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

TL;DR

This study evaluates large language models' ability to generate prior authorization letters, finding they produce clinically strong content but lack necessary administrative details for real-world healthcare workflows.

Contribution

It provides a structured multi-scenario evaluation of LLMs for prior authorization letter generation, highlighting strengths in clinical content and gaps in administrative accuracy.

Findings

Models generate accurate diagnoses and well-structured medical necessity arguments.

Common administrative gaps include missing billing codes and authorization durations.

Clinical adequacy does not guarantee administrative suitability for payer workflows.

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 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.