The US FDA’s Sentinel Innovation Center is partnering with John Snow Labs and Cerner Enviza, an Oracle company, to create AI tools to extract critical information from electronic health records (EHR) notes to improve drug safety. The two-year project will focus on the asthma drug montelukast and its possible mental health side effects. The MOSAIC-NLP project is part of the FDA’s ongoing efforts to leverage data and advanced analytics to enhance the safety of FDA-regulated products, including drugs, medical devices, and food.
The US Food and Drug Administration’s (FDA) Sentinel Innovation Center has announced its collaboration with John Snow Labs and Cerner Enviza, an Oracle company, to create artificial intelligence (AI) tools to extract critical information from electronic health records (EHR) notes to improve drug safety. The goal is to develop a new methodology to support automated queries, or phenotyping, of patient data and clinical notes for pharmacoepidemiology. The two-year project will initially focus on the asthma drug montelukast and its possibility of mental health side effects.
Traditional methods of analyzing clinician notes can be cumbersome for fully understanding the symptoms and outcomes that patients experience at the population level. However, recent advances in AI technology offer a scalable and transportable natural language processing (NLP) process that can transform unstructured clinical notes into validated and usable data for physicians and researchers.
The project, known as the Multi-source Observational Safety Study for Advanced Information Classification Using NLP (MOSAIC-NLP), is also supported by participation from Children’s Hospital of Orange County, National Jewish Health, and Kaiser Permanente Washington Health Research Institute, which will provide clinical expertise and consulting.
“This is an incredible opportunity to work with these exceptional leaders to use Oracle’s de-identified EHR data to help transform unstructured clinical notes into validated and useable data for physicians and researchers,” said Mike Kelly, global head of Cerner Enviza.
Connected technologies and unified data can accelerate innovation and help providers realize better recommendations and outcomes for their patients, added Kelly.
“We are thrilled to team with Cerner Enviza to apply NLP in such an important real-world evidence project,” said David Talby, CTO of John Snow Labs. “We’re honored by the Sentinel Innovation Center’s vote of confidence in our joint ability to help investigate this use case. Together, Cerner Enviza and John Snow Labs have all the right expertise, data, and technology to make it happen.”
The Sentinel Innovation Center aims to enhance the safety and efficacy of FDA-regulated products and protect public health by leveraging real-world data through the FDA Sentinel System. The Center aims to use real-world data to generate new knowledge that can improve patient outcomes and inform regulatory decision-making.
The MOSAIC-NLP project is part of the FDA’s ongoing efforts to leverage data and advanced analytics to enhance the safety of FDA-regulated products, including drugs, medical devices, and food.
Development and evaluation of tools that can enhance our ability to utilize unstructured EHR data is a key strategic priority for the Sentinel Innovation Center, said Rishi Desai, Ph.D., Mass General Brigham executive leadership team member, Sentinel Innovation Center. “We look forward to this new relationship and exciting initiative led by Cerner Enviza.”
The collaboration between the FDA’s Sentinel Innovation Center, John Snow Labs, and Cerner Enviza represents a significant step forward in the use of AI and NLP to improve drug safety by extracting critical information from unstructured EHR notes. This innovative approach has the potential to transform the way clinicians and researchers understand patient outcomes at the population level and could lead to new insights into the safety and efficacy of drugs and medical devices.
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