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

TL;DR

This paper introduces ProFees, a large language model-based framework designed to automate CPT E/M coding, effectively handling real-world complexities and significantly improving coding accuracy over existing systems.

Contribution

ProFees is a novel LLM-based approach that addresses real-world complexities in CPT E/M coding, achieving substantial accuracy improvements.

Findings

ProFees improves coding accuracy by over 36% compared to commercial systems.

ProFees outperforms the strongest single-prompt baseline by nearly 5%.

The framework effectively tackles real-world complexities in medical coding.

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.