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

Joaquin M Espinosa, Kate Burdekin, Jeanette O Auman, Benjamin Tyndall, James F Cnota, Sigan L Hartley, et al.

Summary: This report summarizes a workshop organized by the NIH INCLUDE Project, which funds research into Down syndrome. Researchers from multiple institutions gathered to address the practical challenges of collecting and combining health data across many different study sites. The workshop reached four broad conclusions: using standardized definitions of data elements across sites is essential for combining data meaningfully; electronic health records can be used to feed research databases if extraction methods are carefully designed; AI tools, particularly those that read and interpret clinical text, can speed up the process of pulling information from records — but only if humans continue to review the results; and clear rules about privacy, fairness, and transparency must govern any AI use in this context.

Why it matters: The difficulties the INCLUDE Project encountered — inconsistent data definitions across sites, heavy manual workload, and the challenge of managing complex longitudinal data — are common to nearly every research network built around a rare or complex condition, including cystic fibrosis registries. The combination of standardized data definitions at the design stage with AI-assisted data extraction at the analysis stage, governed by clear oversight rules, represents a practical and increasingly mature approach to building better research infrastructure. The workshop's emphasis on governance — treating privacy, bias prevention, and transparency as operational requirements rather than aspirations — is particularly relevant as AI tools are adopted rapidly, and sometimes uncritically, in health data settings.

Expert perspective: This is a summary of workshop discussions, not a research study. There are no data, no measured outcomes, and no formal evaluation of whether the approaches described actually improve research quality. The recommendation that AI-assisted text analysis can accelerate data abstraction is consistent with broader evidence, but the specific performance characteristics, error rates, and costs in Down syndrome research or similar rare disease settings are not reported. The call for standardized data definitions is well-established in principle; the persistent challenge is getting institutions to adopt and maintain them over time, which workshop proceedings cannot guarantee. For those building or updating rare disease registries, the most practical takeaway is architectural: design data collection around shared standards from the beginning, use AI tools to assist rather than replace human review of clinical text, and build governance structures that are genuinely implemented rather than listed as good intentions. This report is a useful orientation, but it describes a direction of travel, not a proven destination.

Relevance: Patient Registries; Clinical Informatics; Data Standards; Data Governance; Real-World Data

Source: https://pubmed.ncbi.nlm.nih.gov/42586352/

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