Choosing the right approach for the right AI to accelerate prior authorization

As AI rapidly reshapes healthcare workflows, choosing the right type of AI for high-risk healthcare processes has never been more important. The use of analytical, generative and predictive AI in clinical and administrative settings has its advantages and limitations, particularly with regard to prior authorization.

As regulatory scrutiny intensifies and the need for speed, compliance, and clarity continues to grow, understanding the nuances between AI approaches is critical for payers, providers, and patients alike.

Artificial Intelligence Analysis

Analytical AI applies deterministic, rules-based logic to structured data. It excels in scenarios where transparency, auditability and compliance are critical. In prior authorization, this means using evidence-based guidance and policy-driven frameworks to make traceable and verifiable decisions.

Analytical AI is ideal for processes such as clinical coding, claims verification, and prior authorization, as these tasks require precision and compliance with regulations. Artificial intelligence should be used to automate approval only when clinical consistency is clear. If a situation is ambiguous or complex, the decision must be submitted to a licensed clinician for review.

generative artificial intelligence

Generative AI creates new content such as text, images, and even synthetic data based on patterns learned from large data sets. Its strengths lie in summarizing, 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 suitable for decisions that require strict compliance or deterministic results because its output is probabilistic and difficult to track or audit.

Applying generative AI to prior authorization poses unacceptable risks. This doesn't mean GenAI has no role in utilization management. Indeed. But this role is suitable for supportive, non-decision-making tasks.

Predictive Artificial Intelligence

Predictive AI uses historical data to predict future events or behaviors. In healthcare, predictive models can identify patients at risk for chronic disease, predict 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 combined with human judgment to avoid unintended biases.

Why Gen AI is the wrong choice for prior authorization

The prior authorization process involves medical necessity, clinical judgment, and policy compliance. Determination of medical necessity requires absolute clarity, compliance with payer policies, and full auditability; standards that generative models cannot guarantee.

Decisions based on variable outputs can compromise regulatory integrity, erode healthcare provider trust, and ultimately impact patient care. For these reasons, generative AI is relegated to a supportive, non-decisional role rather than central to clinical evidence and healthcare policy implementation.

Regulators are already scrutinizing “AI denial” and warning health plans against adopting decision-making systems that are opaque or unauditable. The CMS Interoperability and Prior Authorization Final Rule will take effect in 2027, requiring greater transparency and interoperability in UM. This includes documenting the reason for each denial, providing real-time status updates, and providing clear, accurate communication between payers and providers.

Why analytics AI is the right choice for preauthorization

Analytical AI provides a deterministic framework that ensures every decision is traceable, explainable and auditable. Unlike generative or predictive models that rely on probabilistic outputs, analytical AI applies structured rules and clinical evidence to deliver consistent, reliable results. This approach does not replace human judgment; It elevates it. By eliminating routine approvals in clinical queues, analytic AI supports faster turnaround times, reduces administrative burden, and enables clinicians to practice within the scope of their license.

In the context of prior authorization, analytical AI refers to the use of policy-consistent intelligence to evaluate structured clinical data submitted at the point of care in accordance with documented medical policies to determine whether services meet criteria for immediate approval, pending review, or escalation to the chief medical officer.

Analyzing how artificial intelligence plays a role in prior authorization

By working closely with the health plan's clinical policy team, 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 analytical AI is applied in prior authorization:

• Targeted clinical investment: The model only evaluates clinical data relevant to decision-making and policy logic. This avoids noise, reduces bias, and improves consistency.
• Strategy logic application: It applies program-specific policy logic that has been codified into deterministic decision pathways based on clinical evidence.
• Constraint decisions: AI only generates clear, policy-compliant recommendations (usually approved, pending, or upgraded), ensuring that decisions involve humans.
• Transparent traceability: Because the output is rooted in clinical evidence, programs and providers can review and interpret each recommendation step by step.
• Upgrade when needed: If a recommendation cannot be made with confidence, the request will be flagged for human clinical review.

It's not just automation. Its intelligence considers each request on its own merit, providing providers with clear information and a definitive record that health plans can audit.

way forward

As artificial intelligence continues to advance, health plans will be bombarded with solutions promising to “fix” prior authorization. Many of them will have slick presentations, fancy buzzwords, and generative tools that look impressive but lack the rigor, specificity, and governance that healthcare requires.

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

• Can this system tell me how each decision is made?
• Does it use my health care policy or rely on historical patterns?
• Is it making predictions or applying coded decision paths?
• Will it defer to clinicians when a case requires expertise?

If the answer is unclear, the risk is there.

Generative AI may be the right approach to solving many problems in healthcare, but for prior authorization, analytical AI is the right approach.

Photo: MirageC, Getty Images

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