Executive Briefing: A quality transformation initiative
Who is this presentation for?
- Those who work in quality control and assurance, and data management
- Those with an interest in an innovative method for data verification using AI capabilities such as NLP and machine learning
The size and the complexity of regulatory submissions to health authorities are consistently increasing. But the process associated with quality checks hasn’t changed for decades; it’s still manual, laborious, time consuming, and error prone. Data verification of healthcare data is a key part of every quality review and has been a challenging task for humans and machines.
Yael Gozin details how, by utilizing AI capabilities such as NLP and machine learning, Pfizer in partnership with BeaconCure developed a new general method for structured data verification, which mimics the way a human reviewer verifies the table’s accuracy. Furthermore, this technology, allows remarkable accuracy as demonstrated in the latest wet-test results: F1 = 0.98, precision = 0.99, and recall = 0.97.
What you'll learn
- Learn about an innovative approach for developing highly accurate, fast, efficient, and versatile structured data verification by using AI
Yael Gozin is a senior director at Pfizer, where she works with clinical development teams across Pfizer’s portfolio, which raised her interests in the use of technology to improve the processes and data quality associated with clinical development and regulatory submissions. Yael provides technical guidance both in designing and implementing innovative AI solutions to automate quality processes and coaching and mentoring of project teams across different therapeutic areas including oncology, innovative pharma, established products, and consumer products. She holds a doctorate from Swiss Federal Institute of Technology, Zurich (ETH Zurich) in organic chemistry and a master’s degree from the Weizmann Institute in organometallic chemistry.
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