Augmenting BrAIn Power

Today clinical operations teams are drowning in data but starving for information, at a time of intense pressure¹ to speed clinical trials and restrain costs. The massive volumes of data generated during clinical trials are woefully inadequate at helping stakeholders spot risk factors and bottlenecks that can disrupt cycle times and budgets, primarily due to the inefficient ways in which operational data is captured and analyzed, often relying on outdated methods such as Excel. Excel was not designed to collect and analyze clinical trial data. It lacks project management capability, yet its extensive use persists^.2,3

Machine learning has the potential to help by revealing clinical trial patterns, identifying risks and predicting outcomes.