aceOpportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems
Background: National electronic health record (EHR) networks can support learning health systems (LHSs) by enabling large-scale data aggregation, monitoring, and benchmarking, but their capacity to produce trustworthy and locally deployable machine learning and artificial intelligence (ML/AI) models remains uncertain. We characterized major US national EHR networks and examined barriers to ML/AI development and deployment across the LHS cycle. Methods: We conducted an environmental scan combining PubMed searches…
National EHR networks covering up to more than 200 million patients can support learning health systems by enabling large-scale aggregation and benchmarking for ML/AI development.
National EHR networks currently function primarily as research platforms, with few ML/AI models prospectively evaluated or integrated into workflows due to heterogeneous capture, privacy constraints, limited representativeness, and need for local recalibration.
Findings are bounded by US-only scope and scan through September 2025, with residual data quality and representativeness issues limiting generalizability and local deployment.
Evidence
- Peer-reviewedLearning Health Systems2026-05-01
How should this claim be treated?
Truvace Impact Record TRV-2026-0187, v1: “Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.” Truvace, 2026-07-13. /record/TRV-2026-0187 (accessed at citation time). sha256 9b669d55f3034887…
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