Blurs roadmap and enhance automation.