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Global Spine J
Global Spine J
spgsj
GSJ
Global Spine Journal
2192-5682
2192-5690
SAGE Publications Sage CA: Los Angeles, CA

10.1177_21925682241230084
10.1177/21925682241230084
Letters to the Editor
Letter to the Editor, “Artificially Intelligent Billing in Spine Surgery: An Analysis of a Large Language Model”
Kong Xiuhua BS 1
Wang Lingling BS 2
https://orcid.org/0009-0001-3039-013X
Liu Changhua MS 1
1 Department of Orthopaedic Surgery, 85114 The First People’s Hospital of Xiaoshan District , Xiaoshan Affiliated Hospital of Wenzhou Medical University , Hangzhou, China
2 Department of General Practice, Community Health Service Center of Ningwei Jiedao of Xiaoshan , Hangzhou, China
Changhua Liu, Department of Orthopaedic Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, No.199, South City Road, Hangzhou 311200, China. Email: lch35101@163.com
24 1 2024
6 2024
14 5 16841684
© The Author(s) 2024
2024
AO Spine, unless otherwise noted. Manuscript content on this site is licensed under Creative Commons Licenses
https://creativecommons.org/licenses/by-nc-nd/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

typesetterts10
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pmcDear Editor,

I am writing to express my appreciation for the insightful evaluation of the manuscript titled “Artificially Intelligent Billing in Spine Surgery: An Analysis of a Large Language Model" by Bashar Zaidat et al 1 published in the Global Spine Journal. The study is a retrospective cohort study that evaluates the effectiveness of the ChatGPT-4 large language model in predicting Current Procedural Terminology (CPT) codes from surgical operative notes. It demonstrates the potential of ChatGPT-4 to reduce the workload of health care professionals, enhance billing efficiency and accuracy, and decrease health care expenditures.

In addition to the evaluation provided in the manuscript, we would like to offer additional insights into the implications and future directions of this research. Firstly, the study’s findings have significant implications for the health care industry, especially in addressing the resource-intensive nature of CPT coding and the potential to reduce administrative expenses. Integrating AI-powered NLP machine learning algorithms, such as ChatGPT-4, into health care systems has the potential to streamline billing processes, reduce coding errors, and enhance overall operational efficiency. Furthermore, the study’s approach to prompt engineering and the comparison of different trials provide valuable insights into the performance of large language models in the context of health care billing. The findings highlight the importance of fine-tuning models and the potential for further advancements in automating billing processes through AI and NLP technologies. Looking ahead, future research in this area could explore the integration of real-time clinical data into large language models, the development of interfaces for seamless integration with existing health care systems, and the fine-tuning of models.

In conclusion, this study makes a significant contribution to the field of AI-assisted billing in health care. Its findings have the potential to drive transformative changes in the health care industry’s billing processes. I commend the authors for their thorough evaluation and insightful analysis. I am eager to witness further advancements in this research area.

ORCID iD

Changhua Liu https://orcid.org/0009-0001-3039-013X
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Reference

1 Zaidat B Lahoti YS Yu A , et al. Artificially intelligent billing in spine Surgery: an analysis of a Large Language Model. Global Spine J 2023;21925682231224753.38147047
