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Preprint

A Non-Destructive Methodological Framework for Modernizing Legacy Clinical Reporting Systems for AI-Driven Pharmacoinformatics: A SAS Case Study

Jaime Yan

arXiv · 2026 · DBLP-indexed

DOI · 10.48550/arXiv.2605.13905

Abstract

Legacy SAS macro libraries resist AI integration because they emit opaque RTF with no machine-readable layer, yet source-level change triggers re-validation. This preprint presents a non-destructive framework — a bridge map, typed Intermediate Representation, and orchestrator that wrap the legacy library unchanged — applied to a 558-component industrial SAS library. Coexistence mode delivers AI-ready JSON output on day one; optional consolidation achieved a 92% SAS code reduction. Parity validation reached at least 80% cell-level parity on 11 of 14 reports from an internal Phase III study and 100% parity (4,764 cells) on the public CDISCPilot01 benchmark, and LLM experiments demonstrated IR-based table summarization, anomaly detection, and configuration generation.

Keywords

legacy modernization · clinical reporting · SAS · intermediate representation · pharmacoinformatics · parity validation · AI readiness

Cite

Jaime Yan. "A Non-Destructive Methodological Framework for Modernizing Legacy Clinical Reporting Systems for AI-Driven Pharmacoinformatics: A SAS Case Study." arXiv, 2026. doi:10.48550/arXiv.2605.13905.