The Standard

How AI for Deterministic Approaches Can Fix PA Denials

A recent survey by the American Medical Association (AMA) revealed a growing concern among physicians: over 60% believe that unregulated AI tools are systematically denying necessary patient care. “Emerging evidence shows that insurers use automated decision-making systems to create systematic batch denials with little or no human review, placing barriers between patients and necessary medical care,” said AMA President Bruce A. Scott, MD, reacting to the survey results.

This alarming trend exacerbates the already burdensome prior authorization (PA) process in the US, delaying essential treatments and placing undue strain on providers, payers and patients alike.

At Smile Digital Health, we believe that AI should support—not override—good medical judgment. That’s why we developed KALM Services (Knowledge Authoring Lifecycle Management), with AI capabilities designed to exponentially accelerate the creation of deterministic computable policies for prior authorization workflows and other use cases like clinical reasoning and quality measure evaluations.

Federal health agencies have entered an era where data interoperability-enabled solutions play a critical role. Government leaders can harness the innovative and proven capabilities referenced in this article to deliver on their essential missions.

Background

In 2020, two major events laid the foundation for this era of interoperability.

Pandemic Response: The first was the coronavirus pandemic, which led to unprecedented needs for health data in support of agency missions. Since its start, decision-makers have required more access to and insights from these data (e.g., clinical records, administrative claims, patient experience) than ever before.

Interoperability Rules: The second was the finalization of the ONC and CMS-led 21^st Century Cures Act interoperability rules. These mandates substantially expanded agencies’ ability to leverage health data solutions (e.g., algorithms, applications, and automation) at scale.