Extracting International Classification of Diseases Codes from Clinical Documentation Using Large Language Models - Simmons, Ashley; Takkavatakarn, Kullaya; McDougal, Megan; Dilcher, Brian; Pincavitch, Jami; Meadows, Lukas; Kauffman, Justin; Klang, Eyal; Wig, Rebecca; Smith, Gordon; Soroush, Ali; Freeman, Robert; Apakama, Donald J.; Charney, Alexander W.; Kohli-Seth, Roopa; Nadkarni, Girish N.; Sakhuja, Ankit (Applied Clinical Informatics, 2025)
Background Large language models (LLMs) have shown promise in various professional fields, including medicine and law. However, their performance in highly specialized tasks, such as extracting ICD-10-CM codes from patient notes, remains underexplored.Objective The primary objective was to evaluate and compare the performance of ICD-10-CM code extraction by different LLMs with that of human coder.Methods We evaluated performance of six LLMs (GPT-3.5, GPT-4, Claude 2.1, Claude 3, Gemini Advanced, and Llama 2-70b) in extracting ICD-10-CM codes against human coder. We used deidentified inpatient notes of authentic patient cases from American Health Information Management Association Vlab for this study. We calculated percent agreement and Cohen's kappa values to assess the agreement between LLMs and human coder. We then identified reasons for discrepancies in code extraction by LLMs in a 10% random subset.Results Among 50 inpatient notes, human coder extracted 165 unique ICD-10-CM codes. LLMs extracted significantly higher number of unique ICD-10-CM codes than human coder, with Llama 2-70b extracting most (658) and Gemini Advanced the least (221). GPT-4 achieved highest percent agreement with human coder at 15.2%, followed by Claude 3 (12.7%) and GPT-3.5 (12.4%). Cohen's kappa values indicated minimal to no agreement, ranging from −0.02 to 0.01. When focusing on primary diagnosis, Claude 3 achieved highest percent agreement (26%) and kappa value (0.25). Reasons for discrepancies in extraction of codes varied among LLMs and included extraction of codes for diagnoses not confirmed by providers (60% with GPT-4), extraction of nonspecific codes (25% with GPT-3.5), extraction of codes for signs and symptoms despite presence of more specific diagnosis (22% with Claude 2.1), and hallucinations (35% with Claude 2.1).Conclusion Current LLMs have poor performance in extraction of ICD-10-CM codes from inpatient notes when compared against the human coder.
Keywords: ICD-10; artificial intelligence; billing; human–computer interaction; billing systems