CLINICAL AI

Community oncology practices across America stand at a crossroads between innovation and extinction. As these independent practices struggle with declining Medicare reimbursements, workforce shortages, and mounting administrative burdens, artificial intelligence emerges as both a promising solution and an elusive goal hampered by outdated payment structures.
The current Medicare reimbursement landscape creates a paradox for AI-enabled medical technologies. While the FDA has authorized over 1,000 AI devices, with growing applications in radiology and cardiovascular care, these innovations often fail to reach patients due to unclear billing codes and fragmented coverage policies. Community oncologists, who treat the majority of cancer patients in more accessible, cost-effective settings than hospitals, find themselves unable to afford implementing AI tools that could dramatically improve patient outcomes without predictable reimbursement pathways.
The Health Tech Investment Act, introduced as S. 1399 in April 2025, presents a transformative opportunity to bridge this gap by establishing dedicated Medicare reimbursement for Algorithm-Based Healthcare Services (ABHS). This legislation would create a streamlined, five-year provisional coverage pathway for FDA-authorized AI medical devices, replacing the current reactive, piecemeal system with consistent, innovation-friendly policies. For community oncology practices operating on razor-thin margins, this predictable reimbursement framework could mean the difference between closure and technological advancement.
Community oncology's unique position makes it particularly well-suited to benefit from and contribute to AI advancement. Unlike academic specialists who focus on single cancer types, community oncologists treat diverse malignancies daily, requiring rapid decision-making across multiple disease states. AI-powered clinical decision support can provide these generalists with deep, disease-specific insights previously available only to subspecialists, enabling more precise treatment selection and improved risk stratification. This is especially critical for complex cases like non-small cell lung cancer, where choosing optimal therapy within narrow therapeutic windows can dramatically impact patient survival.
The diversity of community oncology patient populations—spanning various racial, ethnic, socioeconomic, and geographic demographics—provides the real-world data essential for developing robust, equitable AI tools. This representative patient base allows community practices to contribute meaningfully to AI algorithm training while ensuring these technologies perform effectively across all populations. As practices face increasing pressure from hospital consolidation and 340B program distortions, AI integration offers a competitive advantage that could help preserve independent oncology care.
Community oncologists must recognize that supporting the HTIA is not merely about accessing new technology—it is about ensuring their practices' survival in an increasingly challenging healthcare landscape. Without legislative action to establish clear AI reimbursement pathways, promising technologies will remain confined to academic medical centers, widening the gap between community and institutional care capabilities. The time for advocacy is now, as patients deserve access to the best science has to offer, regardless of where they receive treatment.