Measuring Quality in Regulatory Medical Writing: A Multi-Layered Evaluation Framework for Artificial Intelligence–Generated Content

Abstract

Generative artificial intelligence (AI) is rapidly entering regulatory medical writing, yet the industry still lacks standardized methods for measuring document quality. Traditional quality assurance practices, including layered reviews, compliance checks, and formal sign-off, help prevent errors but do not provide an objective basis for evaluating or comparing quality across documents. As AI systems take on larger roles in drafting regulatory content, organizations need to show that AI-assisted outputs meet the same standards of accuracy, completeness, and compliance expected of human-authored text. This article draws a clear line between quality assurance (ensuring standards are met) and quality measurement (quantifying how well standards are met) and argues that measurement must come first. We propose a multilayered evaluation framework built around postedit distance, defined as the proportion of text requiring human revision, and supported by human expert review, structured rubrics, automated consistency checks, and benchmark testing. We also introduce the Peer Quality Index, a composite scoring system that brings together 6 dimensions: accuracy, compliance, clarity, consistency, completeness, and efficiency. A cross-functional team of experts developed and reviewed the framework, which we present here as a methodological foundation for broader industry adoption. By anchoring quality assessment in measurable evidence while preserving expert judgment, this approach offers a defensible path toward transparency, consistency, and trust in AI-assisted regulatory writing