@inproceedings{nassar-etal-2025-taming,
    title = "Taming the Real-world Complexities in {CPT} {E}/{M} Coding with Large Language Models",
    author = "Nassar, Islam  and
      Lin, Yang  and
      Jin, Yuan  and
      Zhu, Rongxin  and
      Tan, Chang Wei  and
      Zhai, Zenan  and
      Mathur, Nitika  and
      Vu, Thanh Tien  and
      Zhong, Xu  and
      Duong, Long  and
      Li, Yuan-Fang",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.84/",
    pages = "1212--1226",
    ISBN = "979-8-89176-333-3",
    abstract = "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."
}