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BRIEF RESEARCH REPORT article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/frai.2024.1457586

AI Integration in Nephrology: Evaluating ChatGPT for Accurate ICD-10 Documentation and Coding

Provisionally accepted
Yasir Abdelgadir Yasir Abdelgadir 1Charat Thongprayoon Charat Thongprayoon 1Jing Miao Jing Miao 1Supawadee Suppadungsuk Supawadee Suppadungsuk 1,2Justin Pham Justin Pham 3Michael A. Mao Michael A. Mao 4Iasmina M. Craici Iasmina M. Craici 1Wisit Cheungpasitporn Wisit Cheungpasitporn 1*
  • 1 Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Michigan, United States
  • 2 Chakri Naruebodindra Medical Institute (CNMI), Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Samutprakarn, Thailand
  • 3 College of Medicine and Science, Mayo Clinic, Rochester, Minnesota, United States
  • 4 Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL, United States

The final, formatted version of the article will be published soon.

    Background: Accurate ICD-10 coding is crucial for healthcare reimbursement, patient care, and research. AI implementation, like ChatGPT, could improve coding accuracy and reduce physician burden. This study assessed ChatGPT's performance in identifying ICD-10 codes for nephrology conditions through case scenarios for pre-visit testing. Methods: Two nephrologists created 100 simulated nephrology cases. ChatGPT versions 3.5 and 4.0 were evaluated by comparing AI-generated ICD-10 codes against predetermined correct codes. Assessments were conducted in two rounds, two weeks apart, in April 2024. Results: In the first round, the accuracy of ChatGPT for assigning correct diagnosis codes was 91% and 99% for version 3.5 and 4.0, respectively. In the second round, the accuracy of ChatGPT for assigning the correct diagnosis code was 87% for version 3.5 and 99% for version 4.0. ChatGPT 4.0 had higher accuracy than ChatGPT 3.5 (p=0.02 and 0.002 for the first and second round respectively). The accuracy did not significantly differ between the two rounds (P>0.05). Conclusion: ChatGPT 4.0 can significantly improve ICD-10 coding accuracy in nephrology through case scenarios for pre-visit testing, potentially reducing healthcare professionals' workload. However, the small error percentage underscores the need for ongoing review and improvement of AI systems to ensure accurate reimbursement, optimal patient care, and reliable research data.

    Keywords: AI-assisted coding, ICD-10, Nephrology, Healthcare reimbursement, clinical workflow efficiency

    Received: 01 Jul 2024; Accepted: 12 Aug 2024.

    Copyright: © 2024 Abdelgadir, Thongprayoon, Miao, Suppadungsuk, Pham, Mao, Craici and Cheungpasitporn. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wisit Cheungpasitporn, Division of Nephrology and Hypertension, Mayo Clinic, Rochester, 55905, Michigan, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.