Saama embeds new features within its AI platform to speed up treatments reaching patients

By Liza Laws

- Last updated on GMT

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Getty Images

Related tags Saama Technologies Artificial intelligence Clinical trials Data management Patient recruitment Research

Yesterday (January 22), Saama announced the release of several ‘groundbreaking’ features within its AI-driven platform, including generative AI (GenAI) chat and interactive review listings (IRL), that it says create a workflow-driven environment for cross-functional collaboration.

The company uses AI and advanced analytics to automate key clinical development processes in a bid to accelerate time to market by taking out manual, resource-intensive processes.

For the first time, users can ask questions of their data in natural language, and instantly receive responses – eliminating costly data analysis delays. Additionally, data management and medical review teams can now work together in a single system to complete comprehensive data reviews from start to finish.

“Outdated approaches to clinical development are delaying treatments from making it to market, and ultimately patients,” said Lisa Moneymaker, chief technology officer and chief product officer, at Saama.

“These cutting-edge features embedded within the Saama platform create a collaborative environment where AI can quickly surface insights and drive users to action. The result is the breakdown of operational silos and the acceleration of development timelines.”

Changing drug development

Saama says it is fundamentally changing how drug development is done by integrating pioneering innovations into its award-winning platform. New features and enhancements now available include Interactive Review Listings (IRL) so data managers, medical monitors, and vendors can now work together in a single environment to complete comprehensive data reviews from start to finish.

Powered by AI, Saama’s platform allows study teams to access and review the data they need without waiting for programmers. With IRL, users can create custom listings using GenAI, and track all queries in a single location, regardless of source. Users can also collaborate with their teams by assigning tasks, raising, and inspecting queries, and managing vendors all in one place.

Its data quality (DQ) co-pilot is a first-of-its-kind GenAI feature embedded within smart data quality (SDQ) that eliminates the need to manually program, test, and deploy data quality checks. Data managers can describe their desired DQ check, and the DQ Co-Pilot will generate accurate code and test data in seconds.

Another breakthrough GenAI innovation, chat for patient insights allows medical monitors to ask questions about their data in natural languages and receive the data they need in seconds. Medical monitors can quickly create custom listings without programmers or coding knowledge, saving weeks of data analysis.

AI-driven data mapping

Users can now apply different data standards by study, manage blinded datasets, as well as leverage Saama’s advanced, AI-driven data mapping feature for faster data standardization. This makes its data hub an even more powerful tool for centrally managing clinical trial data.

An all-new interface, the company says, allows executives and portfolio managers to quickly drill down from high-level dashboards to study-level data on a single screen. The intuitive interface allows users to easily identify KPIs of interest and brings together data from multiple source systems in a single view.

When using Saama’s innovative solutions, clinical trial sponsors and contract research organizations reduce the time from data capture to query generation by nearly one month and reduce the time to generate a single query by 90%.

“We are committed to fostering innovation so that we may best support the complex needs of, and ultimately deliver value to, our customers,” said Avi Kulkarni, chief customer success and growth officer.

“The continued evolution of the Saama platform provides the foundational tools to modernize R&D and allow clinical development teams to do what they do best – gain insights from data to drive medical breakthroughs.”

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