Artificial intelligence (AI) is making significant strides in automating medical coding with large language models (LLMs). Yet, findings from Mount Sinai Health System in New York, reveal that many existing LLM implementations are not yet up to the task, often generating mapping inaccuracies and creating issues with HCC score integration.
Join our webinar, where industry experts will break down the underlying challenges of current LLM applications and discuss how integrating comprehensive clinical terminology can enhance coding precision and improve overall efficiency.
Objectives
Identify LLM limitations regarding mapping errors and weak HCC integration
Use clinical terminology to boost accuracy and reliability
Fine-tune LLMs to improve mapping and cut costs
Featured speaker(s)
Vidhya Sivakumaran, PhD
VP, Clinical Informatics & Terminology, Data Engineering
IMO Health
Jingqi Wang, PhD
SVP, Data Science & Chief AI Architect
IMO Health
This program has been approved for 1.0 continuing education unit(s) with the American Health Information Management Association (AHIMA).