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TRUVACE RECORD VERSION
record: TRV-2026-0187
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-13T14:13:57.957451Z
status: published
lens: trace
sector: health
headline: Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems
dek: 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…
gain_title: 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.
problem_title: 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.
trace_subject: development and local deployment of trustworthy ML/AI models using US national EHR networks for learning health systems
gain_reading: 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.
gain_evidence: National electronic health record (EHR) networks can support learning health systems (LHSs) by enabling large-scale data aggregation, monitoring, and benchmarking | National EHR networks offer critical infrastructure for LHSs
problem_reading: 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.
problem_evidence: only a small subset was prospectively evaluated or integrated into clinical workflows | Common barriers included heterogeneous data capture, privacy and linkage constraints, residual variation despite harmonization, limited representativeness, need for local recalibration, and sociotechnical challenges | currently function primarily as research rather than ML/AI platforms
quick_read: Researchers conducted an environmental scan through September 2025 of 23 US national EHR networks that aggregate patient-level data, ranging from under 1 million to over 200 million patients, and reviewed 34 ML/AI studies built on them. Most networks used common data models, yet few models were prospectively evaluated or integrated into clinical workflows.

The findings matter because scale alone does not ensure deployable AI for learning health systems; heterogeneous capture, privacy and linkage limits, residual variation after harmonization, limited representativeness, and implementation hurdles remain. It remains uncertain how to enable reliable local recalibration and monitoring to move networks from research infrastructure to operational AI platforms.
limitation: Findings are bounded by US-only scope and scan through September 2025, with residual data quality and representativeness issues limiting generalizability and local deployment.
tag: Automated dual reading
key_points: Environmental scan through September 2025 identified 23 US national EHR networks spanning federal and academic consortia, vendor-led consortia, commercial aggregators, and practice-based research networks. | Networks ranged from fewer than 1 million to more than 200 million patients and most used common data models to standardize inputs. | Review found 34 ML/AI studies using these networks, but only a small subset was prospectively evaluated or integrated into clinical workflows.
rundown: The scan abstracted scale, settings, data domains, harmonization approaches, and access models for 23 networks identified via PubMed and governance documents through September 2025.

Barriers were mapped onto a seven-step LHS-AI cycle from data capture to implementation and monitoring, highlighting privacy and linkage constraints and sociotechnical challenges to evaluation.
sources:
- peer_reviewed | Learning Health Systems | https://doi.org/10.1002/lrh2.70090 | 2026-05-01
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