Parikh Ravi B, Helmchen Lorens A
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA.
NPJ Digit Med. 2022 May 20;5(1):63. doi: 10.1038/s41746-022-00609-6.
Over the past 7 years, regulatory agencies have approved hundreds of artificial intelligence (AI) devices for clinical use. In late 2020, payers began reimbursing clinicians and health systems for each use of select image-based AI devices. The experience with traditional medical devices has shown that per-use reimbursement may result in the overuse use of AI. We review current models of paying for AI in medicine and describe five alternative and complementary reimbursement approaches, including incentivizing outcomes instead of volume, utilizing advance market commitments and time-limited reimbursements for new AI applications, and rewarding interoperability and bias mitigation. As AI rapidly integrates into routine healthcare, careful design of payment for AI is essential for improving patient outcomes while maximizing cost-effectiveness and equity.
在过去7年里，监管机构已批准数百种人工智能（AI）设备用于临床。2020年末，医保机构开始为选定的基于图像的AI设备的每次使用向临床医生和医疗系统进行报销。传统医疗设备的经验表明，按次报销可能会导致AI的过度使用。我们回顾了当前医学中AI的付费模式，并描述了五种替代和补充性报销方法，包括激励结果而非使用量、利用预先市场承诺以及对新AI应用进行限时报销，以及奖励互操作性和减少偏差。随着AI迅速融入常规医疗保健，精心设计AI的付费方式对于改善患者治疗效果、同时实现成本效益和公平性最大化至关重要。
