Health systems are wrestling with converting AI-generated clinical text into structured data that EHRs and analytics platforms can use. Large language models produce fluent summaries, notes and patient communications, but their free-text outputs often lack codified elements, consistent vocabularies and provenance, complicating billing, quality measurement and clinical decision support. Providers must contend with hallucinations, variable accuracy and workflow disruption while ensuring patient privacy and regulatory compliance.
Experts in the video argue that the path forward combines NLP extraction, FHIR-aligned mapping, rigorous validation and human-in-the-loop review to transform generated text into reliable data. Investments in standardized ontologies, annotation pipelines, monitoring and governance are essential to integrate text-to-data at scale. Vendors and health systems should pilot use cases with measurable outcomes—documentation burden reduction, coding accuracy and care coordination—before wider deployment.
