This research introduces a novel verification method for AI-generated clinical notes using process-supervised reward models (PRMs) that evaluate outputs step-by-step rather than just the final result.

• Extends PRM techniques (successful in math/coding) to the critical medical domain
• Incorporates real-world clinician expertise to define evaluation steps for medical documentation
• Demonstrates improved verification compared to traditional reward models
• Validated through comprehensive physician reader studies

This advancement matters for healthcare because it provides a more reliable way to verify AI-generated clinical documentation, potentially reducing errors and increasing trust in AI assistants for medical professionals.

Process-Supervised Reward Models for Verifying Clinical Note Generation: A Scalable Approach Guided by Domain Expertise