在基于算法的医疗程序和服务时代,心脏CT的编码和覆盖范围
Cara Santillo1, Kirsten Tullia2, Richard A Frank3
1HeartFlow, Inc, MPH, Health Management & Policy, USA.
Journal of cardiovascular computed tomography
|September 17, 2024
概括
为了确保患者从心脏CT创新中受益,医疗保健编码,覆盖和支付系统必须适应基于算法的医疗保健程序和服务 (ABHS). 人工智能分类学促进了经过验证的ABHS的报销,不断变化的支付政策解决了临床采用挑战.
科学领域:
- 医疗信息学 医疗信息学
- 卫生经济学 卫生经济学
- 心血管成像 - 心血管成像
背景情况:
- 心脏CT创新正在迅速发展.
- 当前的医疗保健报销结构可能无法充分解决基于算法的新型医疗保健程序和服务 (ABHS).
- 编码,覆盖和支付的调整对于患者的利益至关重要.
研究的目的:
- 概述调整医疗保健补偿的必要性,以适应心脏CT中的基于算法的新型医疗保健程序和服务 (ABHS).
- 突出AI分类学在为验证的ABHS提供补偿方面的作用.
- 讨论临床实践中ABHS的不断变化的支付政策.
主要方法:
- 审查医疗程序当前的编码和支付系统.
- 对CPT代码集附录S的分析",AI分类学".
- 检查新型医疗保健服务的不断变化的支付政策,包括OPPS和PFS.
主要成果:
- 人工智能分类学提供了一个框架,用于创建不同的代码,以偿还临床验证和FDA标记的ABHS.
- 门诊患者预期支付系统 (OPPS) 和医生费用表 (PFS) 的支付政策正在更新.
- 这些更新旨在适应在临床环境中采用ABHS的独特方面.
结论:
- 编码,覆盖和支付的调整对于实现心脏CT创新的好处至关重要.
- 人工智能分类是对基于算法的医疗保健程序和服务的报销的关键推动因素.
- 不断发展的支付政策对于先进的心脏CT技术的成功临床整合至关重要.
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