Panelists Discuss Current and Future Reimbursement Mechanisms for AI-Based Healthcare Products | Hogan Lovells

AI Development Beyond Reimbursement Mechanisms

Opening the Summit’s discussion on the need to define categories within AI for reimbursement, Michael Abramoff, MD, PhD, Founder and Executive Chairman of Digital Diagnostics, a standalone AI company, and Chairman of AI Healthcare Coalition, shared concerns about how to adapt AI technology in clinical practice, and how ethics have been leveraged to create AI reimbursement frameworks, providing adequate safeguards related to patient benefits, mitigation of racial, ethnic and other biases, and costs, as has been successfully achieved for a stand-alone AI product. Stuart Langbeinhealth practice group partner Hogan Lovells, pointed out that there are a range of uses for AI technologies in healthcare, questioning whether we need to think about different types of AI from a reimbursement perspective, in order to match the type of AI to an appropriate reimbursement mechanism.

Nicole Sweeney, Senior Director of Market Access Strategy and Operations at Tempus Labs, expressed concern that AI development is moving at a faster pace than our reimbursement mechanisms allow. She explained that her company often thinks about how best to adapt its AI to the payment systems that exist today. For example, clinical lab offerings are evolving to be driven by AI, but Medicare clinical lab payment systems do not clearly consider AI in their test evaluation.

Anitra Graves, MD, medical director at Noridian Health Care Solutions, a Medicare contractor, shared other panelists’ concerns, saying the payment systems currently in place do not account for ensemble machine learning algorithms. (ML) which have high computational expenses. , but instead compare them to results from non-ML technologies. The challenges exist “in the big spaces,” Dr. Graves said, where we need to figure out how to value new systems. From a regulatory perspective, the group agreed that we have not yet reached the level of sophistication to better accommodate AI reimbursement.

Adapt new technologies to old coding frameworks

Moving on to discuss coding issues, Mr. Langbein explained how three CPTs® (Current procedural terminology[1]) categories were created to address AI – aided, augmentative and autonomous – asking the panel if this is a start towards a categorization of AI that will be useful for reimbursement. Langbein noted that while sometimes coding leads to payment, in other cases payment comes before coding. Generally, Ms. Sweeney replied, the United States may need to encourage our coding system to evolve to promote innovation through adequate payment for new technologies, which may be limited given the limitations of the systems. current coding. Dr Abramoff added that it has become clear that the CPT editorial board process can be one of the safeguards for AI, particularly with regard to patient benefits, as specific codes to fine-grained AI are being developed with WADA.®Digital Medicine Payment Advisory Group.

Dr. Graves said that while sponsors of medical products with code may find themselves on the front line for this reason, coding presents challenges when it comes to healthcare AI as regulators attempt to to integrate new ways of delivering services into old frameworks. “The issue of reimbursement as it relates to AI can be quite frustrating,” she noted. Dr. Graves predicted that eventually we will end up with a tiered reimbursement system because a product may not have the maturity to have mature explainability and interpretability, but that doesn’t mean that this product does not have a certain value.

Medicare works to improve IA assessment and payment levels

Moving on to discuss their biggest concerns with the current state of healthcare AI reimbursement, Ms. Sweeney responded that from an industry perspective, there is too much pushback on approaches based on on the value. Research costs for AI are not represented in the minimum payment offered by Medicare for the product or service, Ms. Sweeney said. Dr. Abramoff agreed that the current fee schedule for physicians does not appear to take this into account, and therefore value-based approaches should be developed transparently by AI creators, and explained how , coupled with bills in a competitive market, this has led to feasible autonomous AI. refund for CPT code 92229.

Dr. Graves explained how MACs are focused on developing a consistent analysis for each of the new technologies that expect Medicare reimbursement. She explained how the vast majority of these technologies have 510(k) approvals, which means that MACs do not have enough evidence to develop the level of confidence in the medical product to certify that it has the expected results. and proven in peer-reviewed literature.

Dr. Graves also said she was working with CMS to develop a stakeholder meeting that will aim to improve regulators’ understanding of AI-related healthcare reimbursement issues at a granular level. For example, MACs are used to looking at performance metrics with traditional diagnostics, but these metrics have a unique nomenclature in the AI ​​space. As a result, MACs question whether they can expect medical product sponsors to provide them with additional information that would allow CMS to have a better understanding of their accuracy and precision data. Dr. Graves told the panel that CMS may seek input on performance standards that regulators can use to assess AI technologies. Regulators would be influenced by sponsors and academic medical centers in terms of submitted information on how their technology fits into the patient care space and what patient population that model has been approved for. Dr Graves said that while there is no formal group at CMS taking on these issues, there is collaboration behind the scenes on regulatory development in this space.

Reimbursement issues with commercial payers, Medicaid

Turning to concerns about commercial payment for AI for healthcare, Ms Sweeney noted that some private insurers are more open to paying for new technologies than others, describing how she has observed that innovators have a some success with commercial plan pilot payment approaches. Dr. Abramoff explained how commercial payers may be more flexible than CMS, but they generally follow Medicare’s actions and in fact quickly followed CMS’s nationwide reimbursement decision for 92229 as of January 2022.

Dr. Abramoff also discussed the challenges of obtaining payment from Medicaid, which he described as time-consuming due to the need to work separately with each of the state’s 50 agencies, but which is crucial. because these payers cover the most vulnerable populations who need it most urgently. access to a demand savings and equity enhancement product such as IDx-DR. As a result, he said, we need to make sure Medicaid recipients don’t lose access to innovative, standalone AI diagnostics. Ms. Sweeney agreed that Medicaid needs central support for reimbursement programs and pilot payment programs because state plans lack funding.

Regarding payment pilot programs, Mr. Langbein suggested that the results of these programs should be brought to the attention of MACs, which may have some leeway in the AI ​​space compared to the repayment of more established technologies. Dr. Graves agreed that MACs would benefit from knowing the results of pilot programs, while noting that commercial payers have more leeway in reimbursing new technologies.

Dr. Graves concluded the roundtable by highlighting how MACs are interested in partnering with stakeholders to ensure patients have access to innovative therapies. While Medicare “may deserve a portion of [the] critical” to prevent access to therapies, the agency is focused on patient outcomes and working with industry, Dr. Graves stressed. Ms. Sweeney agreed that an open conversation is essential to ensure that healthcare technologies are developed in the best interests of patients.

[1] CPT is a registered trademark of the American Medical Association.

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