Choosing the Right Methods for the Right AI to Accelerate Prior Authorizations

As artificial intelligence rapidly reshapes healthcare workflows, choosing the right type of AI for high-stakes healthcare processes has never been more critical. There are strengths and limitations to using analytical, generative, and predictive AI in clinical and administrative settings, particularly prior authorizations. 

With regulatory scrutiny intensifying and the demand for speed, compliance, and clarity growing, understanding the nuanced differences between AI approaches is essential for payers, providers, and patients alike.

Analytical AI

Analytical AI applies deterministic, rule-based logic to structured data. It excels in scenarios where transparency, auditability, and compliance are critical. In prior authorizations, this means using evidence-based guidelines and policy-driven frameworks to make determinations that can be traced and validated. 

Analytical AI is ideal for processes like clinical coding, claims validation, and prior authorization because these tasks demand precision and regulatory adherence. AI should be used to automate approvals only when clinical alignment is clear. In cases of ambiguity or complexity, decisions must be deferred to licensed clinicians for review.

Generative AI

Generative AI creates new content like text, images, or even synthetic data based on patterns learned from large datasets. Its strength lies in summarization, drafting, and conversational interfaces. In healthcare, generative AI can streamline administrative tasks such as creating patient education materials or summarizing lengthy clinical notes. However, it is not suited for decisions that require strict compliance or deterministic outcomes, as its outputs are probabilistic and difficult to trace or audit. 

Applying generative AI to prior authorization introduces unacceptable risk. This doesn’t mean GenAI has no role in utilization management. It absolutely does. But that role is suited to supportive, non-decisional tasks.

Predictive AI

Predictive AI uses historical data to forecast future events or behaviors. In healthcare, predictive models can identify patients at risk for chronic conditions, anticipate hospital readmissions, or optimize resource allocation. These insights help clinicians intervene earlier and improve population health outcomes. 

Predictive AI is powerful for planning and prevention, but its recommendations should always be paired with human judgment to avoid unintended bias.

Why Gen AI is the wrong choice for prior authorizations

The prior authorization process sits at the nexus of medical necessity, clinical judgment, and policy compliance. Medical necessity determinations demand absolute clarity, adherence to payer policies, and full auditability; standards that generative models cannot guarantee. 

Decisions based on variable outputs could compromise regulatory integrity, erode provider trust, and ultimately impact patient care. For these reasons, generative AI belongs in supportive, non-decisional roles, not in the core of clinical evidence and medical policy enforcement. 

Already, regulators are scrutinizing “AI denials” and warning health plans against opaque or unreviewable decision-making systems. The CMS Interoperability and Prior Authorization Final Rule, set to take effect in 2027, mandates greater transparency and interoperability in UM. This includes documenting the reason for every denial, providing real-time status updates, and offering clear, accurate communication between payers and providers.

Why analytical AI is the right choice for prior authorizations

Analytical AI provides a deterministic framework that ensures every decision is traceable, explainable, and auditable. Unlike generative or predictive models, which rely on probabilistic outputs, analytical AI applies structured rules and clinical evidence to deliver consistent, defensible outcomes. This approach doesn’t replace human judgment; it elevates it. By removing routine approvals from clinical queues, analytical AI supports faster turnaround time, reduces administrative burdens, and enables clinicians to practice at the top of their license.

In the context of prior authorizations, analytical AI refers to the use of policy-aligned intelligence that evaluates structured clinical data, submitted at the point of care, against codified medical policy to determine whether a service meets criteria for immediate approval, pend for review, or escalate to a chief medical officer.

How analytical AI works in prior authorizations

By working in close collaboration with the health plan’s clinical policy teams, analytical AI can be embedded into the prior authorization process, so payers can modernize UM without sacrificing clinical integrity.

Here’s what happens behind the scenes when applying analytical AI in prior authorizations:

• Targeted clinical inputs: The model evaluates only the clinical data relevant to the decision and policy logic. This avoids noise, reduces bias, and improves consistency.
• Policy logic application: It applies plan-specific policy logic that has been codified into deterministic decision pathways rooted in clinical evidence. 
• Constrained decisioning: The AI generates only defined, policy-aligned recommendations (typically approve, pend, or escalate) ensuring decisions keep humans in the loop. 
• Transparent traceability: Because outputs are rooted in clinical evidence, every recommendation can be audited and explained, step-by-step, by the plan and the provider.
• Escalation when needed: If a recommendation cannot be confidently made, the request is flagged for human clinical review.

This isn’t just automation. It’s intelligence that considers each request under its own merits, giving providers clarity and health plans audit-ready determination records. 

The path forward

As AI continues to evolve, health plans will be bombarded with solutions promising to “fix” prior authorization. Many of these will feature slick demos, glowing buzzwords, and generative tools that look impressive but lack the rigor, specificity, and governance that healthcare demands.

To separate signal from noise, payers must ask the right questions:

• Can this system show me how each decision was made?
• Does it use my medical policies or rely on historical patterns?
• Is it making predictions or applying codified decision pathways?
• Does it defer to clinicians when cases require expertise?

If the answer isn’t clear, the risk is.

Generative AI may be the right method to solve many problems in healthcare, but for prior authorizations, analytical AI is the way to go.

Photo: MirageC, Getty Images

Matt Cunningham, EVP of Product at Availity, spent nine years in the Army in light and mechanized infantry units, including the 2nd Ranger Battalion. He brought his Army operations experience to the healthcare industry and has been focused on solving the problem of prior authorizations and utilization management for the past 15+ years. He helped scale a services company from $20M to the largest healthcare benefit services company. Matt has served as Head of Call Center Operations, Director of Product Operations, Chief Information Officer, and lead integration efforts for mergers and acquisitions.

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