AI Data Normalization: Why Code Sets Still Matter With: Rajiv Haravu
Rajiv Haravu, SVP of Product Management at IMO Health, explains why medical code sets remain essential despite the rise of large language models. While generic LLMs can generate answers, they often lack the specialized context needed for accurate clinical coding and are prone to hallucinations. By combining LLMs with proprietary medical content, structured code sets, and editorial policies, IMO Health significantly improves AI accuracy. Haravu also explores the potential of knowledge graphs to link diagnoses, labs, medications, and procedures, as well as using AI agents to streamline dictionary migrations in EHRs.
Episode Contents:
About the Guest
Rajiv Haravu is SVP of Product Management at IMO Health, specializing in medical terminology, data normalization, and AI integration in healthcare. Learn more at: https://www.linkedin.com/in/rajivharavu/
Key Takeaways
- Generic LLMs struggle with precise medical coding without domain context
- Proprietary data and editorial policies boost AI accuracy
- Knowledge graphs can connect diagnoses, labs, meds, and procedures
Transcript Summary
Why can’t we just use LLMs for medical coding?
Generic LLMs are trained on internet data and lack deep medical semantic understanding. Without domain-specific context, they risk hallucinations and inaccurate coding.
How does IMO Health improve results?
By providing proprietary data, structured code sets, and editorial policies that guide LLM prompts.
Where else can AI help?
AI can expedite knowledge graph creation and dictionary migrations in EHRs, with humans in the loop for low-confidence cases.
More Topics
- AI in Patient Care
- AI in the Healthcare Industry
- AI and Medical Innovation
- Healthcare Ethics and Policy
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About the Series
AI and Healthcare—with Mika Newton and Dr. Sanjay Juneja is an engaging interview series featuring world-renowned leaders shaping the intersection of artificial intelligence and medicine.
Dr. Sanjay Juneja, a hematologist and medical oncologist widely recognized as “TheOncDoc,” is a trailblazer in healthcare innovation and a rising authority on the transformative role of AI in medicine.
Mika Newton is an expert in healthcare data management, with a focus on data completeness and universality. Mika is on the editorial board of AI in Precision Oncology and is no stranger to bringing transformative technologies to market and fostering innovation.