Challenges Associated with Getting New Technology in the Hands of Patients, Part 2

Challenges Associated with Getting New Technology in the Hands of Patients, Part 2

One of the largest American radiology firms, RadNet, recently launched an Enhanced Breast Cancer Detection (EBCD) service that offers an FDA-cleared artificial intelligence (AI) technology with patients’ annual breast screening regimen. Clinical studies and real-world evidence (internal) support the technology’s ability to aid radiologists in providing an accurate mammogram report and improving overall cancer detection. But it doesn’t come free. RadNet faces the same conundrum most innovators face in the current United States healthcare market: how to get the technology reimbursed. RadNet has taken a bold move in inviting patients to pay an out-of-pocket fee for the service, at least for now. Some may question the appropriateness of this approach, particularly in terms of health equity. While others argue that without a direct-to-consumer approach, no patients would benefit from the innovation. Either way, this is a huge challenge in our healthcare system, and there is a strong need to provide a bridge from innovation to widespread patient access.

Out-of-Pocket Payment Model is a Necessary and Crucial Step

RadNet’s self-pay model for EBCD is similar to the approach taken with respect to the tomosynthesis model, which first hit the market with a $50 out-of-pocket fee before proving clinical benefit and being covered by Medicare. RadNet acknowledges the importance of balancing affordability with patients’ willingness to pay in the early stages. Therefore, RadNet conducted numerous focus group discussions early in their pilot to better understand patient response to the program and the impact of their out-of-pocket model and received a generally positive response. Some participants expressed willingness to pay significantly more than the set charges to obtain the benefits of the AI.

In order to demonstrate the clinical benefits of the EBCD program, RadNet is actively conducting real-world evidence studies to prove the benefit and assess the market response to the program. They are optimistic that patients will advocate for insurance coverage, and CMS will begin looking into reimbursement for AI algorithm use, highlighting the potential benefits and long-term cost savings associated with early cancer detection. RadNet sees the self-pay model as a bridge to a longer-term solution. Most AI startups face a significant financial uphill climb and depend on many rounds of financing by patient investors with the hope of someday recovering the years of losses while bringing lifesaving technologies to the masses. RadNet is invested in the long run and wants to ensure access to its technology for everyone women for years to come. Its AI division is still operating in the red, but with the EBCD program, there is a foundation to sustain continued progress until reimbursement is approved.

Striking a Balance

RadNet’s challenges with the EBCD program serves to highlight the ongoing problem innovators face bringing new healthcare products and services into widespread clinical practices so that all patients may benefit. Some potential solutions include:

  • Evidence-Based Guidelines: Professional societies of radiology and medicine should collaborate to review the evidence on AI radiology, its clinical benefits, and its limitations. Moreover, AI radiology vendors promoting their product should conduct rigorous studies so that societies can review the clinical data and help optimize the integration of AI radiology into those guidelines and promote reimbursement from CMS.
  • Increased Government Funding: Government funding enables research to evaluate the effectiveness and impact of AI radiology, leading to the acceleration of evidence-based guidelines. Simultaneously, CMS exploring reimbursement models should ensure that AI radiology services can reduce the healthcare economic burden and provide value in the long run. These initiatives are not only driven by the goal of promoting access but also by the potential for cost savings and improved health outcomes. From a health economics standpoint, reimbursement for AI radiology services can be justified based on the value it brings to the healthcare system. For example, if AI radiology demonstrates the ability to reduce unnecessary scans, tests, and procedures, value is created by lowering the overall healthcare cost for the patient. Therefore, by investing in AI radiology and focusing on reimbursement pathways, the government recognizes the potential for greater clinical value, ensuring a sustainable healthcare system that benefits both patients and the healthcare system as a whole.
  • Emphasis on Informed Consent: Informed consent is vital when patients are required to pay out-of-pocket for AI radiology services. Clear and unbiased communication is essential to ensure patients understand the benefits, limitations, and potential risks. Healthcare providers should engage in transparent discussions, explaining the added value of AI radiology and its implications. Emphasizing informed consent promotes patient autonomy and ethical decision-making.

As the field of AI in medicine continues to evolve, it is essential that payment models follow suit. Therefore, collaboration among stakeholders, payers, and regulatory bodies is crucial. By fostering ongoing discussions, exploring pricing models, and conducting studies to prove clinical benefit, the aim is to establish a sustainable and ethically sound landscape for AI radiology. RadNet is an example of how one leading AI business is trying to succeed in the face of these innovation challenges. The EBCD program serves as a catalyst for further discussion. It is a bold move with challenges, and it is yet to be determined if it will be successful in driving new advancements in AI technology and promoting equitable access to enhanced healthcare services, but it provides a new potential path for future AI innovators to consider with the big financial mountains they must climb.

Brendan Ryu

Brendan Ryu

Brendan is a fourth year medical student applying to radiology residency. He aspires to accelerate MedTech innovation and to build a career integrating innovation into clinical practice.

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