What Do Payers Mean by “Clinical Utility”?

Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the medtech landscape. From AI-powered diagnostics to robotic surgery assistants, these tools hold immense promise for improving patient care. However, for these innovations to achieve successful market access, they often need to secure payer coverage and reimbursement. To do that, their clinical utility needs to be clearly demonstrated.

What is Clinical Utility?

Clinical utility refers to the clinically meaningful value the AI tool or intervention brings to patient care. It encompasses a range of factors, including:

• Does the AI tool improve patient outcomes in some way? How? And by how much, exactly?

• Can it improve the accuracy of diagnosis, risk prediction, or treatment plans, compared to what clinicians currently use under standard of care protocols?

• Do physicians actually use the AI-generated reports to manage patients differently, and can you quantify that difference?

Demonstrating clinical utility is crucial for several reasons. First, it reinforces the scientific validity of the AI/ML tool. It ensures the tool is not just technologically impressive but actually translates to meaningful improvements in the health of the plan's members or the government's beneficiaries. Second, clinical utility helps guide treatment decisions. With robust evidence of effectiveness, health care providers can confidently integrate the tool into their practice. Finally, clinical utility is essential for gaining widespread adoption. Health plans and institutions are hesitant to invest in technologies that lack clear benefits for patients and the healthcare system.

Why Health Plans Care About Clinical Utility

The rise of AI/ML MedTech presents both opportunities and challenges for health plans. On the one hand, these tools have the potential to improve patient outcomes and potentially reduce long-term healthcare costs. For instance, AI-powered early disease detection can lead to earlier interventions and better patient prognoses, ultimately reducing the overall cost of treatment.

But these value propositions cannot be simply a theoretical exercise. AI medtech companies have to demonstrate their tangible impact on patient care.

Health plans are also cautious about cost-effectiveness. Implementing new technologies often comes with a price tag. Before adopting an AI/ML tool, health plans need to be convinced that the clinical benefits outweigh the costs. Clinical utility studies provide the necessary evidence for payers to make informed decisions regarding reimbursement and coverage.

Clinical Utility Needed from Payers vs. Regulatory Approval

It's important to distinguish clinical utility from regulatory approval. While the regulatory environment continues to evolve, the US Food and Drug Administration (FDA) plays a critical role in ensuring the safety and efficacy of AI medical devices before they reach the market. The FDA approval process focuses on aspects like technical performance and potential risks.

While FDA approval is a crucial first step, it doesn't automatically guarantee clinical utility. A device might be technically sound and receive FDA clearance, but it still needs to demonstrate its value in real-world clinical settings. This is where clinical utility studies come in. These studies typically involve rigorous evaluations in clinical trials and healthcare settings, focusing on the specific benefits the AI/ML tool offers to patients and the healthcare system.

Examples of Clinical Utility in Action

Here are a few examples of how AI/ML MedTech is demonstrating clinical utility:

• AI-powered diabetic retinopathy screening: Diabetic retinopathy is a leading cause of blindness. AI algorithms can analyze retinal images with high accuracy, enabling early detection and prevention of vision loss. Studies have shown that AI-based screening programs can effectively identify patients at risk, leading to timely treatment and improved patient outcomes.

• Machine learning for personalized cancer treatment: AI can analyze vast amounts of patient data, including genetic information and medical history, to predict the best course of treatment for individual cancer patients. This personalized approach can improve treatment efficacy while minimizing side effects. Clinical trials are demonstrating promising results in using AI to guide cancer therapy decisions.

• AI-assisted robotic surgery: Robotic surgery offers minimally invasive procedures with improved precision. Integrating AI into robotic surgical systems further enhances the surgeon's capabilities by analyzing real-time data and providing guidance during surgery. Studies suggest that AI-assisted robotic surgery can lead to shorter operating times, reduced blood loss, and faster patient recovery.

What AI MedTech Can Do in Early Stages

Clinical utility is at the heart of AI/ML MedTech uptake by payers. It ensures that these innovative tools translate to meaningful improvements in patient care and healthcare delivery. By focusing on clinical utility, MedTech developers, healthcare providers, and health plans can work together to unlock the true potential of AI/ML and revolutionize the future of medicine.

If you'd like to get early guidance on what clinical utility can look like for your product, you can get in front of a consultative payer medical director or a hospital purchaser to understand what type of clinical utility data they'd like to see for your product. Contact Coustier Advisory for more information.