Bristol Myers Squibb solves its clinical study data conformance challenge with a machine learning system

Pharma companies often use historical clinical trial data to inform future trials. However, combining legacy studies is a process prone to friction—not only due to the sheer complexity of clinical data but also because legacy studies come in disparate formats and can differ dramatically across therapeutic areas, organizations and study phases. Like its peers, Bristol Myers Squibb (BMS) has a massive repository of historical studies that it mines on an ongoing basis to uncover insights that could guide clinical and medical decisions, but the conformance process became a bottleneck for the company. To overcome this, BMS partnered with ZS to develop a machine learning-based conformance solution that reduced the time required to conform studies, leading to a three-time increase in the number of conformed studies BMS could publish and a savings of $3 million over three years.

White Paper from