In recent years, the digitalization of the healthcare industry has been accelerated to meet demands for smarter devices and robotics, wearable technology, AI-based data analysis, and enhanced platforms and simulations, among others. This digitization has driven an increased interest in incorporating artificial intelligence (AI) and machine learning (ML) technologies into medical devices.
Over the last decade, the US Food and Drug Administration (FDA) has reviewed and authorized a growing number of devices using its 510(k) clearance, de novo, and approved premarket (PMA) approval processes with AI/ML functionality across many different therapeutic categories—and anticipates this trend to continue. In addition, AI and ML technologies may be used to support the investigation, development, and/or production of medical devices and other FDA-regulated products. When used for medical or other healthcare-related purposes, these technologies are likely subject to FDA regulations, policies, and guidance.
In this article, we discuss the existing FDA programs and recently issued guidance impacting AI/ML technologies intended for use in healthcare, as well as what to expect from the FDA’s fiscal year (FY) 2023 priority list. We also examine the reimbursement framework for AI/ML and some challenges ahead for the medical device industry.
FDA’s regulation and oversight of AI/ML software continues to grow, as evidenced by the online list compiled and maintained by FDA’s Center for Devices and Radiological Health (CDRH) of medical devices using AI/ML technologies that CDRH has cleared or approved. That list currently includes more than 500 devices, the vast majority of which were cleared via the 510(k) process, along with a few de novo submissions and PMA applications. In terms of an FDA review branch, a significant majority fall under radiology, followed by cardiovascular, hematology, and neurology.
However, FDA/CDRH’s regulatory priorities for AI/ML technologies expand beyond premarket review, and are led by CDRH’s Digital Health Center for Excellence.
Recently Issued Guidance Documents Affecting AI/ML
CDHR remains active in promulgating guidance documents impacting AI/ML technologies, including the following recently issued guidance:
What’s in Store for 2023?
Toward the end of 2022, the CDRH published its annual list of intended guidance documents for FY 2023 (A-List and B-List). The following priorities from these lists are most likely to impact AI/ML technologies:
Changes are also on the horizon for AI/ML-enabled devices marketed for pandemic-related uses under an emergency use authorization (EUA) or one of FDA’s many COVID-related guidance documents describing enforcement policies. As noted above, CDRH’s A-list includes finalization of its previously issued draft guidance documents on transition plans for such devices. Under the draft guidance documents, FDA had proposed a three-phase, 180-day transition period for devices covered by either an EUA or a COVID-related enforcement policy. The final guidance documents are expected to issue this year.
Above, we examined how AI/ML technologies may be approved by the FDA. But what are the benefits of developing said technologies if they cannot be implemented into medical devices and sold? How will these AI/ML technologies find their way to day-to-day use in the healthcare industry?
Limited but Growing Opportunities for Direct Reimbursement
The reimbursement framework for AI/ML technologies is not advanced, and there are currently limited opportunities to realize direct reimbursement. One of the biggest impediments lies in the fact that US healthcare reimbursement remains focused on clinicians as the “source” of a reimbursable service. Recognizing that, removing a clinician from a patient care service is in some ways antithetical to the existing payment framework. But, although human clinicians remain a fixture, AI/ML can enhance clinicians’ ability to make faster decisions based on larger sets of patient data collected, see more patients due to efficiencies in appointments and evaluations, and lead to an overall reduction in time and overhead costs.
Indeed, the American Medical Association (AMA) has already developed a conceptual framework for AI/ML in healthcare, releasing its classification system in December 2021 and updating it in August 2022. The AMA recommends classification of AI devices into three overall categories based on the “work performed by the machine” in delivering an overall service: Assistive, Augmentative, and Autonomous.
Minimal Exploration by Federal Health Agencies
There has been some minimal, but growing, exploration of AI/ML reimbursement in federal healthcare programs in recent years. The Centers for Medicare & Medicaid Services (CMS) has been exploring reimbursement for certain limited procedures utilizing AI since 2018, but recent activity demonstrates that the agency’s interest is increasing.
In 2022, CMS continued to explore payment for Current Procedural Terminology Code 92229 (described by AMA as an “autonomous” service) in both the Hospital Outpatient Prospective Payment System (OPPS) and Physician Fee Schedule (PFS), and it requested public comment about software as a service, analytics, and payment for new technologies and clinical software, not only in the context of OPPS payments but as part of future adjustments to the PFS practice expense methodology. Changes to the practice expense methodology could be a game-changer for various technologies that struggle to achieve reimbursement because the expense must be directly incurred by the physician practice.
AI/ML Impact in Value-Based Care
State Laws Impacting AI/ML
Irrespective of growing reimbursement opportunities, the use of AI/ML in healthcare settings quickly implicates state rules governing the practice of medicine and other licensed professions. Already, many state medical boards are assessing how the introduction of AI will reshape medical practice.
Boards are considering the impact of telehealth, AI, and the use of other technology on the standard of care and how licensees should responsibly use these tools to furnish healthcare services. For exaample, the Federation of State Medical Boards passed a resolution in 2018 to establish a workgroup on “AI and Its Potential Impact on Patient Safety and Quality of Care in Medical Practice.” Although the working group has not yet issued formal guidance, this highlights professional licensing agencies’ focus in identifying how AI can improve patient safety and care and whether a revised regulatory framework may be necessary to respond to this new reality.
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