Industry must engage to ensure patient access to AI-enabled health innovations
AI-enabled technologies have demonstrated enormous potential for health care, fueling advances in areas as varied as drug development, software-as-a-service, and analysis of medical images. Importantly, AI systems can address pressing issues such as health care workforce shortages and improving access for underserved communities. Notwithstanding these promises, health AI technologies continue to face obstacles in reaching patients.
Global developments including the AI Act (EU), the Biden Executive Order on AI (U.S.), and Colorado’s AI Act (the first of its kind at the state level) are industry-agnostic but will likely present unique challenges for AI developers in the health sector, including the possibility of duplicative regulation or conflicting regulatory obligations. Policymakers continue to face a steep learning curve, and industry perspectives are vital to advance appropriate regulations that both foster innovation while protecting patients and users from the negative impacts that can come with the promise of AI.
Regulators of products that contain AI have decades-long experience authorizing and overseeing software algorithms but, as algorithms have become more complex and are increasingly used to diagnose and treat patients, regulators are challenged to keep up with the pace of innovation and regulate the products using the existing frameworks. Further, industry strives to meet the expectations of regulators, which can vary across geographies for the same product; lending support for harmonization measures where possible.
AI developers seeking to commercialize also continue to struggle within the existing legacy coverage and reimbursement pathways. While many stakeholders are urging reform, the current coverage and payment framework requires extensive coordination among multiple stakeholders, raising vexing questions, including: Can existing valuation models and processes be utilized to create appropriate reimbursement rates for AI services? Are there non-traditional payment or other incentive models which may ensure patient access by, for example, permitting temporary transitional coverage or prioritizing preventive care? And, does the innovator have sufficient resources to continue to provide access while acceptable reimbursement is accessed?
Finally, AI enabled systems and tools are dependent on their lifeblood, which is data—data to develop them, data to refine them, data to innovate them, and data to control them. At the same time, the issues around patient data are growing in complexity as regulators, patients, and clinicians become better equipped to understand the challenges and risks of utilizing patient data, especially in the context of AI systems.
Stakeholders with AI-enabled systems and tools currently under development must stay engaged to ensure patient access to the benefits of their innovations. Learn more about Hogan Lovells’ leadership through the AI Healthcare Coalition to engage with policymakers on these issues.