通过固定支付系统来催化健康人工智能
Narges Razavian1, Prem Batchu-Green2, Vikas Chowdhry3,4
1Center for Healthcare Innovation and Delivery Science, Population Health and Radiology Departments, New York University Langone Health, New York, NY, USA.
NEJM AI
|February 16, 2026
概括
医疗保健人工智能 (AI) 的采用被监管和经济问题所阻碍,而不是创新. 需要进行政策改革,以克服报销障碍,并整合人工智能工具,以改善患者护理和减少临床医生的负担.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 卫生经济学 卫生经济学
背景情况:
- 人工智能 (AI) 在各个领域迅速发展,但医疗保健的采用仍然有限.
- 这种停滞源于监管和经济的错误调整,而不是缺乏数据,临床需求或创新.
- 即使是FDA批准的人工智能工具也面临着因过时的退款和分散的利益相关者激励措施而面临的采用障碍.
研究的目的:
- 检查卫生人工智能工具的报销格局.
- 识别阻碍医疗保健中采用人工智能的支付障碍.
- 提出政策改革,以更好地整合人工智能和生成性人工智能到医疗保健中.
主要方法:
- 对当前医疗人工智能报销框架的分析.
- 识别监管途径中的瓶,例如采用当前程序术语 (CPT).
- 讨论政策改革,包括定价模式和整合开销.
主要成果:
- 现有的报销框架为在医疗保健中采用有效的人工智能工具创造了重大障碍.
- 过时的支付结构和错误的激励措施阻止了部署可以提高效率和患者结果的AI解决方案.
- 医疗保健中的生成人工智能需要紧急开发潜在的监管框架.
结论:
- 政策改革至关重要,以克服卫生AI的报销和监管障碍.
- 解决CPT采用,整合成本和定价模型的问题可以促进AI部署.
- 生成型人工智能需要积极的监管策略,以确保患者受益和成功的医疗保健转型.
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