用大模型解决真实医疗编码中的复杂问题，提升准确率超36%。
Taming the Real-world Complexities in CPT E/M Coding with Large Language Models
- 基于大语言模型构建框架，应对真实医疗场景下的编码难题
- 在真实数据集上比商用系统准确率提升36%以上
- 适合医疗信息化、AI辅助诊疗系统开发者参考
评估与管理（E/M）编码属于当前程序术语（CPT）分类体系，用于记录医生为患者提供的医疗服务。主要用于计费，对医生而言准确编码至关重要，但该任务属于辅助性工作，加重了文书负担。自动化编码可减轻医生负担，提升计费效率，最终改善患者护理质量。然而，诸多现实复杂性使自动编码成为挑战。本文详述关键难点，并提出基于大语言模型的ProFees框架，随后进行系统评估。在专家标注的真实世界数据集上，ProFees相比商用CPT E/M编码系统编码准确率提升超过36%，接近5%优于最强单提示基线，证明其在应对真实复杂性方面的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。