Healthcare organizations are increasingly turning to AI-driven solutions to reduce administrative burdens and improve care outcomes across the ecosystem. The shift to AI-driven Prior Authorization is an area with immense potential for benefits and value, and almost equally high risks that need to be managed.
The Adoption Landscape
Integrating AI capabilities into Prior Authorization workflows aims to streamline and automate manual and severely time-consuming adjudication and claims processes.
Many Payers are looking to leverage AI to triage incoming requests, automate routine approvals, attempt to predict outcomes, and ensure that only complex cases require manual human review. Provider adoption of AI focuses on reducing their own administrative workload by auto-filling requests, extracting data directly from EHRs and clinical notes, and tracking request status in real-time.
Growing Concerns and Overlooked Risks
Despite the promise of efficiency, the use of AI in prior authorization is raising serious concerns. According to the AMA, 61% of physicians fear that the use of unregulated AI by Payers is increasing systemic prior authorization denials, potentially overriding medical judgment and harming patients.
These risks are tied to two often overlooked steps when introducing AI automation within Prior Authorization workflows.
When these two areas are overlooked, assumed or unmanaged, AI integrations for Prior Authorization can exacerbate systemic biases, introduce errors, and lack the auditability and transparency needed for audits and appeals.
The Solution: A Data-First Foundation
At Smile, we believe that successful AI adoption requires moving beyond the current "model-first" hype, toward a sustainable high-quality data engine.
Data has to come first, because it is an organizational asset, upon which everything else is built. By prioritizing the data foundation, organizations can accelerate deployment, improve model accuracy, and drastically reduce the escalating costs of healthcare AI at scale.
OmniVera ingests legacy data from disparate sources, and from a variety of formats into FHIR®, including CDAs and legacy, ensuring source data is complete, comprehensive, structured and semantically aligned. A unified and validated FHIR foundation standardizes data mapping and transformation, and validates quality before AI models are initiated.
The Approach: Defensible AI Models with ‘Humans-in-the-Loop’
Upon the FHIR and Clinical Quality Language (CQL) data foundation, a deterministic AI approach is applied.
For Prior Authorization, these computable policies are verified by a clinician, then applied consistently against unified source data on every request. While market AI tools can assist in adjudication speed, a deterministic approach goes beyond that, ensuring the auditability and clinical integrity required for healthcare decisions, without the increased tokenization costs.
The table below compares outcomes in Prior Authorization workflows using the common probabilistic (likelihood-based) approach with Smile’s deterministic approach.
Comparing AI Approaches in Prior Authorization
Scaling Clinical Intelligence
The solution and approach outlined above pushes the boundaries of what has been possible in healthcare for decades, unifying workflows and aligning automations that rely on the same source data.
Building progressive AI pipelines on unified and validated FHIR data source, aligns and coheres traditionally fragmented use-cases like Prior Authorization, Clinical Decision Support (CDS), Quality Measures (HEDIS® and custom measures), and Risk Adjustment.
Scaling clinical intelligence across Payer and Provider networks becomes possible with strategically implemented AI pipelines that run on unified FHIR data. Clinical intelligence is expert-validated once, shared and re-used across these different workflows, without additional costs and resources, to provide high value, appropriate care.
