A Bias-Aware Analytic Framework for Real-World Data with AI-Audited Workflows: Application to Opioid Ordering in Pediatric Oncology

Background

The growing use of computational modeling of real-world data (RWD) in clinical research introduces significant risks for data scientists and AI trainers already managing inconsistency, unexamined bias, and analytical opacity. Building upon more than 600 guideline-based checklists and pre-planning templates for data quality and transparency reporting in observational RWD, we propose a run-time operational execution loop to embed reproducibility analytics within the workflow. Our approach acts as a run-time gatekeeper mapping prespecified analytics goals to a closed-loop LLM semantic code auditor, checking for both logic, bias, and vulnerabilities during code execution. Our framework was designed to bridge the gap between passive disclosure, ensuring proposed requirements, and post-hoc validation. Our structured hierarchical approach coupled with AI auditing is the first phase in the development of full-stack automation of bias-aware RWD analytic validation.