Data quality, equity and clinician usability risks
Key Questions
What data quality issues affect AI readiness in EHR systems?
Between 40-60% noise or gaps in GP records create significant hidden costs and risks for EHR-based AI. Techniques such as RAG are recommended to reduce these issues.
How can benchmarks support evaluation of AI models for Irish EHR contexts?
The BRIDGE multilingual benchmark assesses LLMs on clinical text across nine languages and offers a tool for testing AI performance in Irish settings while addressing data quality and governance.
What solutions exist for structuring unstructured EHR data for AI use?
xCures has raised $46M to develop AI that converts unstructured EHR data into actionable intelligence from over 300 million records with source traceability, providing a relevant benchmark for improving AI readiness.
Missing GP information, unstructured records, weak testing environments and context-dependent model performance remain barriers to safe EHR-based AI and decision support. Implementation evidence continues to warn about portal access, migration gaps, workflow fit, frontline training, downtime planning and adoption measurement; automated referrals and EHR switching add further transition, productivity and accountability risks.