Turning Prior Authorization Compliance Into Progress: The Case for Glass Box, Clinically Trained AI

When AHIP, HHS, and CMS announced their series of commitments to streamline prior authorization earlier this year, they set clear expectations that health plans need to simplify the burdensome manual process–delivering faster, safer, and more transparent clinical decision-making. Additionally, with the CMS Interoperability and Prior Authorization Final Rule’s (CMS-0057-F) initial provisions taking effect in 2026, the message to the industry is unmistakable: the time to reshape prior authorization is now. These changes are forcing health plans to act, but the most forward-thinking plans have an opportunity to do more than meet the deadlines: they can redesign the process itself.

Speed or automation alone won’t resolve deeper challenges. If prior authorization workflows remain disconnected from trained clinical judgment, digital tools will simply replicate old inefficiencies. To deliver meaningful change, health plans need AI that thinks like a clinician–built to understand clinical nuance, apply evidence-based recommendations, and deliver decisions transparently–so that automation both enhances clinical decision-making and builds trust.

The status quo is clinically disconnected

For years, physicians have reported prior authorization as one of the most frustrating, time-consuming barriers to patient care. At the same time, health plans face mounting regulatory and industry pressure to accelerate approvals while maintaining safety and accountability.

According to the 2025 National Provider Survey on Prior Authorization, 97% of administrators and 93% of clinicians have seen prior authorization delays cause avoidable emergency care or hospitalizations. More than half of providers reported patients abandoning treatment due to these delays, while 95% of administrators and 92% of clinicians called prior authorization a burden. Nearly all respondents agreed that outdated, opaque systems disrupt care delivery–many still relying on fax or phone for submissions.

These challenges reflect more than workflow inefficiencies; they stem from systems optimized for administrative processing rather than driving evidence-based care. Even when digital, legacy systems often trap providers in documentation loops or replicate longstanding flaws, hindering care access and quality outcomes.

A transparent, clinically guided approach to modernization

Generic AI tools tend to prioritize speed over clinical reasoning and quality, often relying on retrospective claims and other structured data or static logic that lack the necessary context for making safe and accurate decisions. To truly transform prior authorization, health plans need purpose-built, glass-box systems that integrate clinical intelligence from the start.

A clinically guided approach begins with AI designed specifically for healthcare, which aligns with medical policy, evidence-based criteria, and patient context to deliver more precise, auditable determinations. By parsing structured and unstructured data, interpreting documentation in real time, and applying physician-level reasoning, these precise models help drive decisions that are policy-aligned and patient-specific. Embedding clinical AI into these systems moves prior authorization beyond automation toward modernization, leading to better efficiency, transparency, consistency, and overall experience for both providers and members. 

When designed this way, AI can:

• Streamline submissions through FHIR-enabled integrations for faster, consistent data exchange
• Deliver real-time approvals while maintaining medical oversight and accuracy
• Personalize provider workflows to reduce administrative effort and improve satisfaction

This approach delivers faster, more precise decision-making and strengthens the connection between clinical judgment and operational performance.

The opportunity behind AHIP’s industry commitments

This year, more than 60 health plans pledged to take meaningful steps toward achieving six key goals outlined by AHIP. These commitments include digitizing prior authorization, narrowing the scope of services requiring review, ensuring continuity of care during plan transitions, improving communication and transparency, achieving at least 80% real-time authorization approvals, and maintaining clinician oversight for medical reviews.

Within the industry’s push for reform lies an opportunity for health plans to lead by going beyond compliance and pursuing true modernization, and the AHIP commitments establish a foundation, not a finish line. Health plans can use these new standards to reimagine and share how authorization decisions are made and how they build relationships with providers. Clinically trained AI can turn compliance into progress by accelerating turnaround times, reducing provider friction, and creating new opportunities for quality improvement and care coordination. Forward-looking plans are already using this moment to strengthen data integration, decrease administrative waste, and bring clinical reasoning into every stage of the process. The foundational changes taken in response to these reforms will extend far beyond the approaching deadlines, positioning those health plans as leaders in the next era of prior authorization and utilization management.

A shared path forward

The unified calls for reform mark a rare moment of alignment between regulators, health plans, and providers. Much of the industry’s attention is understandably centered on meeting the new federal mandates–responding to standard requests within seven calendar days and urgent cases within 72 hours, and using APIs for efficient data-sharing–but that focus should only be the starting point. Real transformation hinges on innovative health plans using the growing demands for change as a catalyst for lasting, scalable improvements.

Clinically trained AI provides the framework for such progress. By integrating clinical evidence, nuance, and compassion into every decision, health plans can meet compliance while also setting higher standards for transparency, provider satisfaction, and patient-specific care–not just meeting the new requirements, but creating a foundation for the future of clinical decision-making.