Senior Vice President, Clinical Innovations and Product Management
Amit Gulwadi has helped to introduce never-been-done-before-product offerings that are at the very delicate intersection of science and technology. Amit’s stewardship of Saama’s clinical innovations process has resulted in a number of advances, including three new, machine learning-based capabilities that extend the existing functionality of the company’s Life Science Analytics Cloud (LSAC).
For one, Amit was integral to Saama’s 2019 capabilities expansion of its Deep Learning Intelligent Assistant (DaLIA), a context and domain-aware conversational user interface for LSAC. The capabilities expansion broadened DaLIA’s capacity for identifying the intent (what a researcher would like to do or know) of a query and catapulting the virtual assistant to an enhanced level of conversational user engagement, shifting the human-computer interaction paradigm more than ever before.
Amit also played a key role in advancing LSAC’s ability to track clinical trial key performance indicators (KPIs), enhancing the management and mitigation of operational and financial risks. The advancement focused on operational analytics that use predictive KPIs to show what’s about to happen (related to site activation, patient enrollment, etc.), instead of traditional KPIs that only show what already happened. The predictive KPIs help managers anticipate issues, intervene earlier, and improve oversight, empowering researchers to make subsequent, in-flight decisions about and modifications to a clinical trial before obstacles delay a trial.
Additionally, Amit’s sponsor-side background and experience greatly informed the development of LSAC’s Drug Efficacy and Patient Safety Analytics, which significantly streamlines the time and effort traditionally required to correlate patient profiles with data variables. With effective clinical data management and standardization, upwards of 50 variables can now be analyzed simultaneously by LSAC for immediate identification of patient outliers, versus the current, time-intensive process of trial staff manually examining only a few variables at a time. Saama estimates that this new capability will result in an approximately 30% savings in clinical trial staff time and effort by rendering the need to rely on manual data analysis obsolete.
Simply put, Amit has disrupted the data analytics culture in the life sciences industry and is changing the way the industry operates.