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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Implementation and Evaluation of ICD-10 Artificial Intelligence Coding Assistance System.

Ming-Chuan Kuo1,2, Yi-Jhen Lin1, Yi-Min Lee3

  • 1Cathay General Hospital, Taipei City, Taiwan.

Studies in Health Technology and Informatics
|August 8, 2025
PubMed
Summary

The International Statistical Classification of Diseases, 10th Revision (ICD-10) presents coding challenges. This study introduces an automated system using deep learning and NLP to improve medical coding accuracy and efficiency.

Keywords:
Artificial IntelligenceICD-10 Artificial Intelligence Coding Assistance SystemTW-DRGs

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Area of Science:

  • Medical Informatics
  • Health Information Systems
  • Artificial Intelligence in Healthcare

Background:

  • The World Health Organization's International Statistical Classification of Diseases, 10th Revision (ICD-10) is the global standard for disease classification.
  • Taiwan's adoption of ICD-10 in 2016, with its extensive ~150,000 codes, increased complexity compared to ICD-9 (~17,000 codes).
  • Increased complexity leads to physician workload challenges and potential underestimation of patient risk factors due to incomplete diagnoses.

Purpose of the Study:

  • To develop an automated disease coding recommendation system.
  • To leverage deep learning and natural language processing (NLP) for enhanced medical coding.
  • To improve the quality and efficiency of medical record documentation and reduce coder workload.

Main Methods:

  • Implementation of deep learning algorithms for pattern recognition in medical data.
  • Application of natural language processing (NLP) techniques to interpret clinical narratives.
  • Development of a recommendation system to assist physicians with ICD-10 coding.

Main Results:

  • The proposed system aims to assist physicians in making accurate coding decisions.
  • It is expected to optimize Health Information System (HIS) interface design for better usability.
  • Anticipated outcomes include improved documentation completeness and reduced physician time spent on coding.

Conclusions:

  • An automated ICD-10 coding recommendation system can address the challenges posed by complex coding standards.
  • The integration of AI technologies can significantly alleviate the workload associated with medical claims.
  • This approach promises to enhance the overall quality and efficiency of healthcare data management.