CER Daily Newsfeed

Value Viewpoint: March 13, 2026

(3/13, Kimberly Westrich, LinkedIn) comments “...New research published in Health Affairs Scholar asks a simple question with big implications: what do Medicare patients with cancer think ‘value’ and ‘quality’ should mean in oncology value-based payment models?...Patients were wary that two-sided risk and cost benchmarks could come between them and their clinicians, reduce access for high-cost patients, and erode trust. Their solutions were straightforward: plain-language patient education about these models, transparent practice-level communication about incentives/risks, and drug-cost accountability that doesn’t rest solely on clinicians.” Full

How Data Partnerships Make Real-World Evidence Possible

(3/16, Aaron E. Carroll, M.D., M.S., AcademyHealth Blog) comments “...Health systems and payers face legitimate concerns about privacy, competition, and regulatory risk. At the same time, policymakers and communities increasingly expect decisions to be grounded in evidence drawn from real-world experience. These tensions cannot be resolved through mandates alone. They require durable frameworks that balance access with accountability and that can withstand political and technological change. [Health Data for Action (HD4A)] offers a glimpse of what those frameworks can look like, but the work cannot stop with a single program.” Full

Real-World Evidence Meets Machine Learning: What It Takes to Future-Proof Evidence Generation

(3/16, Christopher McSpiritt, MedCity News) comments “...With ML-powered RWE, scientists can create synthetic control arms that accelerate research timelines, drastically reduce costs, and cut patient recruitment demands by 20-50 percent. Furthermore, ML models can identify patient subpopulations most likely to benefit from particular treatments, or those at highest risk for adverse events. They can also predict how certain patients will respond to various therapies based on their unique clinical and genomic profiles. Additionally, ML models can continuously trawl the FDA Sentinel system to uncover potential adverse events exponentially faster than manual queries.” Full

How AI-Driven Functional Precision Medicine Unlocks Personalized Cancer Therapy

(3/16, Jim Foote, DOTmed News) reports “...While genomic sequencing and AI-assisted analytics have improved disease classification, biomarker identification, and the discovery of drugs that may help, most patients are still treated using standardized protocols driven by population-level data rather than individual tumor behavior...A new class of technology, AI-driven Functional Precision Medicine (FPM), is emerging to address this gap. By combining patient-derived tumor biology with advancements in proprietary cell enrichment processes, automation, robotics, and artificial intelligence, FPM platforms enable oncologists to rapidly test how an individual patient’s cancer responds to hundreds of FDA-approved drugs and combinations, delivering ranked treatment options within days.” Full