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

Computer Science > Artificial Intelligence [Submitted on 31 Mar 2026] AI-Generated Prior Authorization Letters: Strong Clinical Content, Weak Administrative Scaffolding Moiz Sadiq Awan, Maryam Raza 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 duration requests, and inadequate follow-up plans. These findings reframe the question: the challenge for clinical deployment is not whether LLMs can write clinically adequate letters, but whether the systems built around them can supply the administrative precision that payer workflows require. Comments: 11 pages, 5 figures, 2 tables Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2603.29366 [cs.AI]   (or arXiv:2603.29366v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2603.29366 Focus to learn more Submission history From: Moiz Sadiq Awan [view email] [v1] Tue, 31 Mar 2026 07:40:31 UTC (57 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)