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The American Medical Association’s new interoperability initiative aims to bridge clinical and administrative terminologies, making prior authorizations faster and more efficient for patients and providers.
The American Medical Association (AMA) has announced a significant step towards modernizing healthcare processes with the launch of an interoperability initiative. This initiative focuses on improving electronic prior authorizations by better connecting clinical terminology to administrative coding systems. The goal is to create a seamless, automated workflow that reduces administrative burdens and ensures patients receive timely care.
At its core, this effort involves developing and deploying mappings between SNOMED CT-a structured set of descriptive medical terms-and Current Procedural Terminology (CPT) codes. SNOMED CT serves as a shared vocabulary for computer systems, while CPT codes are used for billing and administrative purposes. By aligning these two systems, the AMA aims to streamline data exchange and improve the efficiency of prior authorizations.
This initiative is particularly timely given the upcoming January 1, 2027 compliance deadline set by the Centers for Medicare & Medicaid Services (CMS) for FHIR-based prior authorization. The Fast Healthcare Interoperability Resources (FHIR) standard is designed to facilitate seamless data exchange between different healthcare systems. By aligning with this standard, the AMA’s initiative will help healthcare providers meet regulatory requirements and enhance patient care.
The AMA plans to begin by targeting prior authorization cases that have the greatest potential to reduce administrative burdens and aid patients in receiving timely care. This targeted approach ensures that the most pressing issues are addressed first, providing immediate benefits to both healthcare providers and patients.
To achieve this, the AMA will work closely with key stakeholders, including health plans and health IT developers, to test and refine the mappings. These collaborations will involve technical working sessions, pilot projects, educational forums, and more. The goal is to create a robust framework that can be widely adopted and continuously improved.

Willie Underwood III, M.D., AMA president, emphasized the importance of this initiative: “Modernizing prior authorization requires trusted, interoperable terminology assets that can support real-world clinical and administrative workflows. By helping bridge SNOMED CT clinical concepts and CPT procedural coding for prior authorization use cases, the AMA aims to support a more seamless process.”
The success of this initiative will depend on widespread adoption and continuous improvement. The AMA’s commitment to working with stakeholders ensures that the mappings will be tested in real-world scenarios, providing valuable insights and opportunities for refinement. As healthcare systems increasingly rely on digital tools and data exchange, initiatives like this one are crucial for ensuring that technology serves the needs of both providers and patients.
This initiative aligns with broader efforts to leverage artificial intelligence (AI) in healthcare. OpenAI’s health AI chief, Karan Singhal, has set a bold goal of ensuring that AI “improves health for all humanity.” By modernizing prior authorization processes, the AMA is laying the groundwork for more advanced AI applications that can further enhance patient care and operational efficiency.
As the healthcare landscape continues to evolve, initiatives like this one will play a vital role in shaping a future where technology and data work together to improve outcomes for everyone involved.
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AMA interoperability initiative brings structured clinical terminology to CPT codes
↗ https://www.fiercehealthcare.com/providers/ama-launches-interoperability-initiative-speed-electronic-prior-auths-terminology-mapping
About the author
Amara's entry point into AI was an epidemiology role at a London research hospital, where she spent five years studying how digital health tools reached — or conspicuously failed to reach — underserved communities. Watching early algorithmic systems in healthcare quietly entrench existing inequalities, she redirected her career toward the systemic consequences of AI at scale. She covers AI through an unflinching lens: who benefits, who bears the cost, and what evidence actually says versus what the press release claims. Her writing is calm and precise, but she doesn't mistake balance for neutrality.
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20 July 2026
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