探索GPT-4在从诊所笔记中创建计费代码方面的潜力
Qingyuan Song1, Yike Li1,2, Bradley A Malin1,2
1Vanderbilt University.
Studies in health technology and informatics
|May 17, 2025
概括
使用GPT-4自动生成医疗计费代码是一项挑战. 在从描述和临床笔记中生成CPT/HCPCS代码时,AI的准确性很低,这表明当前用于此任务的大型语言模型的局限性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床文档 临床文档
背景情况:
- 标准化医疗计费依赖于复杂的编码系统,如CPT/HCPCS.
- 从非结构化的临床笔记中手动编码分配是耗时且容易出现错误的.
- 庞大的代码数量 (超过22000个) 加剧了这一挑战.
研究的目的:
- 评估GPT-4在自动生成CPT/HCPCS计费代码方面的能力.
- 评估GPT-4在从文本描述和现实世界临床笔记中生成代码时的准确性.
主要方法:
- 提示GPT-4从它们的文字描述中生成CPT/HCPCS代码.
- 此外,GPT-4还被要求生成从范德比尔特大学医学中心诊所笔记中获得的信任分数的计费代码.
- 基于准确代码匹配和真实阳性率来评估性能.
主要成果:
- 在从描述中生成精确的CPT/HCPCS代码时,GPT-4实现了20.8%的准确性.
- 该模型显示,从临床笔记中分配正确代码的平均真实阳性率为28.9%.
- 与计费代码一起生成了信任分数.
结论:
- 像GPT-4这样的当前大型语言模型在准确生成医疗计费代码方面面临重大挑战.
- 临床笔记的非结构性和编码系统的复杂性限制了LLM在这个领域的表现.
- 需要进一步的研究来提高AI准确性,以实现自动化医疗编码.
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