AI-powered data management: Navigating data complexity in clinical trials
The data flood gates have opened wide for clinical trial research. In fact, the amount of data gathered may be more akin to a tsunami or a monsoon. For decades, researchers struggled with a lack of data available in clinical trials; however, they may have received more than they asked for. Research shows that the biopharmaceutical industry generates up to a trillion gigabytes of data annually and clinical trials, one of the principal contributors to these data points, generate an average of up to 3 million data points per trial. This influx of sources can make it challenging to discern relevant from superfluous information, complicating analysis and delaying critical decision-making.
An increase in decentralization paired with expanded collection methods in clinical trials have increased access to and accumulation of data. Information gathered from remote monitoring devices, electronic health records (EHRs), laboratory tests, surveys and questionnaires and third-party databases, all contribute to the data challenge in clinical trials. In reality, the number of touchpoints across clinical trials, from sponsors to clinical research organizations (CROs) to site staff, combined with the complexity and disparity of data sources leads to challenges in ensuring data quality.