Joe Ehrline, Vice President of Sales
Saama Technologies was already a leading clinical data analytics company, but it continued to make its mark in the sector with the launch of its Life Science Ecosystem for its growing partner network. The Life Science Ecosystem helps partner companies leverage Saama solutions without having to replace existing vendors. Understanding that most companies are reluctant to go for clinical data analytics transformations due to the down-time, high price tag, and significant learning curve, Saama was committed to pioneering a way for customers to leverage Saama’s Life Science Analytics Cloud (LSAC) without abandoning their existing infrastructure of vendors and other partners.
The Ecosystem empowers clinical research organizations (CROs) to leverage LSAC to automate their prospective/retrospective studies and regulatory processes into data-driven, consistent, quality-by-design processes. Clinical and real-world data providers use LSAC’s capabilities to enable customers to generate data insights faster than ever before. Clinical trial management software (CTMS) and electronic data capture (EDC) systems providers engage LSAC to translate systems of records to systems of insights in real time—thereby demonstrating value to customers. Ultimately, this type of three-way partnership between a data analytics solutions provider, trial sponsor, and the sponsor’s existing vendors could be the key to redefining the clinical trial process.
This year, Saama also launched DaLIA (Deep Learning Intelligent Assistant), an AI-based virtual assistant that provides easy-to-use, context, and domain-aware conversational experiences with key data and insights from LSAC. This alternative to the traditional user experience with a keyboard and mouse is specifically designed to simplify the planning, feasibility, and conduct challenges inherent in and pervasive throughout the drug development continuum. Saama is currently deploying DaLIA in pilot programs at multiple pharmaceutical customers to support clinical operations and optimize trial site selection.