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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.25007 |
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| _version_ | 1866917049154928640 |
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| author | Nassar, Islam Lin, Yang Jin, Yuan Zhu, Rongxin Tan, Chang Wei Zhai, Zenan Mathur, Nitika Vu, Thanh Tien Zhong, Xu Duong, Long Li, Yuan-Fang |
| author_facet | Nassar, Islam Lin, Yang Jin, Yuan Zhu, Rongxin Tan, Chang Wei Zhai, Zenan Mathur, Nitika Vu, Thanh Tien Zhong, Xu Duong, Long Li, Yuan-Fang |
| contents | Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for billing purposes, it is in physicians' best interest to provide accurate CPT E/M codes. %While important, it is an auxiliary task that adds to physicians' documentation burden. Automating this coding task will help alleviate physicians' documentation burden, improve billing efficiency, and ultimately enable better patient care. However, a number of real-world complexities have made E/M encoding automation a challenging task. In this paper, we elaborate some of the key complexities and present ProFees, our LLM-based framework that tackles them, followed by a systematic evaluation. On an expert-curated real-world dataset, ProFees achieves an increase in coding accuracy of more than 36\% over a commercial CPT E/M coding system and almost 5\% over our strongest single-prompt baseline, demonstrating its effectiveness in addressing the real-world complexities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25007 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Taming the Real-world Complexities in CPT E/M Coding with Large Language Models Nassar, Islam Lin, Yang Jin, Yuan Zhu, Rongxin Tan, Chang Wei Zhai, Zenan Mathur, Nitika Vu, Thanh Tien Zhong, Xu Duong, Long Li, Yuan-Fang Artificial Intelligence Machine Learning Evaluation and Management (E/M) coding, under the Current Procedural Terminology (CPT) taxonomy, documents medical services provided to patients by physicians. Used primarily for billing purposes, it is in physicians' best interest to provide accurate CPT E/M codes. %While important, it is an auxiliary task that adds to physicians' documentation burden. Automating this coding task will help alleviate physicians' documentation burden, improve billing efficiency, and ultimately enable better patient care. However, a number of real-world complexities have made E/M encoding automation a challenging task. In this paper, we elaborate some of the key complexities and present ProFees, our LLM-based framework that tackles them, followed by a systematic evaluation. On an expert-curated real-world dataset, ProFees achieves an increase in coding accuracy of more than 36\% over a commercial CPT E/M coding system and almost 5\% over our strongest single-prompt baseline, demonstrating its effectiveness in addressing the real-world complexities. |
| title | Taming the Real-world Complexities in CPT E/M Coding with Large Language Models |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.25007 |