Transforming Prior Authorization through Artificial Intelligence: A National Framework for Reducing Administrative Burden and Improving Patient Access
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I3P103Keywords:
Prior Authorization, Artificial Intelligence, Administrative Burden, Patient Access, Interoperability, Clinical Decision Support, Healthcare GovernanceAbstract
Prior authorization is widely used to manage healthcare utilization and control costs, yet its current implementation often creates substantial administrative burden, delays in treatment, inconsistent insurer requirements, and barriers to patient access. Healthcare professionals frequently spend considerable time preparing documentation, responding to payer queries, and managing appeals, while patients may experience uncertainty, interrupted care, and delayed access to clinically necessary services. This paper examines how artificial intelligence can transform prior authorization through a national framework designed to reduce administrative workload while improving timeliness, transparency, equity, and clinical accountability. The proposed framework integrates interoperable health-data exchange, AI-supported documentation extraction, eligibility checking, risk-based case triage, clinician-led review, patient communication tools, and continuous performance monitoring. AI is positioned as a decision-support mechanism rather than an autonomous decision-maker, ensuring that complex, high-risk, and denied cases remain subject to qualified human oversight. The framework also incorporates safeguards for privacy, explain ability, bias detection, patient appeal rights, and independent governance. By standardizing workflows and reducing repetitive administrative tasks, an AI-enabled national prior-authorization system may improve provider efficiency, accelerate appropriate treatment access, and support a more patient-centered healthcare system. Future empirical research should evaluate the framework using real-world payer, provider, and patient outcome data.
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