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April 3, 2024
Paying For AI In Healthcare: Setting The Right Precedent Amidst Growing Use | Health Affairs Forefront
Summary
Mitchell Tang, Kaylee Wilson, and Ateev Mehrotra's article in Health Affairs Forefront, titled "Paying For AI In Healthcare: Setting The Right Precedent Amidst Growing Use," critically examines the challenges and considerations of incorporating artificial intelligence (AI) into healthcare payment systems. It highlights two AI services, fractional flow reserve computed tomography (FFR_CT) and autonomous diabetic retinopathy screening, which exemplify the complexities of reimbursing AI applications in healthcare. The authors point out the discrepancy between the current fee-for-service payment models, which are primarily cost-based, and the unique cost structures and value propositions of AI services. They argue for treating AI company fees as indirect rather than direct practice expenses to more accurately reflect their cost structure and encourage efficient AI pricing models. Additionally, the piece debates the necessity of compensating providers for the time spent analyzing AI results, suggesting that AI tools should ideally improve the efficiency of diagnosis and information synthesis without requiring separate payment for technology use. The authors caution against setting precedents with current AI billing practices that could stifle innovation or encourage overuse, advocating for a payment strategy that incentivizes the adoption of AI based on efficiency and quality improvements rather than direct reimbursement.
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