Hybrid AI for Automated Clinical Risk Coding
Keywords:
Value-Based Healthcare, Healthcare Risk Adjustment, Hierarchical Condition Categories (HCC), International Classification of Diseases (ICD), Medicare Advantage Risk Models, Generative Artificial Intelligence in Healthcare, Large Language Models for Clinical NLP, Automated HCC Code Identification, Clinical Note Information Extraction, Hybrid LLM–NLP Architectures, Embedding-Based Clinical Classification, Unstructured Clinical Data Processing, Healthcare Payment Optimization, Domain-Specific Language Models, AI-Driven Medical Coding.Abstract
The long-overdue transition of the United States health system toward value-based care has triggered a new demand for healthcare risk adjustment. The chronic medical complexities of at-risk populations are captured and represented through clinical concepts from the International Classification of Diseases (ICD). These codes are prevalent in payment models such as the Medicare Advantage program and the Affordable Care Act marketplace. Medicare Advantage payers receive funding from the Medicare program based on a patient-level risk adjustment prediction model, which is based on Hierarchical Condition Categories (HCC). The accurate and efficient identification of HCC codes in unstructured clinical notes could reduce operational costs while directly benefiting the medical community and patients. However, few solutions address this problem. Generative AI can help address these emerging needs by automating the complex natural language processing (NLP) tasks involved in HCC code identification.
This research study proposes a novel coding architecture that employs generative AI for automatic HCC code identification from clinical notes. A hybrid large language model-natural language processing (LLM-NLP) generative AI architecture is designed; it includes a large language model that generates a set of candidate HCC codes, which is filtered based on the restricted natural language processing of classifiers using a new embedding approach. The novel architecture is applicable to domain-specific tasks requiring extracting relevant concepts from a very large number of entity classes.
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