Saama Launches Industry’s First AI-driven Data Platform to Accelerate Clinical Development

Suite of AI-enhanced SaaS solutions automates routine processes and provides a holistic view of trial operations and patient behaviors in real time

Saama, a provider of AI– and ML-based solutions that accelerate clinical development and commercialization, announced  the launch of its unified platform of SaaS-based products to accompany its existing portfolio of customized solutions and services.

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When using Saama’s advanced solutions, clinical trial sponsors and CROs reduce query identification and generation times by as much as 90% per query, realize up to 50% time savings for study data transformations, reduce time from data entry to analysis by more than 35%, and more.

The new Saama platform deploys its proven artificial intelligence (AI)- and machine learning (ML)-enhanced solutions to automate key clinical development processes and provide a holistic view of trial operations and patient progress in one location.

“Life science companies face significant headwinds in terms of the complexity and cost of conducting clinical research and bringing new treatments to market,” said Vivek Sharma, Chief Executive Officer, Saama. “Saama’s unified platform demonstrates to trial sponsors and CROs the power of automation to drive efficiencies as well as enable greater visibility into clinical data to deliver novel treatments to patients.”

Today’s clinical research and development environment is faced with many challenges, including the cost of developing novel treatments, pressure to bring a greater number of therapies to market faster, the increasing volume and velocity of patient data, and a shortage of talent to bring life-changing therapies to patients who need them.

By applying AI and advanced analytics to key clinical development processes, Saama’s platform eliminates manual, resource-intensive activities, allowing life science organizations to optimize productivity as well as gain deeper insights into patients’ behaviors and their real-time response to treatments.

AI models take years and millions of data points to train accurately. Saama has nearly a decade of AI research with 90+ models built for life sciences and trained on over 300 million data points. These models are embedded directly within the Saama platform, allowing for the fast and seamless deployment of new models within existing products as the platform continues to evolve and scale to meet the needs of customers.

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“We have the ability to fundamentally change the way we run clinical trials – to be more efficient and to transform how we collect and analyze data,” said Lisa Moneymaker, Chief Technology Officer and Chief Product Officer, Saama. “By unifying our product suite into the Saama platform, we have the foundation to support clinical trials in a repeatable and scalable way. We will continue to build on this infrastructure to unleash new products that help to speed new treatments through the drug development process and into the hands of the patients that need them most.”

The Saama platform supports the full spectrum of clinical development with its portfolio of AI-enabled SaaS solutions comprised of:

• Data Hub – the most robust solution available today that centralizes and standardizes clinical, operational, financial, and more data in a single location.
• Operational Insights – provides a holistic view of trial operations, in real time, to all team members and seamlessly visualizes clinical, operational, and financial data, including EDC, CTMS, and eTMF data.
• Patient Insights – centralizes and visualizes patient data in a single location – as well as applies AI to patient data in real time – allowing researchers to predict patient behavior, track progress, and even accelerate clinical signal discovery.
• Source to Submission (S2S) – automates the complex SDTM transformation process with advanced AI/ML allowing sponsors to simplify and accelerate their regulatory submissions.
• Smart Data Quality (SDQ) – automates data cleaning, review, and reconciliation processes for data managers, reducing query generation times by as much as 90% per query.

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