AI Medtech Reimbursement: Insights from Recent Literature

Artificial intelligence (AI) is poised to transform the medtech landscape, offering innovations that promise to enhance patient outcomes, streamline operations, and reduce healthcare costs. However, the adoption of AI technologies in healthcare is heavily dependent on securing reimbursement—a complex and often daunting process. This article synthesizes insights from recent literature to explore the key challenges and strategies in AI medtech reimbursement, providing some insights for companies looking to bring their AI innovations to market.

The Reimbursement Challenge: Who Will Pay for AI?

Despite the growing number of FDA-approved AI devices, particularly in fields like radiology, securing reimbursement remains a significant hurdle. As of early 2020, approximately 72% of FDA-approved AI devices were related to radiology, yet many of these innovations struggle to achieve widespread adoption due to reimbursement challenges.

Regulatory approval is a critical first step, but convincing payers to cover these new technologies is an even greater challenge. Payers are often hesitant to reimburse AI technologies without clear evidence of their cost-effectiveness and clinical utility compared to traditional methods. This creates a major barrier to market adoption, as many AI innovations remain stuck in a reimbursement limbo, unable to demonstrate their value in real-world healthcare settings.

Economic Evaluations and Health Technology Assessment (HTA)

To navigate the reimbursement landscape, AI medtech companies must prepare for rigorous economic evaluations via health technology assessments (HTA). These assessments are essential for demonstrating the value of AI technologies to payers, providing evidence that these innovations offer both clinical benefits and cost savings.

A systematic review emphasized the importance of using comprehensive economic models to assess the long-term costs and benefits of AI technologies. These models help predict potential savings and improved outcomes that AI can deliver—critical factors that influence payer decisions on reimbursement. HTAs, in particular, evaluate not only the economic aspects but also the ethical and social implications of adopting AI technologies, ensuring that they align with broader healthcare goals such as improving patient care and reducing disparities.?

A Call for New Reimbursement Models

One significant barrier to the adoption of AI in medtech is the lack of established reimbursement pathways within existing insurance frameworks. AI technologies, particularly those that offer novel diagnostic or therapeutic capabilities, often do not fit neatly into existing fee-for-service categories for reimbursement, leading to coverage delays or denials.

A review of insurance payment methods for AI technologies highlights the need for tailored reimbursement strategies that align with the unique characteristics of AI-driven interventions (for example, many AI medtech business models are based on time-interval subscription). These strategies must address the specific criteria that payers use to evaluate new technologies, including evidence of cost-effectiveness, clinical utility, and the potential to integrate seamlessly into existing care pathways.

For AI technologies to be adopted at scale, sustainable reimbursement models are essential. These models must not only support the financial sustainability of AI innovations but also ensure that they are accessible and beneficial to patients across diverse healthcare settings.

A recent publication in the New England Journal of Medicine emphasizes that without sustainable reimbursement, the potential benefits of AI in healthcare—such as improving patient outcomes, enhancing physician productivity, and reducing costs—cannot be fully realized. The paper calls for a shift from discussing the potential of AI to focusing on actionable adoption at scale.

Recent literature also suggests that value-based reimbursement strategies can help medtech companies secure reimbursement by tying financial incentives to the delivery of high-quality care. For AI technologies, this means that reimbursement will increasingly depend on the ability to provide robust evidence of the technology’s impact on patient outcomes and overall healthcare costs. The shift from fee-for-service to value-based models requires AI developers to focus on long-term value creation and measurable outcomes that resonate with payer expectations.

Strategic Reimbursement Planning: The Importance of Early Engagement

Given the complexity of securing reimbursement, AI medtech companies must adopt a proactive approach by engaging with payers, coding experts, and other stakeholders early in the development process. Early engagement allows companies to understand payer requirements, gather the necessary evidence, and adapt their technologies to meet these criteria.

Incorporating feedback from payers during the early stages of development can help companies avoid common pitfalls, such as the use of inappropriate coding or failure to meet the specific needs of healthcare providers. Strategic planning and early payer engagement are crucial for navigating the reimbursement landscape and ensuring successful market adoption.

Are you ready to tackle the reimbursement challenges for your AI medtech innovation? Schedule a consultative interview with our experts to ensure your technology is positioned for success in the complex healthcare landscape.

Sources:

Abramoff MD et al. Scaling Adoption of Medical AI — Reimbursement from Value-Based Care and Fee-for-Service Perspectives. Published April 12, 2024. NEJM AI 2024;1(5). DOI: 10.1056/AIpc2400083

Alami S, et al. Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity. J Med Internet Res. 2020 Jul; 22(7): e17707. doi: 10.2196/17707: 10.2196/17707

Beck da Silva Etges AP, Liu H, Jones P, Polanczyk CA. Value-based reimbursement as a mechanism to achieve social and financial impact in the healthcare system. JHEOR. 2023:10(2):100-103. doi:10.36469/jheor.2023.89151

Chen MM, et al. Who Will Pay for AI? Radiology: Artificial Intelligence 2021; 3(3):e210030. https://doi.org/10.1148/ryai.2021210030

Coustier N. Reimbursement Déjà Vu: Lessons for AI/ML Medtech Founders from the Molecular Diagnostics Industry. Published May 30, 2024. MedTech Intelligence feature article.

Murray NM, et al. Insurance payment for artificial intelligence technology: Methods used by a stroke artificial intelligence system and strategies to qualify for the new technology add-on payment. Neuroradiol J. 2022 Jun; 35(3): 284–289. doi: 10.1177/19714009211067408: 10.1177/19714009211067408

Parikh RB. Paying for artificial intelligence in medicine. Nature Digital Medicine (2022)5:63 ; https://doi.org/10.1038/s41746-022-00609-6

Venkatesh KP, et al. Leveraging reimbursement strategies to guide value-based adoption and utilization of medical AI. Nature Digital Medicine (2022) 5:112 ; https://doi.org/10.1038/s41746-022-00662-1

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