Common data element (CDE) and AI-Enabled approaches for clinical data Management: Workshop proceedings from the NIH INCLUDE Project.

OBJECTIVE: To evaluate challenges in managing multisite, longitudinal, and multisystem clinical data for Down syndrome (DS) research and identify approaches to improve standardization and reuse. METHODS: This report summarizes the outcomes and lessons learned from a NIH workshop convened by the INCLUDE Project to align and modernize clinical data management (CDM) practices using common data elements (CDEs) and artificial intelligence (AI)-enabled tools. RESULTS: (1) consensus that shared CDEs, ontologies, and consistent data models are foundational for cross-study harmonization and interoperability; (2) identification of complementary electronic health record strategies, including standards-based exchange and research data models to support scalable extraction and analysis; (3) recognition that AI-enabled methods, including natural language processing with human review, can accelerate abstraction of information from clinical narratives and support data harmonization across heterogeneous sources; and (4) prioritization of governance needs for privacy protection, transparency, bias mitigation, and ongoing oversight when applying AI to sensitive health data. CONCLUSION: The major conclusion is that combining standardized CDE-driven design with appropriately governed AI-enabled workflows can reduce manual burden, improve data quality, and enable integrated multimodal research, with lessons that are not only applicable the INCLUDE Project but also to other complex clinical research programs.