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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision,

Hong-Jie Dai1,2,3, Chen-Kai Wang1,4,5, Chien-Chang Chen6

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An AI-powered natural language processing system assists with International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding, improving Taiwan diagnosis related groups (Tw-DRGs) assessment and reducing manual workload for certified coding specialists.

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International Classification of DiseasesTaiwan diagnosis related groupsdeep learningelectronic medical recordnatural language processing

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Area of Science:

  • Medical informatics
  • Artificial intelligence in healthcare
  • Natural Language Processing (NLP)

Background:

  • Manual International Classification of Diseases (ICD) coding is complex, time-consuming, and error-prone, especially with the transition to ICD-10.
  • Inaccurate coding leads to financial losses and impacts the accuracy of Taiwan diagnosis related groups (Tw-DRGs).
  • Automated coding systems are crucial for enhancing efficiency and accuracy in medical diagnosis coding.

Purpose of the Study:

  • To evaluate the feasibility of an ICD-10-CM autocoding system using free-text discharge summaries.
  • To assess the system's ability to automatically determine principal diagnoses and major diagnostic categories (MDCs) for Tw-DRGs.

Main Methods:

  • Developed AI-assisted ICD-10-CM coding systems using deep learning models on discharge summaries.
  • Created a web-based user interface and deployed the system for certified coding specialists (CCSs).
  • Assessed system performance using reference and real hospital data for ICD-10-CM coding and Tw-DRG determination.

Main Results:

  • The GPT-2 model achieved the highest F1-score (0.667 overall, 0.851 for top 50 codes) on test data.
  • GPT-2 demonstrated superior agreement with CCSs for Major Diagnostic Categories (MDCs), with an average Cohen's kappa of 0.714.
  • The AI system showed consistent performance, supporting CCSs in ICD-10-CM coding and Tw-DRG assessment.

Conclusions:

  • NLP-driven AI-assisted coding systems can effectively support CCSs in ICD-10-CM coding.
  • The system has the potential to reduce manual workload and expedite Tw-DRG assessment.
  • The developed system demonstrates effectiveness in both ICD-10-CM coding and Tw-DRG judgment.