Saama Unveils New Capabilities for Life Science Analytics


The firm announced three new machine learning-based capabilities that extend the existing functionality of its Life Science Analytics Cloud.

Data analytics company Saama Technologies, Inc. has rolled out three new machine learning-based capabilities to increase the functionality of their Life Science analytics cloud.

The suite includes programs integrated with artificial intelligence including Virtual Assistant/AI, Operational and Financial Risk Mitigation, and Drug Efficacy and Patient Safety Analytics.

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“This trio of new LSAC proficiencies demonstrates Saama’s commitment to strategic, targeted and pragmatic deployment of AI to continually advance clinical operations,” said Malaikannan Sankarasubbu, Vice President of AI Research at Saama. “These exciting new features are the first in a series that will be launched throughout 2019 to exponentially enhance the value of our LSAC platform for Saama’s life science partners. These new features translate into clinical trial time and cost savings and, ultimately, safer and more effective drugs.”

Virtual Assistant/AI

Saama introduced its Deep Learning Intelligent Assistant in 2018. It’s a conversational assistant for LSAC that is domain and context-aware. With its new Virtual Assistant functionality, it can identify the intent of queries to improve conversational user engagement. Its replies can now factor in the names of locations, people and organizations along with times, quantities, percentages, and monetary values, and it can remember the context of previous inquiries. This can all lead to vast improvements in clinical operations insights, with queries about enrollment, financial risk and other key decision making factors included.

Operational and Financial Risk Mitigation

The new Operational and Financial Risk Mitigation is designed to improve the ability to track key clinical trial performance indicators to manage and mitigate operational and financial risks.  An auto-machine learning model will provide the ability to go beyond tracking only planned and actual KPIs, improving decision making. It eliminates the need for teams to run their own analyses, reducing time and cost. LSAC uses historical data from various trial sites and applies the appropriate machine learning algorithms.

Drug Efficacy and Patient Safety Analytics

The LSAC has also been enhanced with a machine learning-based Drug Efficacy and Patient Safety analytics feature that simplifies the process of correlating patient profiles with data variables. Up to 50 variables can now be analyzed simultaneously by LSAC. This gives researchers the ability to identify previously undetectable patient deviations and any corresponding safety issues. Saama says this new functionality can result in approximately 30% savings in clinical trial staff time and effort.

Sue Walsh

About Sue Walsh

Sue Walsh is News Writer for RTInsights, and a freelance writer and social media manager living in New York City. Her specialties include tech, security and e-commerce. You can follow her on Twitter at @girlfridaygeek.

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