Clinical data extraction errors in regulatory submissions represent a nationally significant challengein the United States, where the FDA evaluates thousands of drug and device applicationsannually—each underpinned by clinical datasets that must conform precisely to CDISC standards—and where extraction failures can delay patient access to therapies, trigger costly resubmissions, orallow safety signals to go undetected. Large Language Models (LLMs) are increasingly explored forautomating this process, yet their propensity to generate plausible but factually incorrect outputs—termed hallucinations—poses severe risks; recent evaluations report hallucination rates varying fromlow single digits under optimized conditions to over 50% under adversarial prompting, depending onsystem design and mitigation strategy (Ji et al., 2023; Huang et al., 2025; Gao et al., 2024). Existingresearch has addressed individual mitigation techniques in isolation, but the published literature lacksa systematic architectural analysis of how these complementary methods should be composed forclinical data extraction tasks governed by regulatory data standards. This work addresses that gap:the author independently conceived and designed a conceptual reference architecture that integrateshybrid Retrieval-Augmented Generation with sparse–dense retrieval (Lewis et al., 2020; Karpukhinet al., 2020), constrained decoding for schema-valid structured outputs (Willard and Louf, 2023),NLI-based evidence grounding with provenance tracking, and calibrated abstention under uncertainty(Kadavath et al., 2022) into a defense-in-depth pipeline for SDTM mapping, adverse event coding, andlaboratory value normalization. The author critically evaluates each component against alternatives,documenting design rationale and trade-offs informed by specialized expertise at the intersection ofclinical data standards, regulatory requirements, and NLP system architecture. This work furthercontributes a consolidated evaluation protocol encompassing offline retrieval metrics, generationfactuality assessment via FActScore decomposition (Min et al., 2023), domain-specific clinical metrics,and a human evaluation rubric—designed to avoid data leakage and support regulatory audit.The author identifies and categorizes key failure modes from the clinical LLM evaluation literature—including entity conflation, temporal reasoning errors, and ontology boundary violations—and mapspublished mitigation strategies to each. The framework is situated within the FDA’s Good MachineLearning Practice (GMLP) principles and emerging transparency requirements. LIMITATION Theframework has not been empirically validated; this work contributes a literature-derived referencearchitecture and evaluation strategy to be tested in future implementation studies. The methodologyand findings are applicable across healthcare systems, research institutions, and clinical researchorganizations nationwide, contributing to the broader national effort to establish trustworthy AI inregulated healthcare environments.This work is intended for research purposes only and is not designed, validated, or approved forclinical use, diagnosis, or treatment of any medical condition.Keywords: large language models, hallucination mitigation, clinical data extraction, retrieval-augmentedgeneration, structured outputs, SDTM, regulatory compliance
Danielmartin Arogyasami (Fri,) studied this question.