[2603.29366] AI-Generated Prior Authorization Letters: Strong Clinical Content, Weak Administrative Scaffolding

Computer Science > Artificial Intelligence arXiv:2603.29366 (cs) [Submitted on 31 Mar 2026] Title:AI-Generated Prior Authorization Letters: Strong Clinical Content, Weak Administrative Scaffolding Authors:Moiz Sadiq Awan, Maryam Raza View a PDF of the paper titled AI-Generated Prior Authorization Letters: Strong Clinical Content, Weak Administrative Scaffolding, by Moiz Sadiq Awan and 1 other authors View PDF HTML (experimental) Abstract:Prior authorization remains one of the most burdensome administrative processes in U.S. healthcare, consuming billions of dollars and thousands of physician hours each year. While large language models have shown promise across clinical text tasks, their ability to produce submission-ready prior authorization letters has received only limited attention, with existing work confined to single-case demonstrations rather than structured multi-scenario evaluation. We assessed three commercially available LLMs (GPT-4o, Claude Sonnet 4.5, and Gemini 2.5 Pro) across 45 physician-validated synthetic scenarios spanning rheumatology, psychiatry, oncology, cardiology, and orthopedics. All three models generated letters with strong clinical content: accurate diagnoses, well-structured medical necessity arguments, and thorough step therapy documentation. However, a secondary analysis of real-world administrative requirements revealed consistent gaps that clinical scoring alone did not capture, including absent billing codes, missing authorization durati...