When:
Time: 8:00am PDT, 11:00am EDT, 4:00pm BST, 5:00pm CEST
Duration: 60 minutes
About This Webinar
Health insurance providers rely upon clinician documentation – and therefore review of that data – for many key processes. Artificial intelligence (AI) and natural language processing (NLP) provide mechanisms for these data to be more thoroughly, repeatedly, and accurately analyzed to support in these activities. This webinar will elaborate on the value of NLP in these key areas, as well as discussing the fundamentals and essentials to look out for in an NLP solution.
In this webinar, IQVIA will present how health insurance providers can use clinically intelligent Natural Language Processing to improve compliance and accuracy in Risk Adjustment, improve Stars ratings through improved quality metrics, and drive health equity by identifying member-level social determinants of health.
Attendees Will Learn About
- What is NLP and how can it be used
- NLP for risk adjustment
- Extracting member-level social determinants of health data with NLP
- How NLP can drive improved Stars ratings
Speakers
Calum Yacoubian
Director NLP Healthcare StrategyCalum Yacoubian
Dr. Calum Yacoubian is Director of Healthcare Product & Strategy at Linguamatics, IQVIA. He is a medical doctor who trained and practiced in the UK before moving into medical technology. He has worked with Natural Language Processing in clinical data for over 5 years and is passionate about the potential for data to drive improved patient outcomes.
Celeste Adams
NLP Application Scientist at IQVIACeleste Adams
Dr. Celeste Adams serves Linguamatics, IQVIA as an Application Scientist. She is a pharmacist that has practiced in retail, informatics, and medical terminology. Celeste has over 20 years of experience and has worked with Natural Language Processing (NLP) in clinical data for the past 8 years. Celeste’s work has focused on using terminology and automated strategies such as NLP to facilitate various use cases in clinical practice from population health to disease identification. She has spent the large part of her career in teaching and training users to understand how to ask questions of any data (EHR, literature, social media). She has worked closely with all organizations such as academic medical centers, health care plans, and specialty vendors.
