LANGaware’s collaboration with Premier, Inc.’s PINC AI™ Applied Sciences to harness real-world data for advanced diagnostics in neurodegenerative and behavioral health diseases

LANGaware, an AI-driven innovator in cognitive health diagnostics, is set to collaborate with Premier, Inc.’s PINC AI™ Applied Sciences (PAS), leveraging real-world data (RWD) for developing therapeutics addressing neurodegenerative and behavioral health disorders.

The partnership will see PAS’ healthcare network and PINC AI’s RWD capabilities combined with LANGaware’s advanced technology to innovate early detection and treatment methods. Recognized for digital cognitive and behavioral health diagnostics, LANGaware will utilize AI technology to analyze speech and assess voice and language biomarkers, to provide insights into the early detection of conditions.

PAS, equipped with a substantial database spanning over 4350 hospitals and 300,000 continuum-of-care providers nationwide, will bring expertise in conducting prospective research using synthetic arms from the PINC AI™ Healthcare Database.

“We are thrilled to partner with PAS and extend the reach of our digital assessment tool to more patients, health systems and life science companies,” stated Vassiliki Rentoumi, Founder and CEO of LANGaware. “Our goal is to revolutionize early detection of cognitive and mental health diseases, supporting novel therapy development as well as earlier intervention by providers, to improve patient outcomes and drive positive changes in the healthcare industry. We are actively seeking partners that share our commitment to transforming patient care.”

Denise Juliano, Group Vice President of Life Sciences at PINC AI Applied Sciences, emphasized the collaboration’s potential impact on addressing conditions like Alzheimer’s, Parkinson’s, and depression, saying, “Neurodegenerative diseases like Alzheimer’s and Parkinson’s and behavioral health conditions such as depression affect millions of people worldwide. The PAS-LANGaware partnership will enable RWD that supports early patient identification and detection – and drives evidence-based decisions by clinicians to improve quality, lower costs, reduce risk and help ensure safety for these patients.”