From Insight to Access: How Integrated AI Solutions and Smarter Reimbursement Can Redefine Cancer Care

Artificial intelligence is transforming oncology by enabling faster, more precise, and scalable approaches to cancer diagnosis and treatment. For decades, testing has largely relied on DNA mutation analysis—a process that is costly, time-intensive, and ultimately benefits only a small fraction of patients. In fact, fewer than one in five cancer patients have actionable DNA mutations, meaning the majority begin treatment without a targeted match, often relying on less tailored solutions.

Today, more advanced approaches are emerging. There are now emerging AI platforms that, by analyzing RNA expression by digital pathology images alone can identify treatment options for more than 90% of patients. Compared with DNA-only testing, which requires weeks to obtain a result and costs thousands of dollars, these newer methods deliver results in just days and at a fraction of the cost. The impact is significant: more patients receiving effective treatments, quicker therapeutic decisions, and fewer wasted efforts on ineffective therapies.

Yet diagnostics are only the beginning. The real transformation will occur when these AI technologies are integrated into top-tier end-to-end AI-based care continuum ecosystems. These systems do more than just generate a test result. They can help guide patients and providers through the entire journey: identifying evidence-based treatment options, alerting patients and clinicians when therapies fail, matching individuals to clinical trials, securing access to off-label or compassionate-use drugs, and navigating the complexities of insurance and prior authorization. Patients will be supported not only with clinical intelligence but also with practical tools such as reminders, financial aid guidance, symptom tracking, and live chat support—to ensure continuity of care and the best possible patient outcomes.

This integrated approach establishes a powerful feedback loop: every patient outcome feeds back into the system, enhancing its predictive precision and continuously improving treatment matching. As data accumulates across thousands of patient journeys, it evolves into a dynamic engine—driving stronger scientific insights, accelerating clinical trial recruitment, and enabling more efficient payer decision-making.

However, technology alone is not enough. If technology integration is one side of the coin, sustainable reimbursement is the other. Without the right reimbursement model(s), even the most effective solutions risk underutilization. Current frameworks often fragment or silo coverage into individual services—a test, a consult, a lab fee—without recognizing the full clinical and economic value of coordinated AI-enabled solutions. This approach risks undermining the adoption of technologies aimed at improving outcomes and reduce costs across the entire journey.

What is required is a solution-based reimbursement model that spans the full continuum of care.  This means aligning payment with the total value delivered across the continuum, from diagnostic insights to treatment navigation, outcomes tracking, trial access, etc. Such models may include establishment of CPT codes for AI-enabled testing platforms, bundled payments that capture the full scope of services, and value-based pilots that tie reimbursement directly to outcomes such as survival, adherence, reduced hospitalizations.  Early payer engagement (health plans, health systems, employers, etc.) and evidence generation will be critical components of a successful strategy.

AI has demonstrated its ability to revolutionize cancer testing by providing faster, more affordable, and more comprehensive results than ever before. Its integration into patient navigation, trial enrollment, and insurance support ensures that insights are put into action. The final step is ensuring reimbursement keeps pace with this new reality. By funding solutions rather than silos, healthcare systems can realize the full potential of AI in oncology: more patients receive the right therapy the first time, resources are used more efficiently, and outcomes are improved on a larger scale.