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

作者：Hong-Jie Dai, Chen-Kai Wang, Chien-Chang Chen, Chong-Sin Liou, An-Tai Lu, Chia-Hsin Lai, Bo-Tsz Shain, Cheng-Rong Ke, William Yu Chung Wang, Tatheer Hussain Mir, Mutiara S. Simanjuntak, Hao-Yun Kao, Ming‐Ju Tsai, Vincent S. Tseng · 发表于：Journal of Medical Internet Research · 年份：2024 · DOI：10.2196/58278 · 被引用次数：21 · 研究领域：Medical Coding and Health Information、Machine Learning in Healthcare、Biomedical Text Mining and Ontologies

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). OBJECTIVE: This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs). METHODS: By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constru...