A regional health system was losing clinical hours to prior authorization paperwork. Nurses and physicians were spending 30-40 minutes per submission, navigating payer-specific forms, gathering clinical evidence from EHR notes, and re-keying the same information across portals. Backlogs were delaying patient care, and the team was burning out on a task that should have been routine.
A clinical AI copilot, deployed inside the team's existing workflow. The copilot reads the patient's clinical record, identifies the procedure and payer in question, pulls the relevant evidence from notes and labs, and drafts a complete prior authorization submission — with the right justification language for the payer's policy. A clinician reviews, edits if needed, and submits. Built on Azure OpenAI and grounded against the health system's own clinical guidelines, with full HIPAA and audit compliance via watsonx.governance.
85% reduction in manual review time per submission. Clinicians now spend minutes, not half-hours, on each prior auth — and approval rates went up because the AI surfaces clinical evidence that humans often miss when rushing. The system processes hundreds of requests per week without adding headcount, and the team has reallocated those hours back to direct patient care.
Prior authorization is one of healthcare's most reviled administrative burdens — and one of its most automatable. The pattern we proved here generalizes: any document-heavy clinical workflow where the source data lives in the EHR and the output is structured text is a candidate. From PA to clinical trial eligibility to discharge summaries, the playbook is the same.