Chang Wei Tan


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2025

pdf bib
Taming the Real-world Complexities in CPT E/M Coding with Large Language Models
Islam Nassar | Yang Lin | Yuan Jin | Rongxin Zhu | Chang Wei Tan | Zenan Zhai | Nitika Mathur | Thanh Tien Vu | Xu Zhong | Long Duong | Yuan-Fang Li
Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track

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. 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.