Evaluating a Natural Language Processing-Driven, AI-Assisted International Classification of Diseases, 10th Revision, Clinical Modification, Coding System for Diagnosis Related Groups in a Real Hospital Environment: Algorithm Development and Validation Study.

Journal: Journal of medical Internet research
Published Date: Sep 20, 2024

Abstract

BACKGROUND: International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs).

Authors

  • Hong-Jie Dai
    Department of Computer Science and Information Engineering, National Taitung University, Taiwan. Electronic address: hjdai@nttu.edu.tw.
  • Chen-Kai Wang
    Big Data Laboratory, Chunghwa Telecom Laboratories, Taoyuan, Taiwan.
  • Chien-Chang Chen
    Bio-Microsystems Integration Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City, Taiwan.
  • Chong-Sin Liou
    Intelligent System Lab, College of Electrical Engineering and Computer Science, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
  • An-Tai Lu
    School of Post-Baccalaureate Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Chia-Hsin Lai
    Intelligent System Lab, College of Electrical Engineering and Computer Science, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
  • Bo-Tsz Shain
    Intelligent System Lab, College of Electrical Engineering and Computer Science, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
  • Cheng-Rong Ke
    Intelligent System Lab, College of Electrical Engineering and Computer Science, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
  • William Yu Chung Wang
    Waikato Management School, University of Waikato, Hamilton, New Zealand.
  • Tatheer Hussain Mir
    Intelligent System Lab, College of Electrical Engineering and Computer Science, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
  • Mutiara Simanjuntak
    Intelligent System Lab, College of Electrical Engineering and Computer Science, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
  • Hao-Yun Kao
    Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung City 80708, Taiwan.
  • Ming-Ju Tsai
  • Vincent S Tseng
    Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, Taiwan.

Keywords

External Resources

Popular Topics
Recent Journals