Jaime Yan's framework makes legacy SAS reports AI-ready without code changes

Drug development and pharmacovigilance are often bottlenecked by legacy clinical reporting systems built on SAS. These monolithic pipelines encode years of regulatory logic but produce opaque output that is incompatible with modern AI tools. Existing modernization approaches force a painful choice between full rewrites and incremental refactoring that preserves structural barriers. Jaime Yan's framework solves this by introducing a non-destructive metadata layer—comprising a bridge map, a typed Intermediate Representation (IR), and an orchestrator—that wraps existing components and re-exposes their outputs as structured data consumable by large language models (LLMs). The framework also supports optional incremental consolidation, replacing selected legacy components with metadata-configured core routines while the remainder operates unchanged.

Validated on a real-world SAS reporting library of 558 components and 373,000 lines of code, the framework demonstrated immediate AI-readiness under coexistence mode. In consolidation mode, the modernized core achieved a 92% reduction in proprietary SAS code. Parity validation on 14 report types from a Phase III study showed cell-level parity of 80% or above on 11 reports (mean 82.7%, best 99.2%). A benchmark using CDISC CDISCPilot01 data achieved 100% parity across 5 reports. LLM experiments confirmed that the IR enables automated pharmacovigilance, table summarization, and trial configuration generation. This regulation-aware path to AI-integrated clinical reporting can accelerate drug development without interrupting ongoing regulatory submissions.