Today, clinical operations teams are drowning in data but starving for information, at a time of intense pressure to speed up clinical trials and restrain costs (1). 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budgets, primarily due to the inefficient ways in which operational data are captured and analysed, often relying on outdated methods such as Excel. Excel was not designed to collect and analyse clinical trial data as it lacks project management capability, yet its extensive use persists (2-3).
Having technology that can automate or assist in the timely monitoring of trials is a huge improvement over the current status quo of manual methods such as spreadsheets, which are cumbersome and erroneous, not to mention they only provide a dated view of trial performance.
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