Taming the Real-world Complexities in CPT E/M Coding with Large Language Models

2025-10-28

AI Summary

This study addresses the clinical challenges of complex Evaluation and Management (E/M) coding, high manual annotation burden, and low billing efficiency. We propose ProFees—a large language model (LLM)-based framework employing multi-step reasoning and structured prompting for CPT coding. Unlike single-step prompting or opaque commercial systems, ProFees explicitly models the multidimensional E/M rules (e.g., history, physical examination, medical decision-making), enabling interpretable and verifiable automated coding. Evaluated on an expert-annotated dataset of real-world clinical documentation, ProFees achieves 89.2% coding accuracy—outperforming leading commercial systems by 36.1% and the best single-prompt baseline by 4.8%. To our knowledge, this is the first work to systematically introduce a structured multi-step reasoning paradigm to E/M coding, significantly improving accuracy, robustness, and clinical trustworthiness.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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. %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.

Problem

• Automating CPT E/M coding to reduce physician documentation burden
• Addressing real-world complexities in medical billing code automation
• Improving coding accuracy using LLM-based framework ProFees

Innovation

• LLM-based framework automates CPT E/M coding
• Addresses real-world complexities in medical coding
• Improves coding accuracy over commercial systems