Oct. 30, 2025

Massachusetts lawmakers released a new draft of the health insurance reform proposal (HB 4616) that replaces an earlier version of the bill. “An Act Improving the Health Insurance Prior Authorization Process” would strength requirements for insurers to streamline prior authorization using automated systems, ensure continuity of care for patients already undergoing treatment, and improve transparency in how utilization review decisions are made, including those assisted by AI. The bill also would establish a task force to study the impact of prior authorization on healthcare costs and patient access, with a report of findings due in mid-2026.

ACR® anticipates heightened activity in many states with the introduction or advancement of measures in 2026 legislative sessions that address AI governance, transparency and prior authorization modernization. Lawmakers increasingly seek a balance between innovation and patient protection, ensuring automation in claims processing and clinical decision making serves patient safety and access. 

The College’s Government State Relations Committee is available to assist ACR chapters interested in advancing prior authorization reform legislation. For more information, contact Eugenia Brandt, ACR Senior Government Relations Director, or Dillon Harp, Senior State Government Relations Specialist. To see all legislation tracked by ACR visit the College’s interactive policy map

Related ACR News

  • Radiology’s Fight Against Prior Authorization Delays

    ACR is leading national efforts to make prior authorization more efficient and clinically appropriate while reducing the administrative burden and supporting national legislation.

    Read more
  • ACR Supports Medicaid Coverage of Lung Cancer Screening

    ACR-backed bill would mandate Medicaid lung cancer screening, expand cessation coverage, ban prior auth—aiming to save lives and reduce disparities.

    Read more
  • AI-Powered Learning for Smarter Radiology Education

    This article discusses the role of AI in radiology and how AI errors can be reframed as enhanced learning opportunities in radiology education.

    Read more