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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Large language models vs human for classifying clinical documents.

Akram Mustafa1, Usman Naseem2, Mostafa Rahimi Azghadi1

  • 1College of Science and Engineering, James Cook University, Townsville, 4811, QLD, Australia.

International Journal of Medical Informatics
|January 23, 2025
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Summary
This summary is machine-generated.

Large language models like ChatGPT 4 show promise in improving International Classification of Diseases (ICD-10) coding accuracy for challenging medical records. While human coders remain superior, ChatGPT 4 achieved comparable median performance, suggesting potential for enhanced clinical documentation.

Keywords:
ChatGPTClinical codingClinical document improvementLarge language modelMachine learningSnomed

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Documentation Improvement

Background:

  • Accurate International Classification of Diseases (ICD-10) coding is vital for clinical documentation.
  • Machine learning and Systematized Nomenclature of Medicine (SNOMED) mapping show potential but struggle with false negatives.
  • Challenges persist in correctly identifying all diagnoses, particularly in complex cases.

Purpose of the Study:

  • To explore the efficacy of advanced large language models (LLMs) in improving ICD-10 classification accuracy.
  • To address limitations of current machine learning and SNOMED mapping in challenging medical records.
  • To evaluate LLMs' performance on cases previously identified as false negatives.

Main Methods:

  • Assessed ChatGPT 3.5 and ChatGPT 4 performance on ICD-10 code classification from discharge summaries.
  • Utilized 802 challenging discharge summaries from the Medical Information Mart for Intensive Care (MIMIC) IV dataset (false negatives from prior methods).
  • Compared LLM outputs against evaluations from five experienced human coders on a subset of 100 summaries.

Main Results:

  • ChatGPT 4 exhibited significantly higher consistency (86-89%) compared to ChatGPT 3.5 (57-67%).
  • Overall human coders outperformed ChatGPT, but ChatGPT 4 matched median human coder accuracy at 22%.
  • ChatGPT 4's classification accuracy varied across different ICD-10 codes.

Conclusions:

  • Advanced language models, particularly ChatGPT 4, demonstrate potential for enhancing clinical coding accuracy.
  • Integrating LLMs with existing methods like SNOMED mapping may improve documentation for complex cases.
  • ChatGPT 4 offers improved consistency and comparable median performance to human coders in challenging ICD-10 classification scenarios.