Application of Clinical Department–Specific AI-Assisted Coding Using Taiwan Diagnosis-Related Groups: Retrospective Validation Study
For human coders, with the assistance of the ICD-10-CM AI-assisted coding system, work time is reduced. Additionally, strengthening knowledge in clinical documentation enables human coders to maximize their role. This positions them to become clinical documentation experts, preparing them for further career development. Future research will apply the same method to validate the ICD-10 AI-assisted coding module.
Authors
- Hao-Yun Kao (ORCID: https://orcid.org/0000-0002-9390-9314)
- Ming-Ta Li (ORCID: https://orcid.org/0009-0001-9957-5415)
- Hong-Jie Dai (ORCID: https://orcid.org/0000-0002-1516-7255)
- Chih-Yen Sun
- Ming‐Ju Tsai (ORCID: https://orcid.org/0000-0003-3621-3334)
- Chia-Hsin Lai (ORCID: https://orcid.org/0009-0003-9533-140X)
- Chong-Sin Liou (ORCID: https://orcid.org/0009-0009-8024-9589)
- An-Tai Lu
- Bo-Tsz Shian
Institutions
- University of Science and Technology (YE)
- Kaohsiung Medical University (TW)
- National Health Research Institutes (TW)
- Kaohsiung Medical University Chung-Ho Memorial Hospital (TW)
- National Kaohsiung University of Science and Technology (TW)
Publication Details
- Journal
- JMIR Human Factors
- Published
- 2024-12-01
- DOI
- https://doi.org/10.2196/59961
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.12