Impact of Utilization Management Policies in Oncology

EP: 1.Impact of Recent Policies on US Health Care System and Oncology Practice

EP: 2.Bridging Policy Change With Patient Access and Care Delivery in Oncology

EP: 3.Role of Pharmacy Benefit Managers in Oncology

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EP: 4.Impact of Utilization Management Policies in Oncology

EP: 5.Challenges of Step Edits in Oncology

EP: 6.Understanding Gaps in Aligning Coverage Policy With Innovation in Oncology

EP: 7.Disconnect Between NCCN Guidelines and Coverage Decisions With BTK Inhibitors in CLL

EP: 8.Impact of Evidence-Coverage Misalignment on Patient Outcomes With BTK Inhibitors in CLL

EP: 9.Using Real-World Evidence of BTK Inhibitors to Inform Coverage Decisions in CLL

EP: 10.Balancing Black Box Warnings With Real-World Oncology Practice

EP: 11.Impact of Utilization Management on Physician Autonomy and Shared Decision-Making in Oncology

EP: 12.Shaping Oncology Policy Through Access and Safety Considerations

EP: 13.Impact of Inflation Reduction Act and the Drug Price Negotiation Program in Oncology

EP: 14.Implications of IRA Drug Pricing Negotiations on BTK Inhibitors in CLL

EP: 15.The Future of Oncology Research in a Shifting Policy Landscape

Panelists discuss how utilization management (UM) policies—such as prior authorization and formulary restrictions—affect daily oncology practice. They describe these policies as double-edged: While intended to ensure appropriate care, they often delay treatment, add administrative burden, and erode patient trust.

The conversation highlights the disproportionate impact on complex oncology regimens where timeliness is critical. Clinicians must often navigate multiple layers of documentation and appeals, diverting attention from direct patient care. Panelists advocate for data-driven UM reforms that differentiate between evidence-supported and outdated restrictions, calling for collaborative review systems that balance safety with clinical autonomy.

The group concludes that more agile, transparent UM frameworks—supported by automation and clinical input—could reduce inefficiency and improve patient outcomes.