FDA Collaborates with Cerner Enviza and John Snow Labs to Harness AI and EHR Data Analytics for Pharmacovigilance

by Andrii Buvailo, PhD          Biopharma insight

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Topics: Emerging Technologies   
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Cerner Enviza, an Oracle company, and John Snow Labs have been selected by the FDA's Sentinel Innovation Center to support the Sentinel Initiative, focusing on AI drug discovery and real-world evidence studies. The collaboration aims to develop AI tools that extract vital information from electronic health records (EHR) and clinical notes, helping the FDA better understand the effects of medicines on large populations.

The two-year project, known as the Multi-source Observational Safety Study for Advanced Information Classification Using NLP (MOSAIC-NLP), will focus on the asthma drug montelukast and its potential mental health side effects. The project will demonstrate how machine learning and natural language processing (NLP) technology applied to unstructured data can help address gaps in knowledge.

Cerner Enviza brings to the table its extensive experience in life sciences, spanning commercial, real-world, clinical, and regulatory research. John Snow Labs, on the other hand, is known for its expertise in AI and NLP in healthcare and is the developer of the Spark NLP library. Together, the two AI drug discovery companies will develop a new methodology for enhancing computerized queries or phenotyping of digital patient data and clinical notes, supporting pharmacoepidemiology.

The Sentinel Innovation Center, led by Mass General Brigham and Harvard Pilgrim Health Care Institute, chose Cerner Enviza to spearhead the team. Rishi Desai, Ph.D., of the Sentinel Innovation Center, emphasized the strategic importance of utilizing unstructured EHR data and expressed enthusiasm for the collaboration with Cerner Enviza.

Traditional manual methods for analyzing clinician notes can hinder comprehensive understanding of symptoms and outcomes at the population level. However, AI advancements offer scalable and transportable NLP processes. Mike Kelly, global head of Cerner Enviza, highlighted the potential for connected technologies and unified data to accelerate innovation, ultimately helping providers deliver better recommendations and outcomes for patients.

David Talby, CTO of John Snow Labs, expressed excitement about working with Cerner Enviza in applying NLP to this significant real-world evidence project. The MOSAIC-NLP project is supported by the participation of Children's Hospital of Orange County, National Jewish Health, and Kaiser Permanente Washington Health Research Institute, which will provide clinical expertise and consulting.

 

A broader scope of AI for drug discovery

The emerging role of AI in utilizing EHR for clinical and translational research has the potential to revolutionize the pharmaceutical industry. By harnessing the vast amounts of data contained within EHRs, AI can help identify patterns and trends that may otherwise go unnoticed, leading to new insights and improved patient outcomes. For instance, AI algorithms can analyze EHR data to predict treatment outcomes, or uncover previously unknown correlations between diseases and treatment regimens. In addition to Cerner Enviza and John Snow Labs, companies like Deep 6 AI, Tempus, and Flatiron Health are making strides in leveraging AI for drug discovery and clinical research. These companies use advanced machine learning techniques to mine EHR data, enabling researchers to uncover novel biomarkers, validate hypotheses, and ultimately accelerate the drug development process. 

As AI for drug discovery continues to evolve, collaborations such as the one between Cerner Enviza, John Snow Labs and FDA, have the potential to transform the pharmaceutical landscape, enhancing safety and real-world evidence studies in the process.

 

Topics: Emerging Technologies   

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