Icd coding systems struggle with rare and complex medical cases
Understanding the Limits of Agentic ICD Coding
Computation and LanguageArtificial IntelligenceMachine Learning
Summary
ICD-10-CM codes help classify medical diagnoses but some codes are harder to assign than others. The authors studied how different AI and rule-based systems perform on common and rare codes in hospital records. They found that AI systems often miss rare codes, while workflow-based systems struggle with complex injury codes. An enhanced system that uses official guidelines helped improve coding for the toughest cases, but no system worked best for all types of codes.
What this means in practice
- •For hospital data teams: Improve accuracy in assigning ICD codes for billing and reporting by choosing systems suited to different code complexities.
- •For medical software developers: Develop hybrid coding tools that combine AI and guideline access to better handle complex medical conditions and rare diagnoses.
- •For insurance coding services: Enhance rare and injury code handling by integrating agentic coding approaches with official ICD-10-CM references to reduce claim errors.$Commercial implications: This paper supports building advanced coding products that increase billing accuracy for insurers by improving rare and complex code assignment.
Tested on one dataset.
Authors
Chong Yock Eng, Yushi Cao, Yiming Chen, Kezhi Mao, Hongchao Jiang
Abstract
ICD-10-CM codes are alphanumeric codes used in the US to classify diagnoses and injuries for medical billing and epidemiological reporting. Standard ICD-10-CM benchmarks report aggregate metrics that obscure performance on complex coding scenarios. We evaluate neural, workflow, and agentic systems on a rarity-stratified set of MIMIC-IV discharge summaries and identify two orthogonal failure modes. Neural classifiers exhibit a 0.43 micro-F1 gap between rare and common codes. Workflow systems handle rare codes well but score near zero on injury and external cause codes that require multi-step guideline following. A tool-augmented agentic configuration with structured access to official ICD-10-CM reference materials recovers up to 0.34 micro-F1 on this subset. No single system dominates across all conditions.